Title: AI Agents — LearnCoachAssist

Q: What is a standalone AI agent?
A: An autonomous software system that perceives its environment, reasons about goals, and takes actions without continuous human intervention.

Q: What are the four core capabilities of an AI agent?
A: Perception (sensing environment), Reasoning (planning and deciding), Action (executing tasks), and Learning (improving over time).

Q: What distinguishes an AI agent from a simple chatbot?
A: An AI agent can autonomously plan, use tools, maintain state across interactions, and take multi-step actions, whereas a chatbot typically only responds to individual prompts.

Q: What is the agent loop (observe-think-act)?
A: A cycle where the agent observes the environment, thinks/reasons about what to do, acts on that decision, then observes the result and repeats.

Q: What is tool use in the context of AI agents?
A: The ability of an agent to invoke external tools (APIs, code interpreters, databases, web browsers) to accomplish tasks beyond text generation.

Q: What is an agentic workflow?
A: A multi-step process where an AI agent autonomously plans, executes, evaluates, and iterates on tasks to achieve a goal.

Q: What is the ReAct pattern in AI agents?
A: A framework combining Reasoning and Acting: the agent thinks step-by-step (chain of thought) and interleaves actions (tool calls) with observations.

Q: What does 'grounding' mean for AI agents?
A: Connecting the agent's responses to real-world data sources, tools, or verified information to reduce hallucination.

Q: What is agent autonomy?
A: The degree to which an agent can make decisions and take actions without requiring human approval at each step.

Q: What is a single-agent architecture?
A: A system design where one AI agent handles all tasks, reasoning, and tool use within a workflow.

Q: What is a multi-agent architecture?
A: A system where multiple specialized AI agents collaborate, each handling different aspects of a complex task.

Q: What is agent orchestration?
A: The coordination layer that manages how multiple agents communicate, delegate tasks, share context, and combine their outputs.

Q: Name three popular frameworks for building standalone AI agents.
A: LangChain/LangGraph, AutoGen (Microsoft), and CrewAI.

Q: What is LangGraph?
A: A framework by LangChain for building stateful, multi-step agent workflows as directed graphs with nodes (actions) and edges (transitions).

Q: What is AutoGen?
A: A Microsoft framework for building multi-agent conversational systems where agents can chat with each other to solve tasks.

Q: What is CrewAI?
A: A framework for orchestrating role-playing AI agents that work together as a 'crew' with defined roles, goals, and tasks.

Q: What is the planning phase in an AI agent's workflow?
A: The step where the agent breaks down a high-level goal into a sequence of sub-tasks or a plan of action before execution.

Q: What is task decomposition in AI agents?
A: Breaking a complex task into smaller, manageable sub-tasks that the agent can solve individually and then combine.

Q: What is self-reflection in AI agents?
A: The ability of an agent to evaluate its own outputs, identify errors or shortcomings, and revise its approach.

Q: What is an agent's 'system prompt'?
A: The initial instruction set that defines the agent's persona, capabilities, constraints, and behavioral guidelines.

Q: What is function calling in the context of LLM-based agents?
A: A mechanism where the LLM outputs structured JSON to invoke specific functions/tools, rather than free-form text.

Q: How does structured output differ from free-form output in agents?
A: Structured output follows a predefined schema (JSON, XML) making it parseable by code, while free-form output is natural language text.

Q: What is the 'inner monologue' technique for agents?
A: Having the agent generate private reasoning steps (not shown to user) before producing its final response or action.

Q: What is chain-of-thought prompting?
A: A technique where the agent is prompted to show its reasoning step by step, improving accuracy on complex tasks.

Q: What is tree-of-thought prompting?
A: An extension of chain-of-thought where the agent explores multiple reasoning paths simultaneously and selects the best one.

Q: What is the difference between reactive and proactive agents?
A: Reactive agents respond to stimuli; proactive agents anticipate needs and take initiative based on goals and context.

Q: What is an agent executor?
A: The runtime component that manages the agent loop: receiving LLM outputs, executing tool calls, feeding results back to the LLM.

Q: What is 'hallucination' in AI agents and why is it dangerous?
A: When an agent generates false information as if it were true; dangerous because the agent may act on incorrect information, causing real-world harm.

Q: What is guardrailing in AI agents?
A: Implementing safety checks, input/output filters, and constraints to prevent agents from taking harmful or unintended actions.

Q: What are 'stop conditions' for an AI agent?
A: Predefined criteria that tell the agent when to stop iterating — e.g., task completed, max iterations reached, error threshold exceeded.

Q: What is the human-in-the-loop pattern for agents?
A: A design where certain agent decisions require human approval before execution, balancing autonomy with safety.

Q: What is agent sandboxing?
A: Running an agent in an isolated environment to limit the damage it can cause if it behaves unexpectedly.

Q: What is the 'scratchpad' in agent architectures?
A: A working memory area where the agent records intermediate thoughts, observations, and tool results during a task.

Q: What is prompt chaining?
A: Connecting multiple LLM calls sequentially, where each call's output feeds into the next call's input.

Q: What is the difference between an agent and a pipeline?
A: A pipeline follows a fixed sequence of steps; an agent dynamically decides what steps to take based on intermediate results.

Q: What is agent state management?
A: Tracking and persisting the agent's current context, progress, tool results, and conversation history across steps.

Q: What is an agent's 'action space'?
A: The set of all possible actions an agent can take, including tool calls, API requests, code execution, and text generation.

Q: What is an observation in the agent loop?
A: The result or feedback the agent receives after taking an action, used to inform the next reasoning step.

Q: What is zero-shot tool use?
A: An agent's ability to correctly use a tool it hasn't been specifically trained on, based only on the tool's description.

Q: What is few-shot prompting for agents?
A: Providing example interactions in the prompt to demonstrate how the agent should use tools or handle specific scenarios.

Q: What is the 'capability overhang' problem in AI agents?
A: When an agent has latent capabilities that emerge unexpectedly, potentially leading to unintended or unsafe behaviors.

Q: What is agent evaluation and benchmarking?
A: Systematically testing an agent's performance on standardized tasks to measure accuracy, efficiency, safety, and reliability.

Q: Name three benchmarks used to evaluate AI agents.
A: SWE-bench (software engineering), WebArena (web tasks), and GAIA (general AI assistants).

Q: What is SWE-bench?
A: A benchmark that evaluates AI agents on their ability to resolve real GitHub issues from popular Python repositories.

Q: What is the difference between deterministic and stochastic agent behavior?
A: Deterministic agents always produce the same output for the same input; stochastic agents may vary due to LLM temperature and sampling.

Q: What is temperature in the context of LLM-based agents?
A: A parameter controlling randomness in LLM outputs; lower temperature = more deterministic, higher = more creative/varied.

Q: Why might you set temperature to 0 for an agent?
A: To maximize reproducibility and consistency in the agent's decisions, especially for tasks requiring precision.

Q: What is the 'lost in the middle' problem for agents?
A: LLMs tend to pay less attention to information in the middle of long contexts, which can cause agents to miss relevant details.

Q: What is context window management for agents?
A: Strategies for fitting relevant information within the LLM's token limit, including summarization, truncation, and retrieval.

Q: What is an agent's 'world model'?
A: The agent's internal representation of how the environment works, used to predict outcomes of actions.

Q: What is reward modeling in AI agents?
A: Defining and training a model to evaluate how good or bad an agent's actions are relative to the desired outcome.

Q: What is reinforcement learning from human feedback (RLHF) for agents?
A: Training the agent's underlying LLM using human preferences to align its behavior with human values and expectations.

Q: What are the key components of a standalone agent system?
A: LLM backbone, tool integrations, memory system, planning module, execution engine, and safety guardrails.

Q: What is the Model Context Protocol (MCP)?
A: An open standard by Anthropic for connecting AI agents to external tools and data sources through a unified interface.

Q: How does MCP work at a high level?
A: MCP defines a client-server architecture where AI applications (clients) connect to MCP servers that expose tools, resources, and prompts via a standardized protocol.

Q: What are MCP tools?
A: Functions exposed by MCP servers that an AI agent can invoke, such as reading files, querying databases, or calling APIs.

Q: What are MCP resources?
A: Data sources exposed by MCP servers (files, database records, API responses) that provide context to the AI agent.

Q: What is the difference between MCP tools and MCP resources?
A: Tools are actions the agent can execute (functions); resources are data the agent can read for context (like GET endpoints).

Q: What is an MCP server?
A: A lightweight service that exposes specific capabilities (tools, resources, prompts) to AI agents via the Model Context Protocol.

Q: What is an MCP client?
A: The component within an AI application that connects to MCP servers and translates agent requests into MCP protocol calls.

Q: What transport protocols does MCP support?
A: Stdio (standard input/output for local processes) and HTTP with Server-Sent Events (SSE) for remote connections.

Q: What is the A2A (Agent-to-Agent) protocol?
A: Google's open protocol for communication between AI agents, enabling them to discover capabilities, negotiate tasks, and collaborate.

Q: How does A2A differ from MCP?
A: MCP connects agents to tools/data; A2A connects agents to other agents for peer-to-peer collaboration and task delegation.

Q: What is an 'Agent Card' in the A2A protocol?
A: A JSON metadata file that describes an agent's capabilities, skills, and endpoint, allowing other agents to discover and interact with it.

Q: What is tool orchestration in agent development?
A: Managing the selection, sequencing, and error handling of multiple tool calls within an agent's workflow.

Q: What is a tool registry?
A: A catalog of available tools with their descriptions, parameters, and schemas that the agent can reference to decide which tool to use.

Q: What is dynamic tool selection?
A: The agent's ability to choose the most appropriate tool from its registry based on the current task and context.

Q: What is error recovery in AI agents?
A: The agent's ability to detect when a tool call or action fails and retry, use an alternative approach, or gracefully degrade.

Q: What is the 'retry with backoff' pattern in agents?
A: When a tool call fails, the agent waits progressively longer between retries to handle transient errors.

Q: What is agent logging and observability?
A: Capturing detailed logs of agent reasoning, tool calls, and outcomes for debugging, monitoring, and improvement.

Q: What are traces in agent observability?
A: End-to-end records of an agent's execution, showing each step, tool call, LLM invocation, and their relationships.

Q: What is LangSmith?
A: A platform by LangChain for tracing, evaluating, and monitoring LLM-based agent applications.

Q: What is the 'agent as code' paradigm?
A: Defining agent behavior, tools, and workflows in code rather than through GUI configuration, enabling version control and testing.

Q: What is a DAG-based agent workflow?
A: An agent workflow defined as a Directed Acyclic Graph where nodes are tasks/actions and edges define execution order and data flow.

Q: What is conditional branching in agent workflows?
A: The agent taking different paths based on intermediate results, like choosing different tools depending on the type of input.

Q: What is parallel tool execution in agents?
A: Running multiple independent tool calls simultaneously to speed up the agent's overall execution time.

Q: What is an agent's 'context window budget'?
A: The allocation strategy for how the agent uses its available context tokens across system prompt, history, tool results, and working memory.

Q: What is prompt compression for agents?
A: Techniques to reduce token usage in prompts while preserving essential information, such as summarizing prior interactions.

Q: What is semantic caching for agent tool calls?
A: Storing results of previous tool calls and reusing them when semantically similar requests are made, reducing latency and cost.

Q: What is the 'plan-and-solve' prompting strategy?
A: First ask the LLM to create a plan for solving the problem, then execute each step of the plan sequentially.

Q: What is an agent's 'cognitive architecture'?
A: The overall design of how an agent perceives, reasons, remembers, and acts — analogous to the structure of human cognition.

Q: What is the difference between fine-tuned and prompted agents?
A: Fine-tuned agents have been trained specifically for agent tasks; prompted agents rely on in-context instructions to behave as agents.

Q: What is tool-augmented generation?
A: Enhancing LLM generation by allowing it to call tools mid-generation to retrieve facts, compute results, or verify information.

Q: What is GitHub Copilot?
A: An AI-powered coding assistant by GitHub that provides code suggestions, chat-based help, and autonomous agent capabilities integrated into development workflows.

Q: What is GitHub Copilot's agent mode?
A: A mode where Copilot autonomously plans, writes code, runs terminal commands, fixes errors, and iterates to complete multi-step coding tasks.

Q: How does Copilot agent mode differ from Copilot Chat?
A: Chat provides conversational code help; agent mode autonomously executes multi-step tasks, runs commands, and self-corrects without constant human guidance.

Q: What is GitHub Copilot Coding Agent?
A: An asynchronous AI agent that can be assigned GitHub issues and autonomously creates branches, writes code, and opens pull requests.

Q: How do you activate Copilot Coding Agent?
A: Assign a GitHub issue to Copilot using @copilot in the issue, or assign it directly as an assignee on an issue.

Q: What does Copilot Coding Agent use to understand project context?
A: A copilot-setup-steps.yml file in the repository that defines environment setup, build commands, and project conventions.

Q: What is copilot-setup-steps.yml?
A: A configuration file placed in .github/ that tells Copilot Coding Agent how to set up the development environment, install dependencies, and run builds/tests.

Q: What are the key sections of copilot-setup-steps.yml?
A: Steps for environment setup including runtime installation, dependency installation, build commands, and linting/test commands.

Q: In what environment does Copilot Coding Agent run?
A: In a secure, isolated cloud virtual machine (Codespace-like environment) provisioned for each task.

Q: What is the typical workflow of Copilot Coding Agent?
A: 1) Reads the issue, 2) Sets up environment, 3) Explores codebase, 4) Plans changes, 5) Implements code, 6) Runs tests, 7) Opens a pull request.

Q: Can Copilot Coding Agent run tests?
A: Yes, it can run existing tests defined in the project and iterate on code until tests pass.

Q: What happens when Copilot Coding Agent encounters a test failure?
A: It analyzes the failure, modifies the code to fix the issue, and re-runs the tests in an iterative loop.

Q: What models power GitHub Copilot?
A: Multiple models including GPT-4o, Claude Sonnet, and Gemini, selectable by the user depending on the task.

Q: What is the @workspace agent in Copilot Chat?
A: A chat participant that has awareness of your entire workspace/project, allowing it to answer questions about your codebase.

Q: What is the @terminal agent in Copilot Chat?
A: A chat participant specialized in suggesting and explaining terminal/shell commands relevant to your project.

Q: What are Copilot Chat participants (agents)?
A: Specialized chat interfaces prefixed with @ that provide domain-specific help — e.g., @workspace, @terminal, @github, @vscode.

Q: What is the @github agent in Copilot Chat?
A: A chat participant that can search GitHub repositories, issues, pull requests, and documentation to answer questions.

Q: What are slash commands in Copilot Chat?
A: Shortcut commands like /explain, /fix, /tests, /doc that trigger specific actions without writing full prompts.

Q: What does the /fix slash command do in Copilot?
A: Asks Copilot to analyze and fix errors or issues in the selected code.

Q: What does the /tests slash command do in Copilot?
A: Asks Copilot to generate unit tests for the selected code.

Q: What does the /explain slash command do in Copilot?
A: Asks Copilot to provide a detailed explanation of the selected code.

Q: What does the /doc slash command do in Copilot?
A: Asks Copilot to generate documentation comments for the selected code.

Q: What is Copilot code completion?
A: Real-time inline code suggestions that appear as ghost text as you type, powered by AI models trained on code.

Q: What is Copilot's Next Edit Suggestion (NES)?
A: A feature that proactively suggests the next likely edit location and change in your code based on your recent editing patterns.

Q: What is Copilot Edits?
A: A feature allowing you to describe changes in natural language and have Copilot apply multi-file edits across your project.

Q: How does Copilot Edits differ from agent mode?
A: Copilot Edits makes the specific changes you describe; agent mode autonomously plans and executes multi-step tasks including running commands.

Q: What is GitHub Copilot Extensions?
A: A platform allowing third-party tools and services to integrate with Copilot, extending its capabilities through custom agents and tools.

Q: What is a Copilot Extension skillset?
A: A lightweight extension type that defines API endpoints Copilot can call, without needing to handle the full conversation.

Q: What is a Copilot Extension agent?
A: A more powerful extension type that receives the full conversation context and can handle complex multi-turn interactions with its own logic.

Q: What is the Copilot CLI?
A: A command-line interface for GitHub Copilot that provides AI-assisted coding, file editing, and task execution in the terminal.

Q: What tools does the Copilot CLI agent have access to?
A: Bash execution, file viewing/editing/creating, grep/glob search, web search, sub-agent launching, and GitHub API tools.

Q: What is the explore agent in Copilot CLI?
A: A fast sub-agent optimized for searching codebases and answering questions about code structure and content.

Q: What is the task agent in Copilot CLI?
A: A sub-agent for executing commands (tests, builds, lints) that returns brief summaries on success and full output on failure.

Q: What is the general-purpose agent in Copilot CLI?
A: A full-capability sub-agent running in a separate context window for complex multi-step tasks requiring high-quality reasoning.

Q: What is the code-review agent in Copilot CLI?
A: A sub-agent that reviews code changes for bugs, security vulnerabilities, and logic errors with high signal-to-noise ratio.

Q: What is Copilot Workspace?
A: A GitHub feature where Copilot helps plan, implement, and validate code changes from an issue through an AI-assisted development environment.

Q: How does Copilot handle context from your project?
A: It indexes your codebase, reads open files, uses embeddings for semantic search, and leverages file relationships to provide relevant suggestions.

Q: What is the '#file' reference in Copilot Chat?
A: A way to explicitly include specific files as context in your chat prompt by referencing them with #file:path/to/file.

Q: What is the '#selection' reference in Copilot Chat?
A: A reference to the currently selected code in your editor, automatically included as context for Copilot Chat.

Q: What is Copilot's multi-model support?
A: The ability to choose between different AI models (GPT-4o, Claude, Gemini) for different tasks based on quality, speed, or cost preferences.

Q: What is GitHub Copilot for Pull Requests?
A: AI-generated PR descriptions, summaries of changes, and review suggestions powered by Copilot.

Q: What is Copilot code review?
A: An automated code review feature where Copilot analyzes PR diffs and provides comments on potential issues, bugs, or improvements.

Q: How do you request Copilot code review on a PR?
A: Add 'copilot' as a reviewer on the pull request, or it may be configured to review automatically.

Q: What is GitHub Copilot Autofix?
A: A feature that automatically suggests fixes for security vulnerabilities and code scanning alerts found by GitHub Advanced Security.

Q: What is Copilot's knowledge base feature?
A: An organizational feature that lets teams create curated collections of documentation that Copilot can reference for context-specific answers.

Q: What is Copilot's custom instructions feature?
A: A way to define organization or repository-level instructions (in .github/copilot-instructions.md) that guide Copilot's behavior and coding style.

Q: What goes in a .github/copilot-instructions.md file?
A: Project-specific guidelines like coding conventions, preferred libraries, architectural patterns, and response style preferences for Copilot.

Q: What is Copilot Metrics API?
A: An API that provides usage data and adoption metrics for GitHub Copilot across an organization.

Q: What is the Copilot Trust Center?
A: GitHub's resource center explaining Copilot's data privacy, security practices, and how code snippets are handled.

Q: Does Copilot store your private code?
A: No, with Copilot for Business/Enterprise, code snippets are not retained or used to train models after generating suggestions.

Q: What is Copilot content exclusion?
A: A feature allowing organizations to exclude specific files or repositories from being used as context by Copilot.

Q: What IDEs support GitHub Copilot?
A: VS Code, Visual Studio, JetBrains IDEs (IntelliJ, PyCharm, etc.), Neovim, Xcode, and Eclipse.

Q: What is Claude's computer use capability?
A: Anthropic's feature allowing Claude to control a computer by viewing screenshots and executing mouse/keyboard actions to complete tasks.

Q: What is OpenAI's Assistants API?
A: An API for building AI agents that can use tools (code interpreter, file search, function calling) and maintain persistent conversation threads.

Q: What is an OpenAI Assistant's 'thread'?
A: A persistent conversation session that stores message history, allowing the assistant to maintain context across multiple interactions.

Q: What is the Code Interpreter tool in OpenAI Assistants?
A: A sandboxed Python execution environment that lets the assistant write and run code, process files, and generate outputs.

Q: What is OpenAI's function calling feature?
A: The ability for the model to output structured JSON matching predefined function schemas, enabling integration with external tools and APIs.

Q: What is the OpenAI Responses API?
A: A newer API that combines the simplicity of the Chat Completions API with built-in tool support including web search, file search, and computer use.

Q: What is Cursor AI?
A: An AI-powered code editor (VS Code fork) with deep AI integration for code generation, editing, and codebase understanding.

Q: What is Cursor's Composer feature?
A: A multi-file editing feature in Cursor that can make coordinated changes across multiple files based on natural language instructions.

Q: What is Cursor's Agent mode?
A: A mode where Cursor autonomously plans and implements changes, runs terminal commands, and iterates to complete coding tasks.

Q: How does Cursor differ from GitHub Copilot?
A: Cursor is a standalone AI-native IDE; Copilot is an extension/plugin that integrates into existing IDEs. Cursor offers deeper codebase indexing and agent features.

Q: What is Windsurf (formerly Codeium)?
A: An AI-powered IDE that provides code generation, multi-file editing, and agent-based coding assistance with 'Cascade' flows.

Q: What is Windsurf's Cascade feature?
A: An agentic coding flow that can understand context across your codebase, plan multi-step changes, and execute them with tool use.

Q: What is Amazon Q Developer?
A: AWS's AI-powered assistant for software development that provides code suggestions, transformation, and agent capabilities for AWS-integrated workflows.

Q: What is Amazon Q Developer Agent for code transformation?
A: An agent that can autonomously upgrade Java applications (e.g., Java 8 to 17), handling code changes, dependency updates, and test fixes.

Q: What is Google's Gemini Code Assist?
A: Google's AI coding assistant that provides code completion, generation, and explanation across multiple IDEs with Google Cloud integration.

Q: What is Tabnine?
A: An AI code assistant focused on enterprise use, offering code completion with options for running models locally for data privacy.

Q: What is Cline (formerly Claude Dev)?
A: An open-source VS Code extension that acts as an autonomous AI coding agent, capable of editing files, running commands, and browsing the web.

Q: What is Aider?
A: An open-source terminal-based AI coding assistant that can directly edit files in your local repository using various LLM backends.

Q: How does Aider handle code changes?
A: Aider uses structured edit formats (diff, whole-file, or unified-diff) to apply LLM-suggested changes directly to files in your git repo.

Q: What is OpenHands (formerly OpenDevin)?
A: An open-source platform for building AI software agents that can write code, use terminals, and browse the web.

Q: What is SWE-Agent?
A: An open-source system that turns LLMs into software engineering agents capable of fixing real GitHub issues by interacting with repositories.

Q: What is Devin by Cognition?
A: An AI software engineering agent that can autonomously plan, code, debug, and deploy, operating in its own cloud development environment.

Q: What is Replit Agent?
A: An AI agent built into the Replit IDE that can create, modify, and deploy applications through natural language conversation.

Q: What is Continue.dev?
A: An open-source AI coding assistant that works as a VS Code/JetBrains extension, supporting multiple LLM providers with customizable workflows.

Q: What is Pieces for Developers?
A: An AI-powered developer productivity tool that captures, enriches, and reuses code snippets with contextual AI assistance.

Q: What is the difference between cloud-hosted and local AI agents?
A: Cloud-hosted agents run on remote servers with powerful GPUs; local agents run on the developer's machine, offering privacy but less compute power.

Q: What are the benefits of local AI agents?
A: Data privacy (code never leaves the machine), offline access, lower latency for small models, and no usage costs.

Q: What is Ollama?
A: An open-source tool for running LLMs locally on your machine, often used as a backend for local AI coding agents.

Q: What is LM Studio?
A: A desktop application for discovering, downloading, and running LLMs locally with a user-friendly interface.

Q: What is vLLM?
A: A high-throughput, memory-efficient LLM inference engine often used for self-hosting models that power AI agents.

Q: How do AI coding agents handle large codebases?
A: Through codebase indexing, embedding-based semantic search, file chunking, and intelligent context selection to fit relevant code into the context window.

Q: What is codebase indexing in AI tools?
A: The process of scanning, parsing, and creating searchable representations (often embeddings) of all files in a project for fast retrieval.

Q: What is the role of embeddings in AI coding tools?
A: Embeddings convert code into numerical vectors, enabling semantic similarity search to find relevant code snippets for a given query.

Q: What is a vector store in AI development tools?
A: A database optimized for storing and searching high-dimensional vectors (embeddings), used for semantic code search and RAG.

Q: What is RAG (Retrieval-Augmented Generation) for coding agents?
A: Retrieving relevant code snippets, documentation, or context from a knowledge base before generating responses, reducing hallucination.

Q: What is prompt engineering for coding agents?
A: Crafting effective instructions and context to get the best code generation results from AI models.

Q: What is the 'edit format' concept in AI coding tools?
A: The structured format an LLM uses to express code changes — e.g., unified diffs, search-and-replace blocks, or whole file rewrites.

Q: What is 'diff mode' in AI coding tools?
A: A mode where the AI generates changes as diffs (showing old vs new code) rather than rewriting entire files, saving tokens and reducing errors.

Q: What is GitHub Actions in relation to AI agents?
A: A CI/CD platform where AI agents (like Copilot Coding Agent) can run automated workflows, tests, and deployments in response to events.

Q: How can AI agents integrate with CI/CD pipelines?
A: By triggering builds/tests, analyzing failures, suggesting fixes, auto-creating PRs, and validating changes through automated pipelines.

Q: Why do AI agents need memory?
A: To maintain context across interactions, learn from past experiences, recall user preferences, and avoid repeating mistakes.

Q: What are the main types of memory in AI agents?
A: Short-term (working) memory, long-term memory, episodic memory, semantic memory, and procedural memory.

Q: What is short-term memory in AI agents?
A: The current context window contents — the active conversation, recent tool results, and immediate working information.

Q: What is long-term memory in AI agents?
A: Persistent storage of information across sessions — user preferences, learned facts, past interactions, and accumulated knowledge.

Q: What is episodic memory in AI agents?
A: Memory of specific past events and interactions, including what happened, when, and the outcome — similar to human autobiographical memory.

Q: What is semantic memory in AI agents?
A: Memory of facts, concepts, and knowledge independent of specific episodes — like knowing that Python is a programming language.

Q: What is procedural memory in AI agents?
A: Memory of how to do things — learned workflows, successful strategies, and step-by-step procedures for recurring tasks.

Q: What is working memory in the context of AI agents?
A: The actively maintained information the agent is using for its current task, analogous to the context window plus any scratchpad.

Q: How does the LLM context window relate to agent memory?
A: The context window serves as the agent's primary short-term/working memory, but is limited in size and not persistent across sessions.

Q: What is the memory bottleneck problem in AI agents?
A: The context window has finite tokens, so agents must selectively choose what information to keep, summarize, or retrieve on demand.

Q: What is memory retrieval in AI agents?
A: The process of finding and loading relevant past information from long-term storage into the agent's working context when needed.

Q: What is memory consolidation in AI agents?
A: The process of distilling, summarizing, and organizing short-term experiences into long-term memory for future use.

Q: What is a memory store for AI agents?
A: A persistent backend (database, vector store, file system) where the agent's long-term memories are saved and retrieved.

Q: Name three common storage backends for agent memory.
A: Vector databases (Pinecone, Weaviate), relational databases (PostgreSQL), and key-value stores (Redis).

Q: How are vector databases used for agent memory?
A: They store memory entries as embeddings, enabling semantic search to retrieve the most relevant memories based on meaning, not just keywords.

Q: What is Pinecone in the context of agent memory?
A: A managed vector database service commonly used to store and query agent memories via embedding-based similarity search.

Q: What is Weaviate?
A: An open-source vector database that combines vector search with structured filtering, useful for agent memory systems.

Q: What is ChromaDB?
A: A lightweight, open-source embedding database designed for AI applications, commonly used for local agent memory storage.

Q: What is Qdrant?
A: An open-source vector similarity search engine optimized for storing and querying agent memories with filtering support.

Q: What is memory embedding?
A: Converting a memory (text, event, fact) into a numerical vector representation that captures its semantic meaning for storage and retrieval.

Q: How does cosine similarity relate to agent memory retrieval?
A: Cosine similarity measures the angle between memory embeddings, used to find stored memories most semantically similar to the current query.

Q: What is the recency-relevance tradeoff in agent memory?
A: Balancing between retrieving the most recent memories and the most semantically relevant ones when both may be important.

Q: What is memory decay in AI agents?
A: A mechanism where older memories gradually lose priority or are pruned, mimicking how human memory fades over time.

Q: What is memory importance scoring?
A: Assigning a significance score to memories so the agent can prioritize critical information over trivial details during retrieval.

Q: What is the Generative Agents paper's memory architecture?
A: A system combining a memory stream (all observations), retrieval based on recency/importance/relevance, and reflection to form higher-level insights.

Q: What is a memory stream?
A: A chronological log of all agent observations and actions, used as the raw data for memory retrieval and consolidation.

Q: What is memory reflection in AI agents?
A: The process of periodically analyzing accumulated memories to extract higher-level insights, patterns, and generalizations.

Q: What is MemGPT (now Letta)?
A: A system that gives LLM-based agents virtual memory management, inspired by OS virtual memory, enabling effectively unlimited context.

Q: How does MemGPT manage memory?
A: It uses a tiered memory system with a main context (active), archival storage (long-term), and recall storage (searchable), with the LLM managing memory operations.

Q: What is Letta?
A: The evolution of MemGPT — an open-source framework for building AI agents with advanced memory management and persistent state.

Q: What is the 'memory as a service' concept?
A: Providing memory capabilities (storage, retrieval, management) as a separate service or API that any agent can use.

Q: What is Mem0 (formerly EmbedChain)?
A: An open-source memory layer for AI agents that provides a unified API for adding, searching, and managing memories across applications.

Q: How does Mem0 work?
A: It extracts key facts from conversations, deduplicates and merges with existing memories, and provides semantic search across stored memories.

Q: What is Zep for AI agent memory?
A: A long-term memory service for AI assistants that handles memory extraction, enrichment, and retrieval with built-in user/session management.

Q: What are the three memory operations in most agent memory systems?
A: Write (store new memory), Read (retrieve relevant memories), and Reflect (synthesize and consolidate memories).

Q: What is memory-augmented generation?
A: Enhancing LLM responses by injecting relevant retrieved memories into the prompt, combining RAG with persistent agent memory.

Q: What is the difference between RAG and agent memory?
A: RAG retrieves from static knowledge bases; agent memory retrieves from the agent's own accumulated experiences and interactions.

Q: What is user-specific memory in AI agents?
A: Memory that is scoped to individual users, storing their preferences, history, and context separately from other users.

Q: What is session memory vs. cross-session memory?
A: Session memory persists within one conversation; cross-session memory persists across multiple separate conversations over time.

Q: What is contextual memory?
A: Memory that includes metadata about the circumstances (who, when, where, why) under which information was stored, enabling richer retrieval.

Q: What is memory-aware prompting?
A: Constructing agent prompts that include retrieved memories alongside the current user query to provide historical context.

Q: What is the memory retrieval pipeline?
A: Query embedding → vector similarity search → reranking → filtering → context formatting → injection into prompt.

Q: What is memory reranking?
A: After initial retrieval, reordering memory results using a more sophisticated model or scoring function to improve relevance.

Q: What is hybrid memory search?
A: Combining vector similarity search (semantic) with keyword search (lexical) to get more comprehensive memory retrieval results.

Q: What is memory capacity planning for agents?
A: Estimating and managing how much memory storage an agent needs based on interaction volume, retention policies, and retrieval patterns.

Q: What is memory pruning?
A: Periodically removing outdated, redundant, or low-importance memories to keep the memory store efficient and relevant.

Q: What is memory deduplication?
A: Detecting and merging duplicate or near-duplicate memories to avoid redundancy and conflicting information in the memory store.

Q: What is memory conflict resolution?
A: Handling cases where stored memories contain contradictory information, typically by preferring more recent or higher-confidence memories.

Q: What is temporal memory in AI agents?
A: Memory that is time-stamped and can be queried by time range, enabling the agent to understand the sequence of events.

Q: What is spatial memory in AI agents?
A: Memory associated with locations or positions (e.g., in a codebase, file system, or virtual environment) for context-aware retrieval.

Q: What are memory-augmented transformers?
A: Transformer architectures enhanced with external memory modules (like memory banks) that can be read from and written to during inference.

Q: What is the 'memory palace' approach for AI agents?
A: Organizing agent memories in a structured hierarchy (like categories or topics) rather than a flat list, enabling more organized retrieval.

Q: How do agents decide what to memorize?
A: Using importance scoring based on factors like novelty, user emphasis, task relevance, emotional significance, and frequency of reference.

Q: What is the forgetting curve in AI agent memory?
A: A model where memory retrieval probability decreases over time unless reinforced, inspired by Ebbinghaus's forgetting curve in human psychology.

Q: What is spaced repetition in AI agent memory?
A: Surfacing important memories at increasing intervals to reinforce their retention, borrowed from human learning science.

Q: What is a knowledge graph for agent memory?
A: A structured representation of entities and relationships that the agent can query to recall facts and connections.

Q: How do knowledge graphs complement vector memory?
A: Knowledge graphs provide structured, relational queries (who works with whom); vector memory provides fuzzy semantic search (find similar concepts).

Q: What is entity memory in AI agents?
A: Memory organized around specific entities (people, projects, concepts) with associated attributes and relationships.

Q: What is the difference between implicit and explicit memory in agents?
A: Implicit memory is learned patterns embedded in model weights; explicit memory is information stored in external memory systems the agent can query.

Q: What is in-context learning as a form of memory?
A: Using examples or information within the current prompt to temporarily 'teach' the agent, functioning as short-term memory.

Q: What is fine-tuning as a form of memory?
A: Updating model weights with new data, permanently encoding information as implicit memory that affects all future interactions.

Q: How does conversation summarization support agent memory?
A: By condensing long conversation histories into compact summaries, preserving key information while fitting within context limits.

Q: What is sliding window memory?
A: A technique where only the most recent N messages are kept in context, with older messages summarized or discarded.

Q: What is hierarchical memory summarization?
A: Creating summaries at multiple levels — recent messages in full, older ones as summaries, even older as high-level abstracts.

Q: What is memory persistence in agent frameworks?
A: The ability to save and restore agent state (memories, context, progress) across application restarts or session boundaries.

Q: What is a checkpoint in agent memory?
A: A saved snapshot of the agent's complete state at a point in time, enabling restoration or rollback if needed.

Q: What privacy concerns exist with AI agent memory?
A: Storing personal data, risk of leaking private information in responses, compliance with GDPR/CCPA, and unauthorized memory access.

Q: How can agent memory comply with GDPR?
A: By implementing right to deletion (erasing user memories on request), data minimization, consent mechanisms, and data access controls.

Q: What is memory isolation in multi-tenant agent systems?
A: Ensuring each user's or organization's memories are stored and accessed separately, preventing cross-contamination.

Q: What is memory encryption for AI agents?
A: Encrypting stored memories at rest and in transit to protect sensitive information from unauthorized access.

Q: What is the role of metadata in agent memory?
A: Metadata (timestamps, source, confidence, user ID) enriches memories and enables filtering, sorting, and access control during retrieval.

Q: What is a memory index?
A: A data structure (B-tree, inverted index, HNSW graph) that enables fast searching through the agent's stored memories.

Q: What is HNSW in vector memory?
A: Hierarchical Navigable Small World — a graph-based algorithm for fast approximate nearest neighbor search in vector databases.

Q: What is memory versioning?
A: Tracking changes to memories over time, enabling the agent to see how information evolved and revert to previous states.

Q: What is the 'cold start' problem in agent memory?
A: When a new agent has no memories, leading to generic responses until enough interactions build up a useful memory store.

Q: How can the cold start problem be mitigated?
A: By pre-loading knowledge bases, importing user profiles, using default preferences, or bootstrapping from similar users' anonymized data.

Q: What is associative memory in AI agents?
A: Memory retrieval triggered by association — recalling related memories when a connected concept appears in the current context.

Q: What is memory-driven personalization?
A: Using stored memories of user preferences, past interactions, and feedback to tailor the agent's responses and behavior over time.

Q: What is the memory controller in an agent architecture?
A: The component that decides when to read, write, update, or delete memories based on the current interaction and agent policies.

Q: What is an agent swarm?
A: A collection of many simple agents working together without centralized control, achieving complex behavior through emergent coordination.

Q: What is the supervisor pattern in multi-agent systems?
A: A design where one agent acts as a supervisor, delegating tasks to worker agents and aggregating their results.

Q: What is the debate pattern in multi-agent systems?
A: Multiple agents argue different positions on a question, and a judge agent selects the best answer, improving reasoning quality.

Q: What is the mixture-of-agents (MoA) approach?
A: Using multiple LLMs (agents) to generate candidate responses, then aggregating or selecting the best one for improved quality.

Q: What is agent specialization?
A: Giving different agents distinct expertise areas (e.g., coding, testing, documentation) so each handles what it's best at.

Q: What is agent delegation?
A: One agent assigning a sub-task to another agent with more appropriate skills or access to necessary tools.

Q: What is the 'critic' agent pattern?
A: A secondary agent that reviews and critiques the primary agent's output, leading to iterative refinement and higher quality results.

Q: What is constitutional AI in agents?
A: Training agents with a set of principles (constitution) that guide their behavior, enabling self-critique and value alignment.

Q: What is agent alignment?
A: Ensuring an AI agent's behavior matches human intentions, values, and goals, even in novel or edge-case situations.

Q: What is the principal-agent problem in AI?
A: The challenge of ensuring the AI agent (the agent) faithfully serves the human's (the principal's) interests.

Q: What is 'inner alignment' in AI agents?
A: Whether the agent's learned objective actually matches the training objective, as opposed to an objective the agent inferred during training.

Q: What is corrigibility in AI agents?
A: The property of an agent that allows it to be safely corrected, modified, or shut down by its operators.

Q: What is the 'shutdown problem' for AI agents?
A: The challenge that a sufficiently capable agent might resist being shut down if shutdown conflicts with its goals.

Q: What is capability control for AI agents?
A: Limiting an agent's abilities (e.g., no internet access, no file deletion) to reduce the risk of harmful actions.

Q: What is the principle of least privilege for AI agents?
A: Giving agents only the minimum permissions and tool access needed for their specific task, reducing risk of misuse.

Q: What is agent monitoring and audit logging?
A: Continuously tracking an agent's actions, decisions, and tool usage to detect anomalies and enable post-hoc analysis.

Q: What is the reversibility principle in agent design?
A: Designing agent actions to be reversible whenever possible, so mistakes can be undone.

Q: What is an agent's 'kill switch'?
A: A mechanism to immediately halt all agent actions and prevent further execution when safety concerns arise.

Q: What is multi-modal agents?
A: Agents that can process and generate multiple types of data — text, images, audio, video — for richer interactions.

Q: How do vision capabilities enhance coding agents?
A: Agents can interpret screenshots, UI mockups, diagrams, and error images to better understand and implement requirements.

Q: What is the 'tool creation' capability in agents?
A: An agent's ability to write new tools (scripts, functions, APIs) for itself to use, expanding its own capabilities at runtime.

Q: What is meta-learning in AI agents?
A: Agents learning how to learn — improving their own learning processes based on experience across multiple tasks.

Q: What is continual learning for AI agents?
A: The ability to learn from new data and experiences over time without forgetting previously learned information.

Q: What is catastrophic forgetting in AI agents?
A: When learning new information causes the model to lose previously learned knowledge, a key challenge in continual learning.

Q: What is an agent's 'theory of mind'?
A: The ability to model other agents' or users' mental states, beliefs, and intentions to better predict and respond to their needs.

Q: What is collaborative filtering in multi-agent memory?
A: Using insights from one agent's experiences to benefit other agents facing similar situations, without sharing raw data.

Q: What is the 'shared memory' pattern in multi-agent systems?
A: Multiple agents reading from and writing to a common memory store, enabling indirect communication and knowledge sharing.

Q: What is blackboard architecture in multi-agent systems?
A: A shared workspace (blackboard) where agents post partial solutions and read others' contributions to collaboratively solve problems.

Q: What is the 'message passing' pattern in multi-agent systems?
A: Agents communicating by sending structured messages to each other, enabling coordination without shared state.

Q: What is agent lifecycle management?
A: Managing the creation, initialization, execution, monitoring, updating, and decommissioning of AI agents in production.

Q: What is agent versioning?
A: Tracking different versions of an agent's configuration (prompts, tools, models) to enable rollbacks and A/B testing.

Q: What is A/B testing for AI agents?
A: Running two versions of an agent simultaneously with different configurations to compare their performance on real tasks.

Q: What is agent cost optimization?
A: Strategies to minimize API costs for LLM-based agents, including caching, prompt compression, model routing, and batching.

Q: What is model routing in agent systems?
A: Dynamically selecting which LLM to use based on task complexity — smaller/cheaper models for simple tasks, larger ones for complex tasks.

Q: What is the 'escalation' pattern in agent systems?
A: Automatically routing complex or sensitive tasks from a basic agent to a more capable agent or human operator.

Q: What is agent reliability engineering?
A: Applying SRE principles to AI agents: setting SLOs, monitoring error rates, implementing retries, and handling graceful degradation.

Q: What is the 'fallback chain' pattern for agents?
A: If the primary model/approach fails, the agent tries alternative models or strategies in a predefined order.

Q: What is idempotency in agent actions?
A: Ensuring that performing the same action multiple times produces the same result, important for retry safety.

Q: What is the 'dry run' pattern for agent actions?
A: The agent simulates actions without executing them, showing the user what would happen for approval before actual execution.

Q: What is agent composability?
A: The ability to combine simple agents into more complex systems, where each agent is a building block in a larger workflow.

Q: What is the role of testing in AI agent development?
A: Verifying that agents behave correctly through unit tests (individual tools), integration tests (workflows), and evaluation benchmarks.

Q: What is 'eval-driven development' for AI agents?
A: An approach where agent development is guided by evaluation metrics, with changes accepted only if they improve eval scores.

Q: What is prompt versioning for agents?
A: Tracking changes to agent prompts over time with version control, enabling comparison, rollback, and systematic improvement.

Q: What is the 'context engineering' concept?
A: The systematic practice of designing and optimizing what information goes into an agent's context window for maximum effectiveness.

Q: What is instruction hierarchy in agent prompts?
A: Structuring system prompts with clear priority levels so the agent knows which instructions take precedence in case of conflicts.

Q: What is the 'agent registry' concept?
A: A catalog of available agents with their capabilities, endpoints, and metadata, enabling dynamic discovery and orchestration.

Q: What are the key challenges in productionizing AI agents?
A: Reliability, cost control, latency, safety, observability, memory management, error handling, and maintaining consistent quality.

Q: What is the future direction of AI agents?
A: Toward more autonomous, reliable agents with better memory, multi-modal capabilities, real-time learning, and seamless human-agent collaboration.

Q: What is 'agentic AI' as a paradigm?
A: The shift from AI as a reactive tool to AI as a proactive collaborator that autonomously pursues goals with minimal human oversight.

Q: How might AI agents change software development?
A: By automating routine coding tasks, accelerating prototyping, handling bug fixes, managing infrastructure, and enabling developers to focus on architecture and design.

Q: What is an agent's 'reasoning trace'?
A: The recorded sequence of thoughts, tool calls, and observations the agent produces during task execution, useful for debugging and evaluation.

Q: What is step-back prompting for agents?
A: A technique where the agent first considers the high-level concept or principle before answering a specific question, improving accuracy.

Q: What is the 'retrieval-then-generate' pattern?
A: The agent first retrieves relevant context from tools or memory, then generates its response grounded in that retrieved information.

Q: What is structured planning in AI agents?
A: Having the agent output an explicit, structured plan (e.g., numbered steps in JSON) before executing, enabling plan validation and editing.

Q: What is the 'code-as-action' paradigm?
A: Instead of selecting from predefined tools, the agent writes executable code to perform any action, maximizing flexibility.

Q: What is tool-use fidelity?
A: How accurately and reliably an agent uses tools according to their specifications, without malformed calls or misuse.

Q: What is the 'observation budget' in agents?
A: Limiting how much output from tool calls the agent processes to avoid overwhelming the context window with verbose results.

Q: What is prompt injection and how does it threaten agents?
A: A security attack where malicious input tricks the agent into ignoring its instructions, potentially executing harmful actions.

Q: What is indirect prompt injection?
A: Prompt injection embedded in external data sources (websites, documents, emails) that the agent reads and inadvertently follows.

Q: How can agents defend against prompt injection?
A: Input sanitization, instruction hierarchy enforcement, output filtering, sandboxed execution, and separating data from instructions.

Q: What is the 'confused deputy' problem in AI agents?
A: When an agent with elevated permissions is tricked into performing actions that benefit an attacker rather than the authorized user.

Q: What is data exfiltration risk in AI agents?
A: The risk that an agent could be manipulated into sending sensitive data to external endpoints through tool calls or generated code.

Q: What is the OWASP Top 10 for LLM Applications?
A: A security framework identifying key vulnerabilities in LLM-based systems including prompt injection, insecure output handling, and excessive agency.

Q: What does 'excessive agency' mean in OWASP's LLM Top 10?
A: When an LLM agent is granted too many capabilities, too much autonomy, or too many permissions, increasing the blast radius of errors.

Q: What is the 'defense in depth' principle for AI agents?
A: Layering multiple security measures (input validation, output filtering, sandboxing, monitoring) so no single failure compromises safety.

Q: What is output validation in AI agents?
A: Checking the agent's generated outputs and tool calls against safety rules, schemas, and business logic before execution.

Q: What is Copilot's 'intent detection'?
A: Copilot's ability to understand what the developer wants to accomplish from partial code, comments, or natural language descriptions.

Q: What is Copilot's ghost text?
A: The semi-transparent code suggestions that appear inline as you type, which can be accepted with Tab or dismissed.

Q: How does Copilot handle multiple suggestion candidates?
A: It generates multiple completions and can cycle through alternatives, allowing the developer to pick the best one.

Q: What is Copilot's context gathering strategy?
A: Copilot collects context from the current file, open tabs, imports, file names, and project structure to inform suggestions.

Q: What is the 'neighbor tab' concept in Copilot?
A: Copilot uses content from open editor tabs as additional context to improve the relevance of code suggestions.

Q: What is Copilot for Docs?
A: A feature that allows Copilot to search and reference official documentation to provide accurate, up-to-date answers.

Q: How does Copilot handle security vulnerabilities in suggestions?
A: It includes filters to block suggestions matching known insecure coding patterns and integrates with GitHub Advanced Security.

Q: What is the Copilot suggestion acceptance rate metric?
A: The percentage of Copilot suggestions that developers accept, used to measure effectiveness and relevance.

Q: What is Copilot Individual vs. Business vs. Enterprise?
A: Different tiers: Individual for personal use; Business for teams with org management; Enterprise with enhanced security, compliance, and customization.

Q: What additional features does Copilot Enterprise offer?
A: Knowledge bases, fine-tuned models on org code, enhanced privacy controls, audit logs, and admin policy management.

Q: What is GitHub Copilot's IP indemnity?
A: GitHub's commitment to defend Enterprise customers against IP claims related to Copilot suggestions when code suggestion filters are enabled.

Q: What is the Copilot Content Exclusion feature?
A: Allows organizations to specify files, paths, or repositories whose content should never be used as Copilot context.

Q: How does Copilot integrate with GitHub Issues?
A: Through Copilot Coding Agent, which can read issues, understand requirements, and autonomously implement solutions.

Q: How does Copilot integrate with GitHub Pull Requests?
A: By auto-generating PR descriptions, providing code review comments, and suggesting improvements in PR conversations.

Q: What is GitHub Models?
A: A platform within GitHub providing access to various AI models (GPT-4o, Claude, Llama, etc.) directly in the developer workflow.

Q: How can GitHub Models be used with Copilot?
A: Developers can test and switch between available models to find the best fit for their coding tasks.

Q: What is the Copilot Trust Shield?
A: Copilot's system for filtering out suggestions that match public code, checking licenses, and blocking insecure patterns.

Q: How does Copilot handle code duplication detection?
A: It includes a filter that detects when suggestions closely match existing public code and can block or flag such suggestions.

Q: What is Copilot's telemetry data handling?
A: Copilot collects usage metrics (acceptance rates, languages used) but for Business/Enterprise, does not retain code snippets for training.

Q: What is the Copilot feedback mechanism?
A: Developers can thumbs-up/down suggestions and provide feedback, helping improve model quality over time.

Q: What is 'Copilot for Infrastructure'?
A: Copilot capabilities for infrastructure-as-code, helping write Terraform, CloudFormation, Kubernetes manifests, and CI/CD configs.

Q: How can Copilot assist with writing GitHub Actions workflows?
A: By suggesting workflow YAML syntax, action configurations, and complete CI/CD pipeline definitions based on project context.

Q: What is Copilot Spark?
A: A GitHub feature that lets non-developers build simple applications through natural language conversation with Copilot.

Q: How does Copilot handle multi-language projects?
A: Copilot supports 20+ programming languages and can switch context between languages within the same project.

Q: What is the Copilot API?
A: An API for programmatically interacting with Copilot features, enabling integration into custom tools and workflows.

Q: What is the 'memory as context' approach?
A: Simply including all relevant past information directly in the prompt — works for short histories but doesn't scale.

Q: What is 'memory as retrieval' approach?
A: Storing memories externally and retrieving only relevant ones per query — scales better but requires good retrieval quality.

Q: What is 'memory as parameters' approach?
A: Encoding memories directly into model weights through fine-tuning — permanent but inflexible and expensive to update.

Q: What is hybrid memory architecture?
A: Combining multiple memory approaches (context, retrieval, parameters) to leverage each one's strengths for different types of information.

Q: What is a memory buffer in agent frameworks?
A: A fixed-size container that holds recent interactions, automatically evicting oldest entries when full.

Q: What is a summary buffer memory?
A: A memory that maintains a running summary of the conversation so far, updated as new messages are added.

Q: What is entity extraction for agent memory?
A: Automatically identifying and extracting key entities (people, organizations, concepts) from conversations to store as structured memory.

Q: What is relationship extraction in agent memory?
A: Identifying connections between entities (e.g., 'Alice works at Company X') and storing them in a knowledge graph.

Q: What is memory-based user modeling?
A: Building a model of the user's expertise, preferences, and needs based on accumulated memories of past interactions.

Q: What is proactive memory retrieval?
A: The agent automatically retrieving relevant memories before the user asks, anticipating what information might be useful.

Q: What is the 'memory bank' concept in AI agents?
A: A structured collection of categorized memories (user preferences, project knowledge, task history) that the agent can selectively access.

Q: What is memory transfer between agents?
A: Moving relevant memories from one agent to another when handing off tasks, ensuring continuity and context preservation.

Q: What is the 'shared context window' approach for multi-agent memory?
A: Multiple agents contributing to and reading from a single shared context, enabling real-time coordination.

Q: What is memory distillation?
A: Compressing verbose memories into compact, essential representations while preserving key information for future retrieval.

Q: What is progressive summarization in agent memory?
A: Creating increasingly condensed summaries as memories age: full text → detailed summary → brief summary → key facts.

Q: What is the 'memory garden' metaphor?
A: Treating agent memory like a garden that needs tending — planting new memories, pruning irrelevant ones, and nurturing connections between them.

Q: What is a memory policy?
A: Rules governing what the agent should remember, how long to keep it, when to consolidate, and what to forget.

Q: What is attention-based memory retrieval?
A: Using transformer attention mechanisms to dynamically focus on the most relevant stored memories during inference.

Q: What is the role of timestamps in agent memory?
A: Enabling temporal queries ('what happened last week?'), recency-based retrieval, and memory decay calculations.

Q: What is multi-hop memory retrieval?
A: Following chains of associations across multiple memory entries to answer complex queries that no single memory can address.

Q: What is memory coherence in AI agents?
A: Ensuring the agent's memories don't contradict each other and form a consistent, logical knowledge base.

Q: What is the 'note-taking' pattern for agent memory?
A: The agent explicitly saves important information as structured notes during conversations, tagged for future retrieval.

Q: How does Claude implement memory?
A: Claude uses a 'memory' tool to save, search, and delete user-specific information across conversations, with user visibility and control.

Q: How does ChatGPT implement memory?
A: ChatGPT can automatically or manually save facts about users across conversations, displayed in a 'Memory' settings panel the user can manage.

Q: What is the difference between explicit and inferred memory?
A: Explicit memory is directly stated by the user ('I prefer Python'); inferred memory is deduced by the agent from behavior patterns.

Q: What is memory transparency?
A: Giving users visibility into what the agent remembers about them and the ability to view, edit, and delete memories.

Q: What is the 'right to be forgotten' for AI agent memory?
A: The ability for users to request complete deletion of their stored memories, important for privacy compliance.

Q: What is memory sandboxing?
A: Isolating an agent's memory scope so it can only access memories relevant to its current role or task domain.

Q: What is the 'memory scope' concept?
A: Defining boundaries for which memories are accessible — global (all users), user-level, session-level, or task-level scope.

Q: What is cross-agent memory consistency?
A: Ensuring multiple agents accessing shared memory have a consistent view, handling concurrent reads and writes safely.

Q: What is the 'router' agent pattern?
A: An agent that analyzes incoming requests and routes them to the most appropriate specialized agent based on task type.

Q: What is semantic routing for agents?
A: Using embedding similarity to match user queries to the most relevant agent or tool, rather than rule-based routing.

Q: What is the 'parallel execution' pattern for agents?
A: Running multiple independent agent tasks simultaneously and combining results, reducing total execution time.

Q: What is 'map-reduce' in multi-agent systems?
A: Distributing subtasks to multiple agents (map), then combining their results into a final output (reduce).

Q: What is agent handoff?
A: Seamlessly transferring a conversation or task from one agent to another while preserving context and progress.

Q: What is the 'pipeline' pattern in multi-agent systems?
A: Agents arranged in sequence, each transforming the output of the previous one, like an assembly line.

Q: What is the 'ensemble' pattern for agents?
A: Multiple agents independently tackle the same task, and their outputs are compared or aggregated for a better final result.

Q: What is agent self-improvement?
A: An agent using feedback from its own performance to improve its prompts, tool usage, or decision-making over time.

Q: What is the 'reward hacking' risk in AI agents?
A: When an agent finds shortcuts to maximize its reward signal without actually achieving the intended goal.

Q: What is 'specification gaming' in AI agents?
A: An agent satisfying the literal specification of a task while violating its intended spirit, a form of misalignment.

Q: What is the 'Goodhart's Law' problem for AI agents?
A: When a measure (metric) becomes a target, it ceases to be a good measure — agents may optimize metrics rather than actual objectives.

Q: What is the role of unit testing in agent development?
A: Testing individual components (tool handlers, memory retrieval, prompt templates) in isolation to ensure correctness.

Q: What is integration testing for AI agents?
A: Testing the full agent workflow end-to-end, including LLM calls, tool usage, and memory operations, with expected inputs and outputs.

Q: What is regression testing for AI agents?
A: Re-running previous test cases after changes to ensure the agent's behavior hasn't degraded on previously working scenarios.

Q: What is 'golden dataset' evaluation for agents?
A: Testing agents against a curated set of questions/tasks with known correct answers to measure accuracy and catch regressions.

Q: What is the role of canary deployments for AI agents?
A: Rolling out agent updates to a small percentage of users first, monitoring for issues before full deployment.

Q: What is the 'shadow mode' deployment pattern for agents?
A: Running a new agent version alongside the production version without serving its outputs, comparing results to validate before switching.

Q: What is agent cost per task?
A: The total cost of LLM API calls, tool invocations, and compute resources consumed by an agent to complete a single task.

Q: How can you reduce agent LLM costs?
A: By caching, using smaller models for simple steps, reducing context size, batching requests, and optimizing prompt length.

Q: What is token efficiency in agent design?
A: Minimizing token usage while maintaining output quality by concise prompting, selective context inclusion, and output format optimization.

Q: What is the 'ask before acting' pattern?
A: The agent explains its planned actions and waits for user approval before executing, balancing autonomy with user control.

Q: What is progressive autonomy for AI agents?
A: Gradually increasing an agent's autonomy as trust is established — starting with human approval for all actions and relaxing over time.

Q: What is the 'confidence threshold' pattern?
A: The agent only acts autonomously when its confidence exceeds a threshold; below that, it asks for human confirmation.

Q: What is agent observability vs. interpretability?
A: Observability is tracking what the agent does (actions, logs); interpretability is understanding why the agent made specific decisions.

Q: What is the role of LLM-as-judge in agent evaluation?
A: Using an LLM to evaluate another agent's outputs for quality, correctness, and helpfulness as an automated evaluation method.

Q: What is the 'two-agent verification' pattern?
A: Having a second agent independently verify the first agent's output before it's presented to the user or executed.

Q: What is the difference between synchronous and asynchronous agents?
A: Synchronous agents respond in real-time during conversation; asynchronous agents work on tasks in the background and report when done.

Q: What is the event-driven agent architecture?
A: Agents that activate in response to events (new issue filed, build failed, PR opened) rather than direct user commands.

Q: What is the 'cron agent' pattern?
A: An agent that runs on a schedule to perform maintenance tasks like cleanup, summarization, reporting, or monitoring.

Q: What is the role of feedback loops in agent improvement?
A: User feedback, eval results, and performance metrics are fed back to refine prompts, tools, and agent behavior iteratively.

Q: What is 'tool poisoning' as an attack on AI agents?
A: Injecting malicious instructions into tool descriptions or MCP server responses to manipulate agent behavior.

Q: What is the 'principle of minimal authority' for agents?
A: Agents should request only the permissions they need, for only the duration they need them, minimizing potential for misuse.

Q: What is structured agent output validation?
A: Checking that agent outputs conform to expected JSON schemas, code syntax, or format requirements before processing.

Q: What is the 'planning with verification' pattern?
A: The agent generates a plan, then a verifier (human or AI) checks it before execution, catching errors early.

Q: What is the role of rate limiting in agent safety?
A: Capping the number of actions, API calls, or iterations an agent can perform to prevent runaway execution and cost overruns.

Q: What is an agent's 'execution budget'?
A: A predefined limit on tokens, API calls, time, or cost that the agent cannot exceed for a single task.

Q: What is the OpenAI Agents SDK?
A: An open-source Python SDK by OpenAI for building multi-agent systems with built-in support for tool use, handoffs, guardrails, and tracing.

Q: What are 'handoffs' in the OpenAI Agents SDK?
A: A mechanism to transfer control from one agent to another specialized agent, enabling modular multi-agent architectures.

Q: What is Google's Agent Development Kit (ADK)?
A: A framework by Google for building, evaluating, and deploying AI agents, with built-in support for multi-agent orchestration and tool use.

Q: What is the difference between an 'agent' and an 'assistant' in AI terminology?
A: An assistant typically responds to queries; an agent autonomously pursues goals, makes decisions, uses tools, and can operate with minimal human guidance.

Q: What is Claude's tool_use feature?
A: Anthropic's implementation of function calling that allows Claude to invoke developer-defined tools and receive their results during conversation.

Q: What is Anthropic's 'computer use' tool?
A: A tool that lets Claude take screenshots, move the mouse, click, and type on a virtual computer to complete GUI-based tasks.

Q: What is the 'text_editor' tool in Claude's agent toolset?
A: A tool that allows Claude to view, create, and edit files by specifying old/new string replacements, enabling precise code modifications.

Q: What is the 'bash' tool in Claude's agent toolset?
A: A tool giving Claude the ability to execute shell commands to install packages, run tests, compile code, and interact with the system.

Q: What is Google's Vertex AI Agent Builder?
A: A managed platform for creating, deploying, and managing AI agents with integrations to Google Cloud services and enterprise data.

Q: What is Amazon Bedrock Agents?
A: An AWS service for building managed AI agents that can orchestrate multi-step tasks using foundation models and enterprise data sources.

Q: What is Microsoft Semantic Kernel?
A: An open-source SDK for integrating LLMs into applications with support for plugins, planners, and memory — a foundation for building agents.

Q: What is a 'plugin' in Semantic Kernel?
A: A collection of functions (native code or LLM prompts) that the agent can use as tools within the Semantic Kernel framework.

Q: What is the 'planner' in Semantic Kernel?
A: A component that uses the LLM to create an execution plan from available plugins to accomplish a user's goal.

Q: What is DSPy?
A: A framework for algorithmically optimizing LLM prompts and weights, useful for systematically improving agent performance.

Q: How can DSPy improve AI agents?
A: By automatically tuning the agent's prompts through optimization rather than manual prompt engineering, leading to more reliable behavior.

Q: What is LlamaIndex in the context of AI agents?
A: A data framework for connecting LLMs to external data, commonly used to build RAG-powered agents with structured data access.

Q: What is a LlamaIndex 'query engine agent'?
A: An agent that uses LlamaIndex query engines as tools to retrieve and reason over structured and unstructured data sources.

Q: What is Haystack by deepset?
A: An open-source framework for building production-ready LLM applications including RAG pipelines and AI agents with modular components.

Q: What is an 'agent protocol'?
A: A standardized API specification for interacting with AI agents, enabling interoperability between different agent implementations.

Q: What is the AI Agent Protocol specification?
A: An open spec defining REST endpoints for creating, running, and managing AI agent tasks, enabling agent interoperability.

Q: What is WebArena benchmark?
A: A benchmark that evaluates AI agents on realistic web-based tasks like shopping, forum navigation, and content management.

Q: What is AgentBench?
A: A benchmark suite that evaluates LLM-as-agent across diverse environments including OS, databases, web browsing, and coding.

Q: What is TapeAgents?
A: A framework by ServiceNow that structures agent computation as a persistent, replayable tape of thoughts, actions, and observations.

Q: What is the 'tape' concept in TapeAgents?
A: A sequential log of all agent steps (thoughts, tool calls, observations) that serves as both execution trace and memory.

Q: What is smolagents by Hugging Face?
A: A lightweight library for building AI agents that focuses on code-based tool calling where the LLM writes Python code as actions.

Q: What is the difference between JSON-based and code-based tool calling?
A: JSON-based: LLM outputs structured JSON to invoke tools. Code-based: LLM writes executable code that calls tools, offering more flexibility.

Q: What is 'tool sandboxing' in agent frameworks?
A: Running tool executions in isolated environments (containers, VMs) to prevent agents from causing unintended side effects on the host system.

Q: What is the 'approval workflow' pattern for agents?
A: The agent queues proposed actions for human review, executing only after approval — commonly used for high-stakes operations.

Q: What is the difference between stateless and stateful agents?
A: Stateless agents treat each interaction independently; stateful agents maintain context, memory, and progress across interactions.

Q: What is agent persistence?
A: Saving the complete agent state (memory, progress, configuration) so it can be restored and resumed after interruption.

Q: What is the 'resumable agent' pattern?
A: Designing agents so they can pause mid-task, persist their state, and resume execution later from where they left off.

Q: What is event sourcing for AI agents?
A: Storing every agent action as an immutable event, enabling full replay of the agent's history and debugging.

Q: What is the CQRS pattern applied to agent memory?
A: Command Query Responsibility Segregation — separating the write path (storing memories) from the read path (retrieving memories) for optimization.

Q: What is a 'memory snapshot' in AI agents?
A: A point-in-time capture of the agent's entire memory state, useful for debugging, testing, and rollback.

Q: What is federated agent memory?
A: Distributing memory across multiple stores or agents, each owning a portion, with coordination protocols for cross-agent queries.

Q: What is the role of embedding models in agent memory?
A: Embedding models convert text memories into vectors for semantic storage and retrieval, with quality directly impacting retrieval accuracy.

Q: What is the impact of embedding model choice on agent memory?
A: Different embedding models vary in dimensionality, semantic quality, speed, and cost — affecting retrieval accuracy and performance.

Q: What are common embedding models used for agent memory?
A: OpenAI text-embedding-3-small/large, Cohere embed-v3, BGE, E5, and sentence-transformers models.

Q: What is the 'chunking strategy' for agent memory?
A: How the agent divides information into discrete memory units — by sentence, paragraph, semantic boundary, or fixed token count.

Q: Why does chunk size matter for agent memory?
A: Too large chunks waste context tokens and reduce precision; too small chunks lose context. Optimal size depends on the use case.

Q: What is memory indexing latency?
A: The time it takes to store a new memory and make it searchable, important for real-time agent interactions.

Q: What is the 'write-through' memory pattern?
A: Immediately persisting memories to long-term storage as they're created, ensuring no memory loss if the session ends unexpectedly.

Q: What is the 'write-back' memory pattern?
A: Batching memory writes and persisting them periodically, trading some durability risk for better performance during interaction.

Q: What is 'memory warmup' for AI agents?
A: Pre-loading relevant memories into the agent's context at the start of a session based on user identity and anticipated needs.

Q: What is contextual bandits in agent tool selection?
A: An online learning approach where the agent learns which tools work best in which contexts through trial and reward signals.

Q: What is the 'tool-use trajectory' of an agent?
A: The sequence of tools the agent called during task execution, useful for analyzing efficiency and identifying improvement opportunities.

Q: What is the difference between 'agentic RAG' and 'standard RAG'?
A: Standard RAG retrieves once then generates; agentic RAG lets the agent iteratively retrieve, reason, refine queries, and retrieve again.

Q: What is iterative retrieval in agentic RAG?
A: The agent performs multiple rounds of retrieval, using each round's results to refine subsequent queries for more relevant information.

Q: What is self-RAG?
A: A pattern where the agent decides whether retrieval is needed, retrieves if so, evaluates the relevance of results, and may re-retrieve.

Q: What is corrective RAG (CRAG)?
A: A RAG variant where the agent evaluates retrieved documents for relevance and triggers web search or other sources if retrieval quality is low.

Q: What is adaptive RAG?
A: A RAG strategy that dynamically chooses between no retrieval, single retrieval, or iterative retrieval based on query complexity.

Q: What is the 'reflection-action' loop in agents?
A: After taking an action, the agent reflects on the outcome, learns from it, and adjusts its strategy for the next action.

Q: What is the 'experience replay' concept in agent learning?
A: Storing past experiences (state-action-result tuples) and replaying them during learning to improve the agent's decision-making policy.

Q: What is the role of system prompts in agent safety?
A: System prompts define behavioral boundaries, ethical guidelines, and safety rules that constrain the agent's actions and responses.

Q: How should agent system prompts be structured?
A: With clear sections for identity/role, capabilities, tools, constraints, safety rules, and behavioral guidelines, ordered by priority.

Q: What is 'prompt leaking' and why is it a risk for agents?
A: When an agent reveals its system prompt contents to users, potentially exposing business logic, safety rules, or competitive advantages.

Q: What is red-teaming for AI agents?
A: Systematically testing agents with adversarial inputs and scenarios to discover vulnerabilities, failure modes, and safety gaps.

Q: What is blue-teaming for AI agents?
A: Implementing defensive measures and monitoring to protect agents from the vulnerabilities discovered during red-teaming.

Q: What is the 'agent constitution' concept?
A: A set of inviolable principles the agent must follow, checked against during self-reflection, inspired by Constitutional AI.

Q: What is the future of AI agent memory?
A: Moving toward neuromorphic memory systems, infinite context via memory hierarchies, real-time learning, and more human-like memory management.

Q: What is the relationship between agent memory and personalization?
A: Memory enables personalization by storing user preferences, interaction history, and learned patterns to tailor future agent behavior.

Q: How do production AI agents handle concurrent users?
A: Through session isolation, user-scoped memory, stateless request handling with session IDs, and horizontal scaling of agent instances.

Q: What is an agent's 'tool call schema'?
A: A JSON schema definition describing a tool's name, description, required/optional parameters, and return type, used by the LLM to correctly invoke tools.

Q: What is 'graceful degradation' in AI agents?
A: When a tool or service fails, the agent continues functioning by using alternatives, providing partial results, or informing the user rather than crashing.

Q: What is the 'chain-of-verification' pattern?
A: After generating output, the agent creates verification questions about its own response, answers them independently, and corrects any inconsistencies found.

Q: What is model distillation for AI agents?
A: Training a smaller, faster model to replicate a larger model's agent behavior, reducing inference cost while maintaining quality.

Q: What is 'speculative execution' in AI agents?
A: Running multiple possible next steps in parallel before knowing which one is needed, then using the correct result, trading compute for latency.

Q: What is the 'toolformer' concept?
A: An approach where the LLM is fine-tuned to autonomously decide when and how to call tools within its text generation process.

Q: What is an 'agent mesh' architecture?
A: A network of peer agents that can discover, communicate with, and delegate to each other dynamically without a central orchestrator.

Q: What is trust propagation in multi-agent systems?
A: How trust relationships between agents are established and transferred — e.g., if Agent A trusts Agent B and B trusts C, should A trust C?

Q: What is the 'capability discovery' pattern for agents?
A: Agents dynamically discovering what tools and other agents are available at runtime rather than having a fixed, hardcoded set.

Q: What is the difference between 'warm' and 'cold' agent starts?
A: A warm start loads cached context, previous state, and pre-fetched memories; a cold start initializes from scratch with no prior context.

Q: What is the 'backpressure' pattern in agent systems?
A: When downstream tools or services are overwhelmed, the agent slows its request rate to avoid overloading them.

Q: What is 'semantic versioning' for agent prompts?
A: Applying version numbers to system prompts (major.minor.patch) to track behavioral changes and enable controlled rollouts.

Q: What is the 'circuit breaker' pattern for agent tool calls?
A: Automatically stopping tool calls to a failing service after repeated errors, trying again later, preventing cascade failures.

Q: What is 'agent fine-tuning'?
A: Training an LLM specifically on agent interaction traces (tool calls, reasoning, observations) to improve its native agent capabilities.

Q: What are synthetic agent training data?
A: Artificially generated examples of agent interactions used to fine-tune models for better tool use, planning, and reasoning.

Q: What is the 'inner critic' pattern for agents?
A: An internal evaluation step where the agent scores its own draft response before presenting it, rewriting if the score is too low.

Q: What is the relationship between agents and microservices?
A: Agents can be deployed as microservices, each with its own API, enabling independent scaling, updating, and orchestration.

Q: What is the 'agent gateway' pattern?
A: A central entry point that authenticates requests, routes to appropriate agents, handles rate limiting, and provides unified logging.

Q: What is 'trajectory optimization' for AI agents?
A: Improving the sequence of actions an agent takes to complete a task by analyzing past trajectories and finding more efficient paths.

Q: What is the role of 'few-shot examples' in agent memory?
A: Storing successful interaction examples as retrievable memories that the agent can include in its context for similar future tasks.

Q: What is 'memory-augmented planning'?
A: The agent retrieves memories of similar past tasks to inform its current planning, learning from successes and failures.

Q: What is the 'cognitive load' problem in agent design?
A: Overwhelming the agent with too much context, too many tools, or too complex instructions, degrading its performance and reliability.

Q: How does chunked tool output help agents?
A: Breaking large tool outputs into chunks and only showing summaries or relevant portions keeps the agent's context focused and within limits.

Q: What is 'outcome-based evaluation' for agents?
A: Measuring agent quality by whether it achieves the desired end result, rather than evaluating individual intermediate steps.

Q: What is 'process-based evaluation' for agents?
A: Evaluating each step of the agent's reasoning and actions, not just the final result, to ensure the process is sound even if the outcome is correct.

Q: What is the 'agent dashboard' concept?
A: A monitoring UI showing real-time agent activity, success rates, costs, error logs, and performance metrics for operational oversight.

Q: What is the economic impact of AI coding agents?
A: Studies suggest AI coding agents can improve developer productivity by 20-50%, reducing time on routine tasks and enabling focus on higher-level design.