1. Foundations of AI Agents
An AI agent is an autonomous software system that perceives its environment, reasons about goals, and takes actions without continuous human intervention. Unlike a simple chatbot t...
Read full chapter →The essential AI Agents (500 Questions) cheat sheet: 8 concise chapters you can read in minutes, distilled from the full deck. When you're ready, drill the flashcards or test yourself under exam conditions.
An AI agent is an autonomous software system that perceives its environment, reasons about goals, and takes actions without continuous human intervention. Unlike a simple chatbot t...
Read full chapter →Modern LLM-based agents extend their capabilities far beyond text generation through tool use — the ability to invoke external tools such as APIs, code interpreters, databases, and...
Read full chapter →AI agents require memory to maintain context across interactions, learn from past experiences, recall user preferences, and avoid repeating mistakes. Memory is generally classified...
Read full chapter →When a single agent becomes insufficient for complex tasks, multi-agent architectures distribute responsibilities across multiple specialized agents. Several coordination patterns...
Read full chapter →A rich ecosystem of frameworks supports agent development. LangGraph, from the LangChain team, builds stateful multi-step agent workflows as directed graphs with nodes representing...
Read full chapter →GitHub Copilot has evolved from inline code completion into a comprehensive agentic coding platform. The foundation is Copilot code completion, which provides real-time ghost text...
Read full chapter →As agents gain autonomy and tool access, safety becomes paramount. Guardrailing implements safety checks, input and output filters, and constraints to prevent agents from taking ha...
Read full chapter →Systematic evaluation is essential to building reliable agents. Standardized benchmarks include SWE-bench, which evaluates agents on resolving real GitHub issues from popular Pytho...
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