Systematic evaluation is essential to building reliable agents. Standardized benchmarks include SWE-bench, which evaluates agents on resolving real GitHub issues from popular Python repositories; WebArena, which tests realistic web-based tasks like shopping and forum navigation; and GAIA for general AI assistant capabilities. AgentBench evaluates LLM-as-agent across diverse environments including operating systems, databases, and coding. Evaluation approaches vary: outcome-based evaluation measures whether the agent achieves the desired end result, while process-based evaluation inspects each reasoning step. Golden dataset evaluation tests against curated questions with known correct answers to catch regressions, and LLM-as-judge uses another model to evaluate quality and correctness automatically.
Testing strategies mirror software engineering best practices. Unit tests verify individual components like tool handlers and memory retrieval in isolation; integration tests exercise full agent workflows including LLM calls and tool usage; regression tests re-run previous cases after changes to detect degradation. Eval-driven development accepts changes only when they improve eval scores. Beyond testing, observability is critical in production. LangSmith provides tracing, evaluation, and monitoring for LLM-based applications, and traces are end-to-end records showing each step, tool call, LLM invocation, and their relationships. The agent dashboard concept offers real-time visibility into success rates, costs, error logs, and performance metrics.
Production deployment introduces operational concerns. Reliability engineering applies SRE principles, setting SLOs, monitoring error rates, implementing retries, and handling graceful degradation. The fallback chain pattern tries alternative models or strategies when the primary approach fails. Rate limiting caps actions and API calls to prevent runaway execution, while an execution budget imposes hard limits on tokens, time, and cost per task. Canary deployments roll updates out to small user percentages first, and shadow mode runs new agent versions alongside production without serving their outputs. Cost optimization strategies include semantic caching of tool call results, prompt compression, smaller models for simple steps, and model routing to dynamically select the cheapest adequate model for each task. Memory management at scale requires capacity planning, memory warmup for warm starts, isolation between users, and privacy compliance including GDPR's right to deletion and memory encryption at rest and in transit.