Testing
AI agent pipelines need CI/CD just like any software — but standard pipelines don't account for LLM non-determinism, evaluation costs, or model-level regressions. This guide covers building CI/CD pipelines specifically for AI agent systems: fast deterministic checks, LLM evaluation gates, model upgrade workflows, safety guardrails,
Testing
AI agents call external APIs — search engines, databases, Slack, GitHub, payment processors. Mocking these in tests is essential: real APIs are slow, rate-limited, non-deterministic, and sometimes irreversible. This guide covers four mocking strategies for agentic systems: HTTP interception, tool-level mocking, record/replay, and in-process fakes. An
Testing
AI agent workflows are harder to test than traditional software because the intermediate steps (which tools get called, in what order, with what arguments) are non-deterministic. This guide covers deterministic testing strategies: behavioral assertions over tool calls, golden dataset testing, state-based verification, and contract testing for agent-tool