Topic
Testing & QA for AI Agents
12 posts on testing & qa for ai agents — part of ai agents & automation on the n4n AI blog.
What belongs in a CI test suite for AI agents
Practical guide to building a CI test suite for AI agents: mock the LLM, test tool use, contract providers, and gate on cost and latency.
Tracing agent failures with LangSmith
A hands-on tutorial to trace agent failures LangSmith: configure tracing, build a failing LLM agent, inspect spans, and enforce correctness in CI pipelines.
Testing whether agent memory actually persists
A practical how-to for testing AI agent memory persistence across sessions, with runnable code to verify state survives restarts and model swaps.
Simulating multi-turn conversations to test agents
Learn how to simulate multi-turn conversations agent testing with deterministic harnesses, mock users, and assertion logic for reliable QA.
Regression testing agents after a prompt change
A practical how-to for regression testing AI agents prompt changes: capture baselines, build eval harnesses, and gate deploys with differential tests.
Load testing AI agents under concurrent traffic
A practitioner's guide to load testing AI agents under concurrent traffic: model real workloads, instrument, generate load, and verify agent correctness.
Unit testing AI agents with pytest and mocked tools
Learn how to isolate LLM calls and external tools to unit test AI agents with pytest, using mocks and fixtures for deterministic, fast tests.
Red-teaming your AI agent: adversarial test cases
A practical guide to red-teaming AI agents adversarial testing: build adversarial test cases, automate attacks, and harden agent workflows against exploits.
Catching hallucinated tool calls before production
A practical guide to detecting hallucinated tool calls AI agents emit, with testing patterns, validation code, and pitfalls to avoid before production.
Building a golden dataset to test agent behavior
Learn how to build a golden dataset for testing AI agents from production traces, define expected behavior, and run automated regression checks in CI.
Benchmarking tool-calling accuracy in AI agents
A practical guide to benchmark tool-calling accuracy AI agents: build eval sets, instrument runs, score structurally, and track regressions in CI.
A/B testing prompts for production AI agents
A practical guide to A/B testing prompts for AI agents in production: define metrics, assign variants, route requests, log, and analyze results with code.
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