Agentic QA
AI Agent Testing
A single prompt gives you an answer. An agent gives you a sequence of decisions and side effects — which is far more useful and far more dangerous. Testing agents means testing behaviour over time, not output in isolation.
The roadmap, stage by stage
Design before you test
Most agent failures are design failures. Split responsibilities into planner, executor and reviewer roles so each step is inspectable.
- Planner: decides what to do
- Executor: performs the action
- Reviewer: checks the result
Guardrails and termination
Every loop needs an exit. Termination conditions, step limits and human-in-the-loop approval gates keep runs bounded and reviewable.
What to assert
Assert on the trace, not just the final answer: which tools were called, with what arguments, how many steps, and whether guardrails held.
- Tool-call correctness
- Step count and cost budgets
- Guardrail and approval enforcement
- Recovery from tool failure
Regression for agents
Fix your scenarios, replay them, and diff traces between versions. Behavioural drift in agents is silent otherwise.
Skills you'll gain
- Multi-agent orchestration (AutoGen)
- LangChain and LangGraph workflows
- Trace-based assertions
- Human-in-the-loop design
- Agent regression testing
Why it matters for your career
Teams shipping agents need someone who can prove the agent behaves. That is a QA role with unusually high leverage.
Recommended learning path
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FAQ
How is agent testing different from API testing?
The system chooses its own path, so you assert on the trace and the guardrails as much as the final output.
Should agents run unsupervised in CI?
Only for non-destructive tasks. Anything with side effects should keep an approval gate.
Which framework should I learn?
AutoGen for multi-agent conversations, LangGraph for explicit stateful orchestration. The courses cover both.
Not sure where you fit on this roadmap?
Take the quiz for a personalised path, or ask Tia — she'll build a plan around your experience.