Advanced jobs / Evaluating and regression-testing your agents
Evaluating and regression-testing your agents
Measures whether your agent got better or worse every time you change a prompt, model or tool.
- Difficulty
- Hard (4/5)
- Human checkpoints
- 3 to plan for
- Matching bots
- 5 on BotPickr
The job
A test harness for your own bots: a dataset of realistic tasks, automatic and human scoring, traces of each run, and checks that run whenever something changes.
Why it is hard
- Agent outputs are open-ended, so simple pass/fail checks are not enough.
- Models change under you; behavior can shift without code changes.
- You need realistic test cases, including failures from production.
- Using one AI to judge another needs its own checking.
What a good setup needs
Tools
- An evaluation tool (LangSmith evaluation, Pydantic Evals)
- A dataset built from real conversations and known failures
- Tracing to see why a run failed
- CI that runs the eval set on every change
Guardrails
- Keep a held-out set that you never tune against
- Spot-check any AI judge against human ratings
- Track cost and latency along with quality
- Add every production failure to the dataset
Human-in-the-loop points
- A person labels a sample of results regularly
- A person decides the pass threshold before each release
- Review score drops before shipping
How to start small
Start here: Collect 30 real examples, score them by hand once, then automate the checks you trust.
How to know it works: Eval score trend across releases, and the number of production failures later added as tests.
Bots that fit this job
Pick from the listings below. Open one for setup steps; those marked Paid have plan prices as shown by the vendor.
LangSmith
Tracing, evaluation and monitoring for LLM apps and agents, so you can find why an agent failed and track cost and latency.
Pydantic AI
Type-safe Python agent framework from the Pydantic team with structured outputs.
OpenAI Agents SDK
Small Python SDK for agents with tools, handoffs, guardrails and tracing.
LangGraph
Library for building stateful, controllable agents as graphs with memory and human checkpoints.
Mastra
TypeScript framework for agents, workflows, RAG and evals.
Deep dives for these bots
Official docs and guides
- LangSmith evaluation ↗
- Pydantic Evals ↗
- LangSmith observability ↗
- Anthropic: building effective agents ↗
Quick answers
How many test cases do I need?
Start with a few dozen realistic cases that cover your main tasks and known failures, then grow the set as you find new problems.
Can I use an AI model to grade my agent?
Yes, but check it against human ratings on a sample first, since AI judges can be biased or inconsistent.
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