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title: 7 Regression Tests Every AI Agent Should Pass Before Deploy
description: A practical checklist of seven regression tests for validating AI agent orchestration layers before production deployment: context loss and retrieval...
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# 7 Regression Tests Every AI Agent Should Pass Before Deploy

**[Machine Learning Mastery](https://daily.dev/sources/mlm)** · 8 min read · 1 upvotes · 0 comments

## Summary

A practical checklist of seven regression tests for validating AI agent orchestration layers before production deployment: context loss and retrieval degradation, tool execution idempotency, prompt injection resistance, structured output adherence, non-termination handling, RAG grounding against parametric knowledge, and state rehydration consistency. Each test is designed to return a binary pass/fail suitable for CI/CD gating, with guidance on common misconfigurations (like flaky assertions, OR-assertion traps, and improper idempotency key derivation). The piece also outlines what these tests won't catch, such as cost/latency regression, tool-contract drift, PII leakage, and embedding space skew.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://machinelearningmastery.com/7-regression-tests-every-ai-agent-should-pass-before-deploy>

## Questions this post answers

### How do I test whether my AI agent's tool calls are idempotent before deploying to production?

Force the same tool-call payload to hit the execution boundary three times and check that the downstream system registers exactly one write, returning a cache-hit response for the duplicate attempts. Derive idempotency keys from the logical identity of the operation - a hash of tool name, canonicalized arguments, and a business correlation ID - never from step ID or message position, since those change on every loop iteration and defeat the key entirely.

_Teams shipping tool-using agents track idempotency and orchestration practices like these on daily.dev._

### What's the difference between deadlock and livelock in AI agent orchestration?

Livelock is when an agent keeps cycling through thought-action-observation steps but never advances toward its goal, while true deadlock is when two agents are mutually blocked waiting on each other's approval, a failure specific to multi-agent systems. To catch non-termination, set a hardcoded budget as a triple of maximum steps, maximum cumulative token cost, and wall-clock timeout, since a step count alone misses a single step that hangs indefinitely.

_Developers debugging stuck agent loops can follow orchestration and reliability patterns on daily.dev._

### How do I test RAG grounding to make sure my agent doesn't blindly trust retrieved documents?

Introduce a synthetic fact into the retrieval pipeline that contradicts common knowledge and query the agent on that topic, checking both directions: the agent should adopt a correct synthetic fact over stale training data, but resist an obviously wrong retrieved fact when the contradiction is detectable. Testing only the first direction misses retrieval poisoning risk, since an agent tuned to always defer to context becomes an easy vector for injected false information.

_Anyone hardening RAG pipelines against poisoning can keep up with grounding techniques via daily.dev._

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---

Tags: [#testing](https://daily.dev/tags/testing), [#ai-agents](https://daily.dev/tags/ai-agents), [#cicd](https://daily.dev/tags/cicd), [#rag](https://daily.dev/tags/rag), [#prompt-injection](https://daily.dev/tags/prompt-injection)

[View this post on daily.dev](https://daily.dev/posts/7-regression-tests-every-ai-agent-should-pass-before-deploy-jj0zpdeuq)

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