CI/CD for Agents: Automating Quality

In traditional software, your build pipeline runs unit tests. In Agentic AI, your pipeline must also run Benchmarks (Module 12). If you change one word in a prompt, you must be 100% sure it doesn't break the agent's ability to use a tool correctly.

1. The Agentic Build Pipeline

1. Commit: Developer updates system_prompt.txt.
2. Lint: Check code for syntax errors.
3. Unit Test: Test the "Tools" (Python functions) in isolation.
4. Evals: Run the agent against the Golden Dataset (50 test cases).
5. Benchmarking: Compare accuracy score of v2 vs v1.
6. Deploy: If score >= v1, push to production.

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2. Prompt Versioning

Prompts should never be hard-coded in Python. They should be treated as Artifacts.

• Store prompts in a prompts/ directory.
• Use a tool like Weights & Biases or LangSmith to version-tag every prompt.
• Pro Tip: Give every prompt a semantic version (e.g., researcher_v1.2.3).

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3. Visualizing the Deployment Flow

graph TD
    Code[Git Commit] --> CI[GitHub Actions / Jenkins]
    CI --> Unit[Tool Unit Tests]
    Unit --> Eval[Agent Eval Suite]
    Eval --> Check{Accuracy > Threshold?}
    Check -- No --> Fail[Alert Developer]
    Check -- Yes --> Prod[Deploy as 'Challenger']
    Prod --> Monitoring[Monitor for 24h]

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4. Environment Parity

Your Dev environment should use the same Vector DB structure as Prod.

• If Dev uses ChromaDB and Prod uses Pinecone, your "Similarity Scores" will be different, and your agents will behave differently.
• The Rule: Always use the same types of infrastructure for testing that you use for live traffic.

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5. Security Scanning (Secret Detection)

Agent developers often accidentally put API keys in their prompts or tool code. Your CI/CD pipeline must include a Secret Scanner (like git-secrets) to ensure you never leak your OpenAI or Pinecone keys to GitHub.

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Key Takeaways

• Prompt changes require the same testing rigor as code changes.
• Automated Evals are the only way to prevent regression in agent quality.
• Versioning must include the code, the prompt, and the model ID.
• Shadow deployments (Challenger) are safer than "Big Bang" updates.