Ship AI Agents Like Software: 5 CI/CD Patterns That Prevent Silent Failures

Your CI/CD pipeline ships code. But an AI agent is not just code. An agent is code + a model + a prompt + tool configurations + retrieval context. Change any one of those and the agent behaves differently. Your pipeline tests one of them. The other four ship untested. This is why teams deploy agents that pass every unit test on Monday and hallucinate in production by Wednesday. The code didn't change. The model did. Here are 5 CI/CD patterns that close the gap between "tests pass" and "agent works." 1. Version the Full Agent Stack, Not Just the Code Traditional CI/CD versions code with git. That covers about 20% of what determines an AI agent's behavior. An agent's output depends on: Code (orchestration logic, tool definitions) Model (provider, model ID, temperature, max tokens) Prompt (system prompt, few-shot examples) Tools (API endpoints, schemas, auth) Context (retrieval pipeline, vector store version) Change the model from claude-3-5-sonnet-20241022 to claude-3-5-sonnet-20250514 a