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AI Agents Behavior Versioning and Evaluation in Practice
- Testing and evaluating AI agent behaviors as they move from prototypes to production is crucial but often overlooked.
- Challenges include data contamination, lack of reproducibility, no structured QA feedback, and absence of baseline comparison.
- A better approach involves isolated agent versioning with separate configuration, logs, and quality metrics for each version.
- Setting up multi-agent versioning includes creating separate branches, database schemas, and Azure AI Agent project.
- Instructions in the article detail creating Neon Postgres on Azure, setting up database schemas, connecting to Azure AI Agent project, and setting up the Python project.
- Benefits of this approach include clean testing environments, structured QA, faster iteration, and safe experimentation.
- This workflow is useful for AI/ML developers, QA engineers, and product teams looking to test and ship new agent behavior confidently.
- By separating agent versions and logging structured QA data, teams can make experimentation safe, comparisons measurable, and releases more confident.
- Starting with two branches allows for independent testing of agent changes, with room to expand as the AI agent ecosystem grows.
- Structured evaluation is key for gaining visibility into behavior differences, ensuring safe experimentation, and supporting confident releases.
- The outlined approach helps teams to test variations of prompt-engineered agents, validate agent responses, and ship new agent behaviors confidently.
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