Eval-Driven Development: A Software Engineering Approach to Production-Grade AI Agents
Last Updated on August 19, 2026 by Editorial Team
Author(s): Alex Punnen
Originally published on Towards AI.
How to turn production traces into a continuous improvement loop using Pydantic AI, OpenTelemetry, Grafana Tempo, SeaweedFS, and LLM-as-a-judge evaluation.
Repo: https://github.com/alexcpn/eval-driven-development
The article argues that production-grade agentic AI requires foundational software engineering practices—especially continuous evaluation and guardrails—rather than relying on prompts or frameworks alone. It presents an “Eval-Driven Development” workflow that mirrors CI/CD for agents: decouple prompts via a versioned contract, run the agent runtime that executes the contract, capture observability data through tracing/logging with OpenTelemetry and tools like Grafana Tempo/Loki, and then run both deterministic evaluations (rule-based checks, regression on traces) and non-deterministic evaluations (LLM-as-a-judge with rubrics) to detect regressions. It details how to structure evaluation layers, including live deterministic test runs, regression suites that re-evaluate stored traces, and judge-based scoring with example failure cases. Finally, it explains how to turn this pattern into an evaluation-informed release pipeline that scores agent releases across multiple axes such as task success, semantic quality, tool correctness, safety/compliance, cost, latency, and reliability.
Read the full blog for free on Medium.
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