Playground in Prod - Optimising Agents in Production Environments#
AI Engineer Europe 2026 digest Program Adjacent pick 8–10 April 2026
Samuel Colvin — Pydantic AI / Logfire (per abstract)
- Why: Agents in production need iteration cadences far faster than CI/CD allows — changing a prompt or model setting should not require a redeploy. This applies the feature-flag/progressive-delivery pattern to agent parameters, then automates the loop.
- What: Managed Variables — a Pydantic AI feature built on the OpenFeature standard that externalizes system prompts, model configuration, tool descriptions, and thresholds for instant update from the Logfire UI with no redeploy — plus GEPA (Genetic-Pareto reflective text evolution) wired into Logfire traces to form a closed loop: observe performance, reflect on failures, evolve prompts, push live without human intervention.
- How: Agent parameters behind an OpenFeature-style flag interface; production traces and evals serve as the fitness signal for reflective prompt evolution, with guardrails, rollback, and A/B between prompt variants.
- Where applicable: The tooling is Pydantic AI/Logfire-specific, but the pattern — flags for prompts plus automated prompt optimization driven by production telemetry — transfers to any agent platform. Workshop format with live demos.