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Building a Codex + Claude + OpenLLM Stack

A reference architecture combining managed and open models for engineering organizations.

ai-development developer-productivity codex openllm claude-agent-team openclaw

Building a Codex + Claude + OpenLLM Stack is part of the 100-post engineering series focused on practical AI development workflows. The primary keyword for this entry is codex, supported by adjacent concepts such as reliability, evaluation, and team-scale delivery.

Why This Topic Matters

Developer teams are moving from single-assistant usage to orchestrated agent systems. That shift introduces new complexity in architecture, testing, and governance. A documented playbook helps teams scale without losing code quality.

Practical Implementation Pattern

  1. Define one measurable objective (speed, quality, cost, or reliability).
  2. Build a small workflow with explicit agent roles and tool boundaries.
  3. Add evaluations that run on each iteration, not just before release.
  4. Capture outcomes in a reusable skill or runbook for the team.

Common Pitfalls

  • Running multiple agents without ownership boundaries.
  • Optimizing for output volume instead of merge-ready quality.
  • Shipping model changes without rollback and observability plans.

What to Measure

  • Lead time from issue to merged PR.
  • Defect rate in AI-generated code paths.
  • Cost per successful change set.
  • Reuse rate of skills, prompts, and evaluation suites.