Your LLM Shouldn't Be Your Coding-Agent Workflow: Separation of Concerns in OpenClaw

If your coding-agent workflow grinds to a halt the moment you hit your LLM usage limit, your architecture has a problem: the LLM is doing too much. A practical rule from the OpenClaw community: the model should reason about the work, but it shouldn't be the workflow itself.

Key Takeaways

• Separate orchestration from judgment: Queues, state management, retries, scheduling, verification, receipts, and recovery can all run deterministically—without LLM involvement.
• Call the LLM only when judgment is required: Focus model invocations on tasks that genuinely need reasoning, not on routine control flow.
• Make your loop infrastructure, not prompting: This separation turns an agent loop from "keep prompting it" into something that can actually operate reliably.

Why This Matters

When the LLM is embedded in every step of your workflow, a usage limit becomes a hard stop. You're blocked not because the work is done, but because the orchestrator can't think without its brain. By moving the deterministic parts—state tracks, retry logic, scheduling, verification checks—into plain code, the system continues to function even when the LLM is unavailable.

The result is a coding-agent loop that behaves like infrastructure: it recovers, retries, and verifies on its own. You only spend LLM tokens (and hit limits) when the job actually requires reasoning.

Who This Is For

Developers building or extending coding agents (like those using OpenClaw) who want to build resilient, production-grade automation rather than fragile prompt chains.