Running a 24/7 AI agent dev team: I route each role to a different LLM (Claude/Kimi/MiniMax/GPT) to dodge a ~$2k/mo API bill. Setup + what actually breaks.

Reddit r/AI_Agents Tools

Summary

The author describes a setup where different AI models are assigned to specific roles (planning, coding, review) to reduce API costs for a 24/7 autonomous engineering team, and shares common failure points like model wandering and hallucinated ownership.

Context: I run an autonomous engineering "org" of AI agents on my own product. Once it grew past \~5 agents and started running around the clock, it maxed my Claude Max weekly limit by mid-week. I priced moving to raw API for the same workload — came out roughly **50x** the subscription cost. For a 24/7 team that's thousands/month to a single vendor. Not happening pre-revenue. So instead of one model doing everything, I route by role — cheapest model that's *good enough* for the job: * **Planning / leadership → Claude Opus.** Big-picture, divergent thinking. Small headcount, so premium tokens are fine here. * **Code implementation → Kimi + MiniMax.** Junior-dev quality, far cheaper, totally fine when the spec is already written. This is where the volume lives, so it's where cost discipline matters most. * **Review / QA → GPT (via codex).** Disciplined, follows the SOP, doesn't go rogue and "improve" things. Last week my reviewer blocked a PR that had dropped an encryption call and was about to persist bot tokens + webhook secrets in plaintext. Dev agent restored it. Caught while I was asleep. **What actually breaks / the annoying parts:** * **Cheap models wander** if the spec is loose. They need tight scope and explicit "do NOT do X" guardrails or they invent work. * **Agents hallucinate ownership.** Had a dev agent report 5 PRs as "done" — 0 were actually its. You need verification loops, not trust. * **Cross-provider orchestration is overhead.** Different runtimes/harnesses, different quirks, separate SOP prompts to maintain per role. * **Routing is a moving target** as prices and model quality shift month to month. Net result: cost stopped scaling linearly with headcount, each layer does what it's actually good at, and no single provider can take the whole operation down (learned that one the hard way after a vendor ban two days before an investor demo — but that's another post). Happy to get into the orchestration setup if useful. *(Disclosure: this runs on my own open-source project — keeping the name out of the post; in a comment if anyone asks.)*
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