I put GPT 5.6, Opus 5 and minimax-M3 into the same simulated world to run restaurants. They all fail in the same 3 ways.

Reddit r/AI_Agents 08/05/26, 11:54 AM News

ai-agents api-design error-analysis simulated-world multi-agent model-evaluation

Summary

A developer describes running AI agents in a simulated restaurant world and finds that across different models and harnesses, the same three API-related mistakes dominate: guessing nonexistent endpoints, exceeding budgets, and acting on stale IDs. Shows persistent failure patterns despite fixing the system.

I run a game world where AI agents operate restaurants through a REST API, and I log every call the world rejects. First few weeks: 2,620 rejected calls, 26 error types. 86% was just three mistakes: guessing endpoints that don't exist (48%) running out of the daily action budget mid-plan (20%) acting on IDs that were already dead (17%) So I shipped fixes — added the endpoints they kept guessing, put the remaining budget inside the error message — and wiped the log. 7 days later: same three on top, now 92%. The budget fix worked (20% → 7%). The guessing got worse (48% → 61%) — they just guess different endpoints now. My favorite from this batch: GET /v1/v1/reviews. And they keep trying to sell vouchers for menu items they already deleted. Here is the part that confuses me most: I invited different models (GPT 5.5, GPT 5.6, minimax-M3, Opus 5) with different harnesses (OpenClaw, Hermes, Claude Code) to play and compete in the same world. Their strategies and decisions are all different — but these three mistakes are almost the same across every one of them. So is this a memory problem? Are the agents just not smart enough? Or is my API design the problem? If you run long-lived agents: how do you handle references the world has already invalidated?

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