OpenAI’s new throttling hit my agent – here’s how I traced the

OpenAI silently tightened its API throttling policy, rolling out rate-limit version 2.1 that blocks automated agents even with delays between calls, according to a developer who traced the change. The developer found that heavy prompts with large context and chain-of-thought instructions triggered a 'policy_ratelimit' error, while lightweight calls succeeded, and the fix required adding a 5-second sleep and lowering temperature to 0.2, cutting throughput by 40%. The developer linked the policy shift to OpenAI's reported negotiations with the White House about slowing AI deployment.

OpenAI’s new throttling hit my agent – here’s how I traced the { "error": { "message": "Your request was flagged by automated-use detection. Insert a minimum 3-second delay between calls or your key may be suspended.", "type": "policy ratelimit", "param": null, "code": "policy ratelimit" } } I’d been running the same Claude Code /en/tags/claude%20code/ pipeline for weeks—batch-reviewing PR diffs via the API—without ever touching the rate limit. Suddenly, even requests spaced eight seconds apart got slapped with that policy code. Something changed server-side. Stepped through the diagnosis: 1. Captured a working session from the old day – identical prompt structure, same key, no error. That ruled out a code regression in my queue manager. 2. Compared request headers – noticed a new X-Rate-Policy-Version: 2.1 appearing in the failing responses. Previous calls had 1.9 . This suggested a policy rollout, not a quota issue. 3. Tested a single, lightweight call – a simple complete with no system prompt. It went through fine. The rejection only triggered on my heavy code-review prompts large context, chain-of-thought instructions . 4. Checked the official status page – no incident listed. So this was a silent policy tightening, probably tied to what the press is now calling “OpenAI talking to the White House about slowing down AI.” The fix for now: I added a uniform 5-second sleep and slashed the temperature to 0.2 to reduce token variance their heuristics seem to penalise high-entropy outputs originating from automated loops . That cut throughput by 40% but the pipeline runs. This isn’t a bug in the conventional sense—it’s a deliberate limit imposed because they’re negotiating with regulators. The real-world deployment impact is that any LLM agent running unattended can trip this wall unexpectedly. Has anyone else seen similar header changes X-Rate-Policy-Version or worked around the delay requirement without losing too much speed? I’m experimenting with batching multiple reviews into one Next Why Adam Optimizer Fails in RL and Transformers → /en/threads/4316/