Agents consuming bounded resources (rate limits, connection pools, memory) and degrading service.
A practical guide to agent resource exhaustion — what it is, what causes it, how to stop it before it ships harm, and how to catch it when prevention fails.
What it actually looks like in production
- Burst from a single tenant exhausted the OpenAI rate limit
- Long-running agents starved a connection pool
- Memory growth from unbounded conversation history
Why it happens
- No per-tenant rate limits
- No connection pooling configuration
- Unbounded context history
- No backpressure handling
How to prevent it (vendor-neutral)
1. Per-tenant rate limits
2. Connection pool sizing
3. Context history caps
4. Backpressure with graceful degradation
How Prefactor helps detect and prevent it
Prefactor sits at the agent runtime and contributes specifically:
- Runtime guardrails that flag or block matching patterns before they land
- Continuous eval suites that catch quality regressions on every change
- Tamper-evident logs of every incident and response action
- Per-agent anomaly alerts on the signals listed below
Detection — what to monitor
- Rate-limit errors clustering
- Pool exhaustion alerts
- Memory growth trends
Response — what to do when it happens
Immediate (minutes): confirm the incident from the trace; pause the affected agent if active harm possible; hotfix the trigger.
Short-term (hours): add the failure case to the eval suite; patch the root cause; redeploy with regression validation.
Medium-term (days): root cause analysis; tighten guardrails or controls; document the incident for post-mortem and audit.
FAQ
Can agent resource exhaustion be eliminated entirely? Usually no — reduce frequency and severity dramatically, and contain blast radius. Aim for low, detected, and contained.
How often should we test for this? Continuously, with every change. Every reported incident becomes a test case.
Can Prefactor detect this in real time? Yes for many variants — guardrails run in-line with sub-second latency.
Related
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