Runtime Circuit Breakers for Production AI Agents
Limit runaway AI agent retries, recursive tool failures, and budget overruns with distributed runtime circuit breakers that automatically halt configured execution paths when thresholds are crossed.
How Distributed Circuit Breakers Protect Production
Passive logging alerts you only after an incident costs thousands of dollars. Observyze circuit breakers intercept failing execution loops at runtime:
CLOSED (Healthy)
All requests pass through normally. Telemetry monitors latency percentiles, error rates, and step costs continuously.
OPEN (Tripped)
Threshold crossed. Requests are rejected immediately without calling upstream LLM APIs, preventing runaway spend.
HALF-OPEN (Probe)
After cooldown, a limited sample of requests is evaluated. If successful, the circuit resets to CLOSED; otherwise it trips back to OPEN.
Triggers & Protection Rules
Configure circuit breaker rules per project or per agent deployment to match your operational tolerances:
Trip the circuit when an agent encounters 3 consecutive tool parse or timeout errors, preventing infinite self-correction loops.
Halt active execution paths when a single trace crosses configured dollar limits (e.g. $0.50 per session) or turn count caps.
Isolate upstream 500/503 provider outages and route traffic to configured fallback models (e.g. Claude to OpenAI or Ollama).
Protect downstream user experiences by tripping when upstream response times exceed p99 thresholds over rolling evaluation windows.
Frequently Asked Questions
Technical details, integration patterns, and operational controls.
An AI agent circuit breaker is a runtime protection mechanism that monitors error rates, consecutive tool failures, latency spikes, or spend thresholds on configured agent request paths. When safety thresholds are crossed, the circuit breaker automatically trips to prevent runaway execution loops and unnecessary API spend.
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Want to test without installing anything?
Explore 6 pre-configured production scenarios in our interactive Demo Mode.