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Hexagram Circuit Breaker v3 icon

Hexagram Circuit Breaker v3

Business Operations Updated 2026.08.30

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About this skill

Problem It Addresses

In intelligent quotation workflows, a single request may pass through rule calculation, LLM judgment, external data sources, and downstream services. The risk is not only hard failure, but failure that looks plausible: timeouts, repeated errors, or elevated error rates that feed into LLM reasoning can produce unstable quotations, duplicate calls, or weak fallbacks. hexagram-circuit-v3 addresses this boundary by making the system explicitly degrade, open, and audit itself when the rule layer or model layer becomes unreliable.

How It Works

The skill organizes circuit breaking into three stages: rule detectionLLM judgmentmanual takeover. Instead of simply returning an error, it changes system behavior when thresholds are crossed:
- Error rate > 30%: enter a half-open state and limit further traffic.
- More than 3 consecutive failures: enter a fully open state and block subsequent calls while preserving recovery probes.
- Timeout > 5 seconds: trigger automatic degradation to a more conservative handling path.

It also provides complete audit logging and automatic recovery detection, helping operators answer when the circuit opened, what evidence triggered it, and when retries become safe. The documentation states an “add-only, no modification” principle, making it suitable as a protective layer over existing quotation logic rather than a replacement for business rules.

Boundaries And Caveats

This skill is most useful when the upstream chain has observable steps, clear failure signals, and structured validation around LLM output. If upstream fields are missing, error codes are inconsistent, or model responses are unstructured, circuit breaking can contain failure spread but cannot fix the underlying defect. Defaults such as 30%, 3 failures, and 5 seconds should be recalibrated against interface SLAs, traffic peaks, and manual takeover capacity.

Use Cases

  • Before shipping a quotation API, wire error-rate, consecutive-failure, and timeout thresholds into the request path, then verify half-open and open states before degradation.
  • When LLM quotation judgments fail repeatedly, activate rule detection → LLM judgment → manual takeover to keep abnormal quotes out of business results.
  • Audit quotation service logs to determine whether timeout, repeated failures, or elevated error rate triggered the circuit breaker, and check whether recovery detection allows traffic.
  • When external data sources become slow, configure the 5-second timeout degradation path so requests fall back to a conservative strategy while preserving retry evidence.

Best For

  • Backend engineers owning quotation pipeline reliability: who want failure thresholds mapped to explicit breaking, degradation, and recovery states.
  • ML engineers validating LLM outputs: who want abnormal model results from directly contaminating quotation conclusions.
  • Business system leads focused on incident auditing: who need trigger thresholds, request states, and manual takeover records during reviews.
  • SREs maintaining intelligent quotation services: who want abnormal traffic contained during peaks and recovery detection verified.