What is Circuit Breaker?
Governance & ControlAn automatic halt that trips on a threshold: error rate, spend, loop count. Borrowed from distributed systems, it is the pattern that stops a $380 conversation and a $47K delegation loop without waiting for a human to notice.
Why It Matters
Agents fail at machine speed, which means the cost of a runaway is set by how long it runs unattended. A LangChain multi-agent system ran an analyzer and a verifier in a mutual delegation loop for eleven days and produced a $47,000 API bill: observability recorded every call, and nothing was configured to stop it. A single-agent version produced a $380 conversation from a retry loop nobody was watching. The circuit breaker is the pattern that ends both without waiting for a human to notice: a threshold trips, the run halts.
What It Trips On
Error rate. Consecutive failed calls or a failure ratio past the threshold: the agent is not making progress, and letting it continue converts a bug into a bill.
Spend. Cost per run, per agent, or per tenant crossing a bound. This is the trip that would have ended the eleven-day loop on day one.
Loop count. Iterations or mutual handoffs past a limit. Delegation loops and retry storms both surface here before the invoice does.
The borrowed name is accurate: like the distributed-systems original, the breaker trades a small number of false halts for never absorbing the unbounded failure.
Where It Breaks
The usual failure is configuring alerts where breakers belong. An alert that fires on the $47K loop still lets the loop run while the notification ages unread; a breaker ends the run and lets the alert explain why. The second failure is tripping without a safe stop: a breaker that kills the process mid-transaction leaves partial state standing, which is why the breaker and the kill switch ship as one mechanism, threshold plus rollback.
How Flytebit Handles It
Circuit breakers in the systems we ship trip on error, spend, and loop thresholds and hand the halt to the safe-stop machinery, so the run ends in a consistent state rather than a killed one. Thresholds are tuned per agent and per environment as part of the deployment manifest. The runaway-cost cases are dissected in Governing Agentic AI, and the engagement version is our AI governance and risk work.