When an agent misbehaves in production, most teams can watch the dashboard or shut everything down, with nothing in between.
Dashboards report; they do not stop anything. Define an activity schema per agent, and a breaching run can be held, escalated or blocked mid-run, one agent at a time, while the rest of the fleet keeps working.
The failure is not that agents misbehave. It is that when one does, nothing short of a full shutdown can reach it.
You learn about the bad action after it completed. The chart that shows the spike has no lever attached to it, because observability tools are read-only by design.
The agent ships with a prompt asking it to behave and nothing that enforces it on a live run. Teams describe their own setup as trust and hope.
Stopping one misbehaving agent means taking down the deployment it shares with everything else. The blast radius of intervening is so large that nobody pulls the lever.
A document says what each agent may do. No mechanism checks a live run against it, so the policy constrains the authors and never the agent.
Stopping a live agent was never designed in. Each layer of the stack assumed someone else would do it.
Tracing tools record what happened and deliberately stay out of the request path. Intervention was out of scope on day one, so no amount of dashboards adds a brake.
What the agent may do exists as a policy document and a prompt, not as a schema a machine can check. There is nothing to evaluate a live run against.
Agents ship on infrastructure where you can stop a service or nothing. A single run, or a single agent among many, is not a thing the platform can halt.
Even when a person spots a bad run live, there is no route from the alert to someone with the authority to pause it before the action completes. By the time the meeting happens, the run has finished.
Prefactor evaluates every run against the agent's activity schema as it happens, so a breach becomes a signal with authority attached, not a line in next week's report.
Instrument the agents you already run. Native SDKs for common frameworks, a TypeScript and Python core SDK for anything custom, and OpenTelemetry ingest for closed tools. Actions land in the record as they happen.
An activity schema per agent. The actions, data, and limits its job allows, written as a schema a machine can check, not a paragraph a person once read. The paper policy becomes an enforceable one.
Every run checked against its schema, live. Each action is evaluated as the run progresses. An agent inside its schema is never interrupted; an action outside it is caught before it completes, not counted after.
Breaches arrive as decisions, not charts. A breach reaches a person with options attached: hold, escalate, or block. The threshold that triggers it is yours to set per agent.
A check with no authority is a dashboard. These are the three things a breach can actually do.
Stop one agent, not the fleet. A breaching action can be held for review, escalated to a person, or blocked before it completes, and there is a kill switch that halts a single agent while the rest keep running.
Tune the schema against reality. Every intervention shows which schema line the run breached. A false hold tightens the schema; a real breach fixes the agent, and the next runs prove which it was.
Every intervention lands in the record. Who held what, when, and why is a lookup, so the answer to "what happened and who stopped it" comes from the record, not from memory in a post-incident meeting.
After a prompt change, an invoicing agent started approving credits above its limit. The schema check held the third credit mid-run and escalated to the owning engineer, who paused that one agent while the rest of the fleet kept working. The fix shipped the same afternoon, and the two held credits were released by a person, not by the agent. Illustrative, but this is what an intervention is for.
Intervene on a single agent without a fleet-wide shutdown, and stop treating "turn it all off" as your incident plan.
See the solution → Product leadersThe agent in front of your customers can be held mid-run before a bad action lands, without taking the product down to do it.
See the solution → Security & governanceThe policy document becomes an enforced schema on every live run, with each intervention recorded for the auditor.
See the solution →Book a demo and bring your riskiest agent: define its activity schema, watch a breach get held mid-run, and stop one agent while the rest keep running.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.