Teams run tens, sometimes thousands, of agents with no single view of what any of them did today.
Agents fail quietly because a trace of a wrong outcome looks normal. Put every agent's runs in one record, evaluate each run against the agent's job, and hidden failures become visible verdicts you can act on.
None of these are rare. They are the default state of an agent fleet nobody instrumented.
The first signal that an agent misbehaved is a user or a customer telling you, days after the run. The trace, if one exists, was never read.
Teams ship agents without telling anyone. The count you report and the count actually running are different numbers, and nobody can say by how much.
In your existing logs, an agent updating a record and a person updating a record are the same event. Incident response cannot tell which happened.
When someone asks what an agent accessed, decided, or changed last Tuesday, the answer is a reconstruction from scattered logs, not a lookup.
Not carelessness. Each cause is a reasonable decision that compounds into a fleet nobody can see.
One team builds on LangChain, another on CrewAI, another on a vendor tool with closed internals. Each emits different telemetry to a different place, or none.
Where tracing exists, it records what happened and stops. A trace of a wrong answer looks identical to a trace of a right one, so nobody reads them until something breaks.
The agent that proved useful in a week was never going to wait a quarter for logging standards. Multiply by every team that did the same.
Each agent has an author. The fleet, the thing that fails as a whole, has no owner and no register, so nobody notices the gap widening.
Prefactor watches every run and evaluates the outcome, so a hidden failure becomes a visible verdict.
Instrument without re-architecting. Native SDKs for the frameworks you build on, a TypeScript and Python core SDK for anything else, and OpenTelemetry ingest for closed tools. No gateway in the request path.
Every run, every agent, one record. Actions, tool calls, and decisions land in one queryable place as they happen, tagged to the agent that did them, distinct from human activity.
A verdict on each run, not just a trace. Every run is checked against the agent's job: did it complete the task, at what quality, at what cost. Failures stop hiding inside normal-looking traces.
The unregistered agents show up. Anything emitting runs appears in the fleet view, so the number you report and the number running converge on the same number.
Seeing the failures is the start. The loop keeps them from coming back.
Failures route to a person while they are small. A run that fails its evaluation can be held, escalated, or flagged for review before the pattern reaches more users.
Fix the agent, not the symptom. The record shows which prompt edit, model change, or integration broke the behaviour, so the fix lands in the right place.
"What did it do?" becomes a lookup. Every action and decision per agent is queryable, so the question that used to take days takes minutes, with a record you can hand over.
A team believed it ran a dozen agents. Instrumenting through one record surfaced three times that number, including two agents still acting on a system that had been decommissioned upstream. Neither had failed loudly; both had been failing quietly for weeks. Illustrative, but this is the standard shape of week one.
Instrument the fleet you already run without re-architecting it, and stop learning about failures from other teams.
See the solution →Heads of AIOne portfolio view of every agent: its owner, its quality trend, its cost, whether it is doing its job.
See the solution →Security & governanceAgent actions separated from human actions, with a record you can put in front of an auditor.
See the solution →Book a demo and we will put a fleet like yours in one record: every run watched, evaluated, and answerable.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.