A session tells you what happened; the evaluation tells you whether it was right, so you debug with one and judge with the other.[1][2]
AgentOps records what an agent did: sessions, token cost, and replay for debugging. Prefactor judges whether each production run met the agent's goal, tracks quality per version, and can hold a risky action. Debug with AgentOps, then add Prefactor once agents act for real users.
| Decision factor | AgentOps | Prefactor |
|---|---|---|
| Where it fits | Recording what the agent did | Knowing whether the agent did its job |
| Lifecycle stage | Debugging and monitoring | Production evaluation |
| Core output | Sessions, traces, and cost breakdowns | A quality score per run, tracked per agent |
| On a bad run | Replays the steps so you can inspect them | Judges it, flags drift, and can hold the action |
| Framework scope | Monitors agents across major frameworks | Evaluates agents from any framework |
| Use them together? | Debug and track cost with AgentOps | Evaluate outcomes with Prefactor |
Best for development teams and ML engineers who need to debug agent runs and understand cost drivers.
Best for teams running agents in production who need to know each one is doing its job, and prove it.
| Capability | AgentOps | Prefactor |
|---|---|---|
| Recording and debugging | ||
| Session and run tracking | ✓ | — |
| Token cost per agent and run | ✓ | ✓ |
| Agent replay for debugging | ✓ | — |
| Error tracking and grouping | ✓ | — |
| Evaluating agents in production | ||
| Quality score per run | — | ✓ |
| Drift detection against a baseline | — | ✓ |
| Outcome judged, not just recorded | Records the run | ✓ |
| Hold or escalate a risky action | — | ✓ |
| Across your stack | ||
| Evaluates agents on any framework | Monitors many frameworks | ✓ |
| One queryable record per agent | — | ✓ |
| Audit trail for a decision | Partial | ✓ |
We sell the layer this section describes. Read it with that in mind.
AgentOps answers what an agent did on a run: the calls it made, the tokens it burned, the point where it failed. It does not say whether the agent is doing its job, at acceptable quality and cost, with evidence to show.
A session of a wrong answer and a session of a correct one look the same: same steps, same latency, same token counts. Prefactor weighs each run against whether the agent met its goal.
When behaviour shifts after a prompt edit or a model update, the sessions keep recording as before. Prefactor tracks quality per agent across versions and flags the change before a user hits it.
A session records the action after it has run. Prefactor holds or escalates it for review before it reaches a user.
A replay helps you debug; it is not evidence that an agent behaved, decision by decision. Prefactor keeps one queryable record per agent.
Reviewed against public product and documentation pages on March 19, 2026. If a vendor has changed a feature, product name, or positioning since then, send a correction and we will update it. Numbered source links in the page body point to the ordered sources below.
Book a demo and we will evaluate a live agent on a fleet like yours: quality per run, drift after a change, and cost per agent.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.