AgentOps replays the run. Prefactor knows if the agent did its job.
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]
What is the difference between AgentOps and Prefactor?
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.
AgentOps or Prefactor, in one table
| 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 |
What each one is for
- Session tracking: each agent run recorded call by call, so you can inspect the parameters and responses after the fact.
- Cost analytics: token-level tracking and cost breakdowns per agent and per run, so you can see where spend goes.
- Agent replay: step through a run to see what the agent did and where it went wrong.
- Error tracking: detection and grouping of errors and exceptions across runs.
- Framework coverage: works with major agent frameworks and model providers for consistent monitoring.
Best for development teams and ML engineers who need to debug agent runs and understand cost drivers.
- Evaluates each run for outcome quality, cost, and whether the agent stayed in its approved scope.
- A quality score per agent tracked across versions, so a regression shows up as a trend rather than a surprise.
- Drift detection when behaviour shifts after a model update or a prompt edit, before a user hits it.
- Holds or escalates a risky action for review before it reaches a user, not after the cost is spent.
- One record across frameworks: agents monitored elsewhere, CrewAI, and custom agents evaluated from the same place.
Best for teams running agents in production who need to know each one is doing its job, and prove it.
Side by side, by lifecycle stage
| 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 | ✓ |
Where AgentOps stops: whether the agent did its job
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.
Which one fits
Stay with AgentOps alone if
- You are debugging agent runs and tuning prompts.
- Session replay and cost breakdowns cover what you need today.
- Your agents are not yet taking actions for real users.
Add Prefactor when
- Agents are doing real work for real users.
- You need a quality score per agent, not just a session record.
- A regression after a prompt or model change has to surface before a user hits it.
- Someone asks you to prove an agent behaved.
How we reviewed this comparison
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.
Sources reviewed
Prefactor contextMethodology
- Reviewed public product, documentation, and launch material visible at the time of writing.
- Mapped each page to the primary buyer, control layer, and runtime capabilities each vendor describes publicly.
- Prefer direct product and documentation pages over analyst summaries or reseller material.
Does Prefactor replace AgentOps?
Does Prefactor work with agents AgentOps already monitors?
What does AgentOps do that Prefactor does not?
Do I still need evaluation if I have monitoring?
Can AgentOps and Prefactor run together?
Related
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