Observability shows you the trace. Prefactor tells you what to fix.
You trace with one and evaluate with the other, so they are not alternatives: Prefactor reads the traces you already collect and judges each outcome against the agent's job.
Observability tools record what an agent did: spans, tokens, latency, dashboards. Prefactor reads those same traces and adds a verdict: a quality score per run, drift detection, and cost per agent. Keep your tracing, then add Prefactor when you need to know each outcome was right.
Observability tools or Prefactor, in one table
| Decision factor | Observability tools | Prefactor |
|---|---|---|
| Where it fits | Recording what the agent did | Knowing whether the outcome was right |
| Primary question | What happened on this run | Was the outcome right, and at what cost |
| Unit of analysis | The trace and its metrics | A quality score per run and per agent |
| Change over time | Dashboards and alerts on metrics | Drift detection when behaviour shifts after a change |
| How it attaches | SDK or OpenTelemetry instrumentation | Native SDK, core SDK, or OpenTelemetry ingest, no rebuild |
| Use them together? | Trace with observability | Evaluate with Prefactor |
What each one is for
- Tracing: capture each step of a run, prompts, tool calls, tokens, and latency, so you can replay what happened.
- Metrics and dashboards: latency, token counts, and error rates charted over time.
- Anomaly alerts: flag a spike in errors or latency and page the on-call engineer.
- Debugging: drill into a single trace to find where a run went wrong.
- Wide instrumentation: SDKs and OpenTelemetry support across common agent frameworks.
Best for engineers debugging agent behaviour and watching operational metrics during development and in production.
- Scores 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 one-off trace.
- 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.
- One record across frameworks: LangChain, CrewAI, and custom agents scored from the same place, with an audit trail per decision.
Best for teams running agents in production who need to know each one is doing its job, and show the evidence.
Side by side, tracing against evaluation
| Capability | Observability tools | Prefactor |
|---|---|---|
| Recording the run | ||
| Span and trace capture | ✓ | Reads your traces |
| Latency, token, and error metrics | ✓ | For cost, per agent |
| Dashboards and anomaly alerts | ✓ | — |
| Evaluating the outcome | ||
| Quality score per run | — | ✓ |
| Quality score tracked per agent and version | — | ✓ |
| Drift detection against a baseline | Metric anomalies only | ✓ |
| Cost attributed per agent and version | Per trace, build it yourself | ✓ |
| Acting and recording | ||
| Hold or escalate a risky action | — | ✓ |
| One queryable record per agent | — | ✓ |
| Scores agents across frameworks from one place | Traces only | ✓ |
| Audit trail for a decision | Partial | ✓ |
The tools in this space
Where observability stops: whether the agent did its job
We sell the layer this section describes. Read it with that in mind.
Observability answers what an agent did on a given run: the spans, the tool calls, the tokens, the latency. None of that says whether the agent is doing its job, at acceptable quality and cost, with evidence you can show.
A trace of a wrong answer and a trace of a correct one look the same: same steps, same latency, same token counts. Prefactor puts a verdict on each run.
The quality score is tracked per agent and version, and a shift in behaviour after a change is flagged before a user hits it.
Each run and each decision lands in a record you can hand to a customer or an auditor.
Prefactor reads the traces you already emit, through a native SDK or any OpenTelemetry source, so you keep what you built.
Does Prefactor replace my observability tool?
Is a quality score not just another metric?
How does Prefactor attach to agents I already trace?
Do I still need evaluation if I have tracing and alerts?
Does Prefactor work across different agent frameworks?
Improve the agents you already trace
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.
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