Observability watches.
We act.
Observability platforms record what agents did — sold to engineers. Prefactor controls what agents are allowed to do — sold to AI leaders. Cameras don't stop bad plays.
| Capability | Observability Platforms | Prefactor |
|---|---|---|
| Session tracing & replay | ✓ | — |
| Metrics & dashboards (latency, tokens, errors) | ✓ | — |
| Anomaly detection & alerts | ✓ | — |
| Outcome quality assessment | — | ✓ |
| Cost efficiency enforcement | — | ✓ |
| Scope adherence detection | — | ✓ |
| Inline blocking & throttling | — | ✓ |
| Approval workflows (human-in-the-loop) | — | ✓ |
| Immutable audit log | — | ✓ |
Read-only vs read-write
Observability platforms record what agents did — every trace, token, and tool call. Governance takes those signals and applies controls: blocking actions that exceed cost thresholds, routing sensitive decisions to human approvers, and throttling agents that drift outside scope. Visibility without enforcement is not governance.
AgentOps Session replay and observability for AI agent development. Read comparison → Fiddler AI ML model monitoring, explainability, and performance tracking. Read comparison → Langfuse Open-source LLM observability with tracing and analytics. Read comparison → LangSmith LangChain's debugging and monitoring platform for LLM apps. Read comparison →
Visibility is the starting point, not the finish line
If you already have observability in place, Prefactor adds the governance layer — assessment, enforcement, and audit-grade controls for production agents.
Book a demo View all comparisons