They build agents.
We govern them.
Agent platforms help engineers build and deploy agents. Prefactor helps AI leaders govern those agents in production. The crew that builds the building doesn't run it.
| Capability | Agent Platforms | Prefactor |
|---|---|---|
| Agent orchestration (tool use, memory, reasoning) | ✓ | — |
| Framework SDKs & abstractions | ✓ | — |
| Deployment infrastructure | ✓ | — |
| Native monitoring (logs, latency, cost) | ✓ | — |
| Cross-framework governance | — | ✓ |
| Outcome quality assessment | — | ✓ |
| Cost efficiency enforcement | — | ✓ |
| Scope adherence detection | — | ✓ |
| Inline blocking & approval routing | — | ✓ |
| Agent registry & lifecycle governance | Partial | ✓ |
| Immutable audit log | — | ✓ |
Different layers of the stack
Agent platforms solve the build-and-run problem. Prefactor solves the govern-in-production problem. The platform that runs the agent should not be the platform that judges it — separation of concerns is not optional in regulated environments.
IBM watsonx Enterprise AI platform for building, training, and deploying agents at scale. Read comparison → LangChain Open-source framework for building LLM-powered applications and agents. Read comparison → CrewAI Multi-agent orchestration framework for collaborative AI workflows. Read comparison → AutoGPT Autonomous agent platform for building and deploying self-directed AI agents. Read comparison →
Build with any framework. Govern with Prefactor.
A single governance layer across every agent framework, provider, and deployment model in your enterprise.
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