Failproof AI
End-to-end reliability for AI agents. Traces every run at the agent runtime, finds where agents fail on its own, and lets you author and enforce a policy that stops the bad action in realtime - deployed on-prem or in the cloud.
DBOS
Durable workflow orchestration built into your code - lightweight durable execution with Postgres-backed state, as a library rather than an external orchestrator.
| capability | Failproof AI | DBOS |
|---|---|---|
| Built for | AI agents | Durable workflows |
| What it solves | Agent reliability (semantic) | Crash recovery (infra) |
| Agent-level tracing | Agent runtime, deeper | Workflow steps |
| Autonomous failure finding | Automatic | No |
| Realtime policy enforcement | Yes, realtime | No |
| Durable execution & retries | No | Yes - core |
| Tells a wrong action from a right one | Yes | No |
| Deployment | Local, on-prem, cloud | Self-host (library) / cloud |
Choose Failproof AI when
- you run high-impact autonomous agents that take real actions
- a wrong action is costly or irreversible
- you need to stop failures at runtime, not just trace them
- you want an end-to-end reliability solution, on-prem or cloud
Choose DBOS when
- you want durable execution as a lightweight in-code library
- you are already on Postgres and want simple durable workflows
- your problem is process durability, not agent decisions
FAQ
Is DBOS an alternative to Failproof AI?
No - complementary. DBOS makes the workflow durable; Failproof AI makes the agent decisions inside it reliable. DBOS recovers and retries a step, but cannot tell a wrong action from a right one.
Does DBOS make AI agents reliable?
It makes them durable, not correct. Failproof AI adds the semantic reliability layer - tracing, failure finding, and enforcement on agent actions.
Can I use both?
Yes - DBOS for durable execution, Failproof AI for agent reliability and enforcement inside the workflow.