One solution. Across every layer of the enterprise.

Self gives every team the context it needs to decide what an agent should be allowed to do next.

For AI Leaders

Move from pilots to autonomous production.

"We can finally tell which agents are ready to run without us."

  • Autonomy rate per agent and action class
  • Deployment confidence backed by replay
  • Proof of ROI in supervisor hours
  • Fleet performance in one view
For Security

Let agents move faster without unlimited power.

"Every agent operates inside limits we set, not limits we hope for."

  • Authority bounded per action class
  • Hard constraints Self cannot exceed
  • Automatic contraction on bad outcomes
  • Kill switch and full action history
For Engineering

Stop rebuilding approval logic for every agent.

"One integration handles every approval path we used to build by hand."

  • One authorize call before the tool
  • Policy as code, versioned with releases
  • Replay before rollout
  • SDKs for Python, TypeScript, Go
For Operations

Put humans on exceptions, not routine approvals.

"My team only sees the decisions that actually need a person."

  • Review queue ordered by consequence
  • Throughput per supervisor
  • Cost per completed action
  • 18,200 hours returned per quarter
For Risk and Compliance

Know why every autonomous action was permitted.

"Every permitted action comes with the reason it was allowed."

  • Evidence behind every grant
  • Policy and agent version per verdict
  • Complete override history
  • Auditable from action back to decision
For Executives

Turn AI spending into AI labor.

"Agent spend finally shows up as recovered labor, not just a bigger bill."

  • Autonomy rate
  • Human hours removed
  • Economic value recovered