Self provides competence infrastructure for AI agents, connecting agent actions, human judgment, and real-world outcomes to demonstrate where agents have proven capability and where they need greater oversight. The platform uses Success Contracts to define acceptable work, retains evidence from reviews and corrections, and enables organizations to make data-driven decisions about agent autonomy and delegation boundaries.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations deploying AI agents struggle to know when autonomous work is truly reliable versus when human reviewers are quietly supplying the critical judgment. Without a systematic way to track which tasks an agent can independently complete, companies cannot make informed decisions about expanding or restricting agent authority, risking either costly failures from over-delegation or wasted capacity from excessive oversight.
Solution
Self provides a competence infrastructure that connects agent actions, human corrections, and real-world outcomes, creating a persistent evidence trail for every piece of delegated work. The platform uses Success Contracts to define what acceptable work means for each customer, then captures the full workflow—including where human intervention was needed and whether the ultimate business outcome was achieved. This allows organizations to compare agent performance against the same requirements across different configurations, models, or conditions, and make grounded decisions about expanding or restricting agent authority. Self turns every review and correction into a retained lesson that informs future assessments, so institutional knowledge about agent capability accumulates rather than resetting with each deployment.
Target Audience
Primary users are engineering teams and operating teams deploying AI agents in enterprise workflows, particularly those handling consequential actions like refunds, routing, or other policy-bound tasks that require human oversight and clear delegation boundaries.
Features
- Success Contracts that define customer-specific requirements for acceptable work, with criteria linked to evidence sources
- Evidence capture that connects agent behavior (E-01), human judgment (H-01), and business outcomes (O-01) with stable source IDs
- Example readout system that evaluates each work case against contract criteria, showing verdicts like "Satisfied with human intervention" or "Not demonstrated by the agent"
- Regression case drafting that converts historical corrections into reusable test cases for comparing models or configuration changes
- Authority boundary management that tracks delegated scope versus outside-scope actions, with customer systems enforcing policy decisions
- Fresh assessment protocol ensuring previous permissions do not carry over when configurations change, while retained requirements must be re-evaluated
- Inspectable JSON format for teaching and review, documenting work class, success criteria, evidence, and next cases to compare