AI docs · Quality & evaluation
Self-Verification in AI Agents
Self-verification equips AI agents with internal feedback loops to autonomously check the accuracy of their own work and correct mistakes, reducing reliance on human oversight for multi-step workflows.
What it is
- An AI capability that allows agents to validate their own outputs and actions without external intervention.
- A shift from human-in-the-loop to autonomous error detection and correction in multi-step processes.
How it works
- Agents use internal checks (e.g., code execution, logical consistency, or ground truth comparison) to verify each step.
- Feedback loops update the agent’s state or actions based on verification results, enabling recovery from errors.
- Reduces error accumulation in complex, multi-hop workflows by catching and correcting mistakes in real time.
Trade-offs
- Increases computational overhead due to additional verification steps.
- May introduce new failure modes if verification logic itself is flawed or incomplete.
When to use it
- For high-stakes, multi-step workflows where error accumulation is costly or dangerous.
- When human oversight is impractical due to scale, speed, or cost constraints.
Let's get to work
Want to build with AI for real?
Beyond the explainer, we design, secure, build and run production AI. Tell us what you have in mind.