Governed autonomy. Proof of execution. Systems that learn.
The Problem
Most AI agent systems share three critical failures:
Agents drift on complex tasks. Without structured planning and proof-of-execution, failures compound silently.
Unsupervised agents are unacceptable in production. Teams need human-in-the-loop gates without sacrificing throughput.
Agents forget. Systems repeat the same mistakes. Without a skill-learning loop, there is no compounding capability.
The Solution
Peregrines provides three foundational capabilities that address each failure:
Deconstructs ambiguous goals into executable steps with explicit scope boundaries, verification criteria, and handoff metadata.
Every action produces disk evidence — commit hashes, file paths, exit codes. No claims without verification.
Patterns discovered during execution are codified into reusable skills. The system improves after every task.
How It Works
Peregrines structures agent work through a clear separation of concerns:
Each layer communicates through structured handoffs with metadata, ensuring traceability from goal to evidence.
Governed Autonomy
Peregrines implements a risk-based approval model. Low-risk operations proceed autonomously with post-factum audit trails. Medium and high-risk actions — production changes, credential edits, infrastructure modifications — require explicit human approval before execution. The system never guesses at criticality; it flags risk and waits.
Deployment
Peregrines supports multiple deployment topologies to match your security and connectivity requirements:
Download the whitepaper for the full architectural overview, or open the control plane to see it in action.