Self-Improving AI Agents

Peregrines™

Governed autonomy. Proof of execution. Systems that learn.

Agentic AI with delegated authority, operating wherever needed, through secure edge-device execution.

“The Peregrines take them home; the talons do the hands-on work.”

→ A governed computer agency: keys stay with you, not the platform — Read the whitepaper

AI agents struggle with reliability, accountability, and growth

Most AI agent systems share three critical failures:

⚡

Brittle Execution

Agents drift on complex tasks. Without structured planning and proof-of-execution, failures compound silently.

🔒

No Governance

Unsupervised agents are unacceptable in production. Teams need human-in-the-loop gates without sacrificing throughput.

📑

No Memory

Agents forget. Systems repeat the same mistakes. Without a skill-learning loop, there is no compounding capability.

A platform built for governed, self-improving execution

Peregrines provides three foundational capabilities that address each failure:

📋

Planner

Deconstructs ambiguous goals into executable steps with explicit scope boundaries, verification criteria, and handoff metadata.

✅

Proof-of-Execution

Every action produces disk evidence — commit hashes, file paths, exit codes. No claims without verification.

🔄

Skill Loop

Patterns discovered during execution are codified into reusable skills. Improvements are proposed and promoted after operator review.

Self-improvement: Skills improve after every task is completed. Proposed improvements are reviewed and promoted by the operator — the system does not self-modify autonomously.

Falconer → Peregrine → Talon

Peregrines structures agent work through a clear separation of concerns:

Falconer
Orchestrator with HITL authority
→
Peregrines
Workers on governed endpoints
→
Talons
Skills that do the hands-on work

Each layer communicates through structured handoffs with metadata, ensuring traceability from goal to evidence.

Product vocabulary is defined on the Falconry Taxonomy — explore the locked definitions.

Human oversight without human bottleneck

Peregrines implements a risk-based approval model:

Approval Rules

  • Low-risk (reversible): Proceed autonomously with post-factum audit trails.
  • Type 1 reversible (medium): Notify-only. The operator is informed after execution.
  • Type 1 reversible (high): Explicit human approval before execution.
  • Type 2 irreversible: Always require approval at every risk tier — low, medium, or high. Never executes without pre-approval.

The system never guesses at criticality; it flags risk and waits.

Cloud or Hybrid

Peregrines supports multiple deployment topologies. Commercial deployments are Cloud or Hybrid only.

Cloud
Hosted on managed infrastructure with full connectivity
Default for commercial
Hybrid
Customer-hosted Peregrines (+ customer-hosted inference where hardware supports)
Split orchestration and execution
Secret-Bearing
Local inference with secret-bearing keys; keys stay with the operator, not the platform
Type 2 always requires pre-approval

Air-gap is not a product tier — it is a connectivity state. The same Cloud and Hybrid tiers can run air-gapped.

Enterprise and Agency

A custom aaiaas.ai installation inside the customer’s environment that can run fully air-gapped, delivered under contract with installation, support, and updates.

Contact the Falconer

Ready to build self-improving agents?

Download the whitepaper for the full architectural overview, or open the control plane to see it in action.