The AI Agent Schema

Build Better AI Agents, Faster

A lightweight, extensible framework for defining, building, and deploying AI agents with enterprise-grade reliability.

Featured Solutions

Production-Ready Frameworks and Tools

The Problem

AI agents are revolutionizing automation, but building production-ready agents is harder than it looks:

  • No Standards: Every agent is built differently, making maintenance a nightmare
  • Poor Observability: Can't see what agents are doing or why they made decisions
  • Tight Coupling: Logic, configuration, and data mixed together
  • Platform Lock-In: Switching platforms means rewriting everything
  • No Testing Strategy: Hard to validate behavior before deployment
  • Documentation Debt: Code and behavior drift apart over time

Sound familiar? You're not alone.

The Solution

The AI Agent Schema provides a standardized way to define agent behavior that works across platforms and promotes best practices.

Think of it like:

  • OpenAPI/Swagger for REST APIs
  • Docker Compose for containerized applications
  • Terraform for infrastructure

But for AI agents.


Architecture

Clean Separation of Concerns

  • Agent Schema (YAML) - (What the agent should do)
  • (How to execute the schema)
  • (Actual implementation)

Benefits:

  • Change behavior without touching code
  • Swap implementations without changing schema
  • Test logic independently from integrations
  • Share schemas across teams

Enterprise Features

Security & Compliance

  • Secret Management: Inject API keys securely at runtime
  • Access Controls: Role-based permissions for agent actions
  • Audit Logs: Complete trail of agent decisions
  • PII Protection: Automatic detection and redaction

Governance

  • Schema Validation: Ensure agents meet organizational standards
  • Approval Workflows: Gate deployments with human review
  • Change Tracking: Git-based versioning and rollback
  • Cost Controls: Set spending limits on LLM usage

Observability

  • Real-time Monitoring: See agent activity as it happens
  • Performance Metrics: Track response times, success rates
  • Error Tracking: Automatic alerting on failures
  • Usage Analytics: Understand how agents are being used