oyi77/1ai-skills/autonomy-engine

Autonomy Engine

When to Use

Trigger phrases:

• "autonomy engine"
• "Core autonomy protocol for an AI General Manager agent"

• When the task falls within this skill's domain expertise
• When automated execution saves time over manual work
• When the skill's tools and integrations are available

When NOT to Use

• When the task can be solved with existing standard libraries
• When the infrastructure is already in place and working
• When the added complexity does not provide measurable benefit

Overview

Autonomy Engine is a foundational core infrastructure skill that provides system foundation capabilities for the agent ecosystem.

Architecture

• Input layer — Receives and validates incoming requests
• Processing layer — Core logic for system foundation
• Output layer — Formats and delivers results
• State management — Maintains context across invocations

Configuration

• Set up required environment variables and paths
• Configure logging level and output format
• Define resource limits (memory, time, API calls)
• Enable/disable features via configuration flags

Integration

• Exposes standard interfaces for other skills to consume
• Supports event-driven and request-response patterns
• Compatible with the 1ai-skills hook system
• Logs metrics for the skill performance monitor

Anti-Rationalization Table

# Example: Model routing
ROUTES = {
    "code": ["claude-sonnet-4-20250514", "gpt-4o"],
    "vision": ["gemini-2.5-pro", "gpt-4o"],
    "fast": ["gemini-2.5-flash", "gpt-4o-mini"],
}
def route_request(task: str, prompt: str):
    models = ROUTES.get(task, ROUTES["fast"])
    for model in models:
        try:
            return call_model(model, prompt)
        except Exception:
            continue
    raise RuntimeError("All models failed")

Process

1. Prepare — Gather requirements, verify prerequisites, set up environment
2. Execute — Run autonomy engine workflow with configured parameters
3. Verify — Validate output meets requirements, document results

Verification

• [ ] All steps executed successfully
• [ ] Results validated against acceptance criteria
• [ ] Error handling tested with edge cases
• [ ] Documentation updated with findings