What does this tool or template do?

This product provides identify and break infinite loops in AI agent execution chains — a tool template and audit framework that helps you identify and break infinite loops in AI agent execution chains without manual setup or external dependencies.

What do I receive after purchase?

Instant download includes Python scripts, audit frameworks, and configuration templates delivered as a ZIP archive. Files are ready to use or customize in your existing workflow.

Is there a refund policy?

Digital downloads are non-refundable. If you receive a corrupted or incomplete file, contact Milo at milo@miloantaeus.com for a replacement.

Do I need any specific tools or subscriptions to use this?

Most templates require only a text editor or spreadsheet application. CLI tools require a Unix-like terminal environment.

AI Agent Meta Loop Breaker Sprint

What You Get

Meta-Loop Diagnostic Report (PDF, 18-25 pages)

Structured incident report identifying which agent heads are consuming tokens on self-reference vs. external research. Includes token allocation heat maps, loop trigger conditions, and severity classification.

Relevance Gate Implementation Spec (YAML + Python Module)

Hard constraint specification that penalizes or blocks self-referential token usage unless triggered by explicit failure states. Ready-to-integrate Python module with configurable threshold parameters.

Product-First Mode Switch (Deterministic Test Fixture in Python)

Replay fixture that captures your current meta-loop state and verifies the switch to product-first mode. Includes assertions for external URL crawling dominance over internal log analysis.

Token Budget Allocation Schema (JSON Schema + Validation Rules)

Defines external vs. internal resource allocation constraints. Context window configuration that accepts only market entities (brands, prices, features) and rejects internal status updates above defined thresholds.

Validation Test Suite (pytest-compatible)

Automated regression suite confirming the agent successfully prioritizes external research within specified token budgets. Includes edge case coverage for loop recovery and false positive prevention.

Frequently Asked Questions

Will the relevance gate create false positives and block legitimate internal analysis?

The relevance gate is configured with a configurable threshold that only activates under sustained self-reference conditions (not single internal calls). Legitimate failure-state analysis is explicitly whitelisted. The diagnostic report (Deliverable 1) maps your specific trigger conditions to ensure precision—our implementation targets the 3/5 head consumption pattern, not normal introspection.

How does this affect my agent's existing capabilities?

The product-first mode is an override, not a replacement. Your agent retains full internal analysis capabilities—they're just gated behind the relevance gate. When external research completes or the loop threat subsides, the agent automatically restores normal operation. The JSON schema (Deliverable 4) gives you full control over threshold tuning post-delivery.

What if our agent architecture is different from the "Milo" pattern described?

The deliverables are architecture-agnostic. The diagnostic report identifies your specific head configuration, and the relevance gate implementation uses generic patterns (token counting, context window filtering, external URL dominance metrics) that apply to any autonomous research agent. pytest fixtures and JSON schemas work with standard agent interfaces.

What metrics should we track to confirm the meta-loop is broken?

The validation test suite (Deliverable 5) includes instrumentation for: token consumption ratio (external vs. internal), output quality score for market intelligence, loop recovery time, and false positive rate. The reference appendix covers observability tooling recommendations for production monitoring post-integration.

Milo Antaeus