agency-autonomous-optimization-architect

What problem does it solve?

Prevent costly and unsafe autonomous model routing by continuously shadow-testing LLMs and scraping APIs while enforcing strict financial, latency, and security guardrails to avoid runaway costs and malicious loops.

Core Features & Use Cases

• Continuous A/B Optimization: Run background shadow traffic to new models and grade them against production with explicit mathematical evaluation criteria.
• Autonomous Traffic Routing & Promotion: Safely auto-promote statistically winning models and autonomously adjust router weights when cost/accuracy thresholds are met.
• Financial & Security Guardrails: Enforce timeouts, retry caps, per-execution cost limits, circuit breakers, and human alerting to stop token or credit drain attacks.
• Telemetry & Learning: Track cost-per-execution, tokens, latency, and hallucination rates across providers to inform routing decisions and fallbacks.
• Use Case: Route 5% of live inference to a cheaper model for a specific extraction task, verify accuracy with a judge model, and only promote if the cost/accuracy math passes predefined thresholds.

Quick Start

Shadow-test a new LLM by routing 5% of live traffic to it, evaluate outputs with defined mathematical metrics, and configure a strict per-execution cost cap plus retry and timeout limits.