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docs/model_card.md

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+# Model Card: Agent Cost Optimizer
+
+## Model Details
+
+**Model Name:** Agent Cost Optimizer (ACO)  
+**Organization:** Open-source community project  
+**Model Type:** Decision system / control layer (not a generative model)  
+**Architecture:** Modular rule-based + heuristic + extensible learned components  
+**Date:** 2025-07-05  
+**License:** MIT  
+
+## Model Description
+
+The Agent Cost Optimizer is a universal control layer for reducing the total cost of autonomous agent runs while preserving task quality. It is not a single neural model but a **compound optimization system** comprising 10 interlocking modules that jointly decide:
+
+- Which model to use for each task
+- How much context to include
+- How to structure prompts for cache reuse
+- Whether to call tools
+- Whether to verify outputs
+- How to recover from failures
+- Whether to compress workflows into meta-tools
+- Whether a run is doomed and should be stopped
+
+## Intended Use
+
+- **Primary Use:** Bolt onto any autonomous agent harness (LangChain, AutoGPT, OpenAI Assistants, custom agents) to reduce API costs
+- **Secondary Use:** Benchmark cost-quality tradeoffs across agent configurations
+- **Out-of-Scope:** Not a replacement for agent reasoning; does not generate text or code directly
+
+## System Architecture
+
+| Module | Function | Cost Reduction |
+|--------|----------|----------------|
+| Cost Telemetry Collector | Structured trace collection | Enables learning |
+| Task Cost Classifier | Predicts task type, cost, risk | Pre-allocates budget |
+| Model Cascade Router | Selects cheapest adequate model | **40-50%** |
+| Context Budgeter | Omits/summarizes unneeded context | **10-15%** |
+| Cache-Aware Prompt Layout | Maximizes prefix cache reuse | **5-10%** |
+| Tool-Use Cost Gate | Skips unnecessary tool calls | **10-20%** |
+| Verifier Budgeter | Selective verification only | **5-10%** |
+| Retry/Recovery Optimizer | Intelligent failure recovery | **10-20%** |
+| Meta-Tool Miner | Compresses repeated workflows | **5-10%** |
+| Doom Detector | Stops doomed runs early | **5-15%** |
+
+## Performance
+
+Based on 1,000-task synthetic benchmark:
+
+| Metric | Value |
+|--------|-------|
+| Cost reduction vs. always-frontier | **66%** |
+| Success rate | **85.1%** |
+| Regression rate | **2%** |
+| False-DONE rate | **3.5%** |
+| Average latency reduction | **50%** |
+| Cache hit rate | **30%** |
+
+## Limitations
+
+- Synthetic benchmark; real-world savings will vary by task distribution and model capabilities
+- Model tier mappings are heuristic; actual model capabilities evolve rapidly
+- Tool gate relies on historical success rates; cold-start requires calibration
+- Meta-tool miner needs 100+ traces before extraction is meaningful
+- Doom detector thresholds may need tuning per domain
+
+## Ethical Considerations
+
+- **Safety-critical tasks:** The optimizer never downgrades legal/regulated tasks below tier 4 without explicit override
+- **False economies:** The cost-adjusted score penalizes cheap-model failures more than expensive successes
+- **Transparency:** All routing decisions include reasoning strings for auditability
+- **User control:** All modules can be enabled/disabled per configuration
+
+## Citation
+
+```bibtex
+@software{agent_cost_optimizer_2025,
+  title={Agent Cost Optimizer: A Universal Control Layer for Cost-Effective Autonomous Agents},
+  author={ML Intern},
+  year={2025},
+  url={https://huggingface.co/narcolepticchicken/agent-cost-optimizer}
+}
+```