# 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}
}
```