---
tags:
- ml-intern
---
# Agent Cost Optimizer (ACO)

A universal control layer that reduces total cost of autonomous agent runs while preserving task quality.

## Core Thesis

Most agent cost is wasted through:
- Overusing frontier models
- Sending huge context every turn
- Using tools unnecessarily
- Failing and retrying blindly
- Ignoring cache boundaries
- Using verifiers everywhere instead of selectively
- Not learning from previous traces

ACO learns when to spend and when not to spend.

## Architecture

### 10 Core Modules

1. **Cost Telemetry Collector** — Structured trace collection with normalized schema
2. **Task Cost Classifier** — Predicts expected cost, risk, model strength needed
3. **Model Cascade Router** — Dynamic model selection (tiny → cheap → medium → frontier → specialist)
4. **Context Budgeter** — Decides what context is needed vs. what can be omitted/summarized/cached
5. **Cache-Aware Prompt Layout** — Optimizes prompt structure for prefix-cache reuse
6. **Tool-Use Cost Gate** — Predicts whether a tool call is worth the cost
7. **Verifier Budgeter** — Selective verification based on risk, confidence, task type
8. **Retry/Recovery Optimizer** — Intelligent failure recovery without blind retry loops
9. **Meta-Tool Miner** — Compresses repeated workflows into reusable deterministic scripts
10. **Early Termination / Doom Detector** — Detects runs unlikely to succeed and stops them

## Installation

```bash
pip install agent-cost-optimizer
```

## Quick Start

```python
from aco import AgentCostOptimizer

optimizer = AgentCostOptimizer.from_config("config.yaml")
result = optimizer.optimize(agent_request, run_state)
```

## Reward Objective

```
cost_adjusted_score =
  task_success_score
  + safety_bonus
  + artifact_completion_bonus
  + calibration_bonus
  - model_cost_penalty
  - tool_cost_penalty
  - latency_penalty
  - retry_penalty
  - unnecessary_verifier_penalty
  - false_done_penalty
  - unsafe_cheap_model_penalty
  - missed_escalation_penalty
```

## Benchmarks

- Coding Agent Tasks
- Research Agent Tasks  
- Tool-Use Tasks
- Document / Contract / QA Tasks
- Long-Horizon Agent Tasks

## License

MIT

<!-- ml-intern-provenance -->
## Generated by ML Intern

This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.

- Try ML Intern: https://smolagents-ml-intern.hf.space
- Source code: https://github.com/huggingface/ml-intern

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "narcolepticchicken/agent-cost-optimizer"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
```

For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.