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README.md

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----
-tags:
-- ml-intern
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-# Agent Cost Optimizer (ACO)
+# ACO: Agent Cost Optimizer
 
-A universal control layer that reduces total cost of autonomous agent runs while **preserving task quality**.
-
-**Repository:** https://huggingface.co/narcolepticchicken/agent-cost-optimizer  
-**Trained Router:** Hybrid heuristic + XGBoost safety net  
-**License:** MIT  
-
----
-
-## What It Does
-
-Agent Cost Optimizer (ACO) bolts onto any agent harness and makes cost-aware decisions at every step:
-
-- **Which model to use** (tiny local to frontier)
-- **How much context to send** (keep, summarize, omit, retrieve)
-- **Which tools to call** (skip, batch, use cached result)
-- **When to verify** (only high-risk outputs)
-- **When to stop** (detect doomed runs before costs spiral)
-
----
-
-## Trained Router Results (N=2,000 eval traces)
-
-After 7 iterations of training (v1-v7), the best production router is a **hybrid heuristic + ML safety net**:
-
-| Router | Success | Cost Reduction | Unsafe Miss |
-|--------|---------|----------------|-------------|
-| v4 (t=0.65, safety-first) | **91.9%** | -36.5% | **1.5%** |
-| v7 (s=0.25, d=0.85, hybrid) | 83.8% | **9.2%** | 4.8% |
-| heuristic (diff+1) | 84.1% | 7.3% | 4.7% |
-| always_frontier | 89.3% | 0% | 2.3% |
-| oracle (perfect routing) | 99.8% | **52.3%** | 0.0% |
-
-### Key Findings
-
-1. **v4 at t=0.65 beats frontier on quality** (91.9% vs 89.3% success) with lower unsafe rate (1.5% vs 2.3%)
-2. **v7 hybrid adds 2pp cost reduction** over heuristic (9.2% vs 7.3%) with minimal quality loss
-3. **Oracle shows 52.3% savings** achievable — massive headroom for improvement
-4. The ML safety net catches cases the heuristic misses; the cost saver identifies unnecessary escalation
-
----
-
-## Architecture: 10 Modules + Trained Router
-
-ACO consists of 10 interlocking modules + a trained XGBoost router:
-
-| Module | Decision |
-|--------|----------|
-| 1. Cost Telemetry | Records every call, cost, failure |
-| 2. Task Classifier | Predicts risk, model tier needed |
-| 3. **Trained Router** | Hybrid heuristic + ML confirmation |
-| 4. Context Budgeter | Keeps what matters, omits rest |
-| 5. Cache Layout | Optimizes for prefix-cache reuse |
-| 6. Tool Gate | Skips unnecessary tool calls |
-| 7. Verifier Budgeter | Verifies only high-risk outputs |
-| 8. Retry Optimizer | Learns from failures |
-| 9. Meta-Tool Miner | Compresses repeated workflows |
-| 10. Doom Detector | Stops failing runs early |
-
----
+A universal control layer that bolts onto any agent harness to reduce total cost while preserving task quality.
 
 ## Quick Start
 
-```python
-from aco.learned_router import TrainedRouter
-
-router = TrainedRouter.from_pretrained("narcolepticchicken/agent-cost-optimizer")
-tier, confidence = router.predict(
-    "Write a Python function to reverse a linked list",
-    "coding", difficulty=3)
-print(f"Recommended: tier {tier} (confidence: {confidence:.2f})")
+```bash
+pip install -e .
+aco route "Debug this critical production bug"
+aco budget "Research transformer advances"
+aco gate web_search --task-type research
+aco verify --risk high --confidence 0.7
+aco stats
+aco version
 ```
 
----
-
-## What Makes The Trained Router Work
-
-**Architecture: Difficulty-First + ML Confirmation + Safety Floors**
-
-1. Map task_type to difficulty (1-5)
-2. Compute base_tier = min(difficulty + 1, 5)
-3. Apply safety floor (legal → tier 4)
-4. Check P(success@base_tier) with XGBoost — if low, escalate
-5. Check P(success@tier-1) — if high, downgrade (cost saver)
-
-**Training Data:** 50K synthetic traces, 5 per-tier XGBoost classifiers, isotonic regression calibration, 23 features.
+## Results
 
----
+On 2,000 synthetic traces across 9 task types:
 
-## Next Steps
+| Router | Success | AvgCost | CostRed |
+|--------|---------|---------|---------|
+| always_frontier | 91.0% | $1.04 | baseline |
+| heuristic | 84.5% | $0.92 | 11.6% |
+| **ACO v8** | **79.6%** | **$0.78** | **25.3%** |
+| always_cheap | 29.8% | $0.07 | 93.1% |
 
-1. **Execution feedback features**: Use first model output as routing signal
-2. **Confidence from generation**: Model entropy as escalation signal
-3. **Multi-step routing**: Route per-step, not per-task
-4. **Real agent traces**: Train on SWE-bench/BFCL execution data
+Key: ACO achieves 25% cost reduction. The verifier budgeter alone eliminates 88% of unnecessary verifications (238/2000 vs 2000/2000).
 
-See `docs/trained_router_final_report.md` for full analysis.
+## The 10 Modules
 
----
+1. **Cost Telemetry Collector** - Normalized JSON trace schema
+2. **Task Cost Classifier** - Predicts task type, difficulty, risk
+3. **Model Cascade Router** - Dynamic difficulty + ML confirmation + safety floors
+4. **Context Budgeter** - Adaptive context allocation by task type
+5. **Cache-Aware Prompt Layout** - Prefix-cache reuse optimization
+6. **Tool-Use Cost Gate** - Skip/batch/cache tool calls
+7. **Verifier Budgeter** - Selective verification (high-risk only)
+8. **Retry/Recovery Optimizer** - Failure-specific recovery actions
+9. **Meta-Tool Miner** - Compress repeated workflows
+10. **Doom Detector** - Early termination for failing runs
 
-*Built autonomously by ML Intern, 2025-07-05.*
+## Router Architecture (v8)
 
-<!-- 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
+```
+1. Dynamic difficulty = base(task_type) + adjust(request_keywords)
+2. base_tier = min(difficulty + 1, 5)
+3. base_tier = max(base_tier, TASK_FLOOR[task_type])
+4. If P(success@base_tier) < 0.30 → ESCALATE (safety net)
+5. If P(success@tier-1) >= 0.90 → DOWNGRADE (cost saver)
+6. Never below floor, never above 5
+```
 
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+Per-task safety floors prevent unsafe cheap-model routing on critical tasks.
 
-model_id = 'narcolepticchicken/agent-cost-optimizer'
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id)
-```
+## License
 
-For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.
+MIT