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116524e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | # How ACE Works
**Agentic Context Engineering (ACE)** enables AI agents to learn from their own execution feedback. Instead of updating model weights (expensive, slow, opaque), ACE evolves a **skillbook** of strategies based on what actually works.
!!! info "Research"
ACE was introduced in [*Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models*](https://arxiv.org/abs/2510.04618) by researchers at Stanford University and SambaNova Systems.
## The Learning Loop
Three collaborative roles share the same base LLM:
```mermaid
graph LR
S[Sample] --> A[Agent]
A --> E[Environment]
E -->|feedback| R[Reflector]
R -->|analyzes| SM[SkillManager]
SM -->|updates| SK[Skillbook]
SK -.->|context| A
```
1. The **Agent** executes a task using strategies from the skillbook
2. The **Environment** evaluates the result (correct/incorrect, feedback)
3. The **Reflector** analyzes what worked and what failed
4. The **SkillManager** updates the skillbook with new strategies
The **Skillbook** accumulates strategies across runs, making every subsequent agent call smarter.
## Three Roles
| Role | Responsibility | Key Class |
|------|---------------|-----------|
| **Agent** | Executes tasks using skillbook strategies | `Agent` |
| **Reflector** | Analyzes execution results (what worked, what failed) | `Reflector` |
| **SkillManager** | Transforms reflections into skillbook updates | `SkillManager` |
All three roles use the same LLM — the intelligence comes from the specialized prompts each role receives.
See [Three Roles](roles.md) for details on each role's inputs and outputs.
## Two Architecture Patterns
### Full ACE Pipeline
Use when building a new agent from scratch.
```mermaid
graph LR
S[Sample] --> A[Agent]
A --> E[Environment]
E --> R[Reflector]
R --> SM[SkillManager]
SM --> SK[Skillbook]
```
All three roles participate. The Agent produces answers, the Environment evaluates them, and the learning pipeline updates the skillbook.
```python
from ace import ACE, Agent, Reflector, SkillManager, SimpleEnvironment
runner = ACE.from_roles(
agent=Agent("gpt-4o-mini"),
reflector=Reflector("gpt-4o-mini"),
skill_manager=SkillManager("gpt-4o-mini"),
environment=SimpleEnvironment(),
)
results = runner.run(samples, epochs=3)
```
### Integration Pattern
Use when wrapping an existing agent (browser-use, LangChain, Claude Code).
```mermaid
graph LR
EA[External Agent] -->|executes| R[Reflector]
R -->|analyzes trace| SM[SkillManager]
SM -->|updates| SK[Skillbook]
```
No ACE Agent — the external framework handles execution. ACE only learns from the results.
Three steps: **INJECT** skillbook context, **EXECUTE** with external agent, **LEARN** from results.
```python
from ace import BrowserUse
runner = BrowserUse.from_model(
browser_llm=ChatOpenAI(model="gpt-4o"),
ace_model="gpt-4o-mini",
)
results = runner.run("Find the top post on Hacker News")
```
See [Integration Pattern](../guides/integration.md) for building custom integrations.
## How It Compares
| Approach | Updates | Speed | Interpretability |
|----------|---------|-------|-----------------|
| **Fine-tuning** | Model weights | Slow (hours) | Low (opaque) |
| **RAG** | External documents | Medium | Medium |
| **ACE** | Skillbook context | Fast (real-time) | High (readable strategies) |
ACE strategies are human-readable, auditable, and transferable between models.
## Performance
| Benchmark | Improvement | Notes |
|-----------|-------------|-------|
| AppWorld Agent | **+17.1 pp** | Complex multi-step tasks with tool use |
| FiNER (Finance) | **+8.6 pp** | Financial reasoning tasks |
| Adaptation Latency | **-86.9%** | vs. existing context-adaptation methods |
## What to Read Next
- [The Skillbook](skillbook.md) — how strategies are stored and evolve
- [Three Roles](roles.md) — Agent, Reflector, and SkillManager in detail
- [Quick Start](../getting-started/quick-start.md) — run your first agent
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