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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 | # Insight Levels
The ACE framework operates at three insight levels depending on what scope the Reflector analyzes.
## Overview
| Level | Reflector Scope | Feedback Source | Implementation |
|-------|-----------------|----------------|----------------|
| **Micro** | Single interaction | Environment (ground truth) | `ACE` runner with `TaskEnvironment` |
| **Meso** | Full agent run | Execution trace (no ground truth) | Integration runners (`BrowserUse`, `LangChain`, `ClaudeCode`) |
| **Macro** | Cross-run analysis | Pattern comparison across runs | Future enhancement |
## Micro-Level
The Reflector receives the agent's output **and** environment feedback (ground truth, correctness). This is the most precise learning signal.
```mermaid
graph LR
Q[Question] --> R[Reflector]
A[Agent Answer] --> R
GT[Ground Truth] --> R
F[Feedback] --> R
```
Use when you have labeled data or a reliable evaluation function.
```python
from ace import ACE, Sample, SimpleEnvironment
runner = ACE.from_roles(
agent=agent,
reflector=reflector,
skill_manager=skill_manager,
environment=SimpleEnvironment(),
)
samples = [
Sample(question="What is 2+2?", context="", ground_truth="4"),
]
runner.run(samples, epochs=3)
```
## Meso-Level
The Reflector receives the full **execution trace** — the agent's reasoning steps, tool calls, actions, and outcomes — but no external ground truth. It learns from execution patterns rather than correctness evaluation.
```mermaid
graph LR
T[Task] --> R[Reflector]
ET["Execution Trace (thoughts, actions, results)"] --> R
```
Use when wrapping external agents where you don't have labeled answers.
```python
from ace import BrowserUse
# The browser-use agent produces a rich trace of actions
runner = BrowserUse.from_model(
browser_llm=ChatOpenAI(model="gpt-4o"),
ace_model="gpt-4o-mini",
)
runner.run("Find the top post on Hacker News")
```
The extracted trace includes:
- Agent reasoning at each step
- Browser actions (click, type, navigate)
- Page observations
- Success/failure of each action
## Macro-Level
Cross-run pattern analysis — comparing strategies across multiple execution histories. Not yet implemented.
## What to Read Next
- [Three Roles](roles.md) — the roles involved at each level
- [Integration Pattern](../guides/integration.md) — meso-level integrations in practice
- [Full Pipeline Guide](../guides/full-pipeline.md) — micro-level pipelines in practice
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