closed-loop-design

# Closed Loop Design Skill

Guide for designing closed-loop prompts that ensure agent reliability through feedback loops.

## When to Use

- Designing new agentic workflows
- Adding validation to existing processes
- Creating self-correcting agent pipelines
- Building automated test-fix cycles

## Core Concept

Every agentic operation should follow the Request-Validate-Resolve pattern:

```text
REQUEST → VALIDATE → RESOLVE
    ↑                    ↓
    └────────────────────┘
```

## Design Workflow

### Step 1: Identify the Operation

What task needs to be validated?

- Code changes (feature, bug fix, refactor)
- File operations (create, modify, delete)
- External interactions (API calls, database queries)
- Build/deploy operations

### Step 2: Define Validation Mechanism

How do we know if it succeeded?

| Operation Type | Validation Mechanism |
| --- | --- |
| Code changes | Run tests, type check, lint |
| File operations | Verify file exists, check content |
| API interactions | Check response status, validate data |
| Build operations | Build succeeds, no errors |

### Step 3: Design the Resolve Path

What happens on failure?

1. **Analyze**: Parse error output to understand failure
2. **Fix**: Make minimal, targeted corrections
3. **Re-validate**: Run validation again
4. **Limit retries**: Set maximum attempts (typically 3-5)

### Step 4: Create the Prompt Structure

Template:

```markdown
## Closed Loop: [Operation Name]

### Request
[Clear task description with success criteria]

### Validate
- Command: `[validation command]`
- Success: [what success looks like]
- Failure: [what failure looks like]

### Resolve (if validation fails)
1. Analyze the failure output
2. Identify root cause
3. Apply minimal fix
4. Return to Validate step
5. Maximum 3 retry attempts
```

## Example: Test-Fix Loop

```markdown
## Closed Loop: Implement Feature

### Request
Implement the user login feature according to spec.

### Validate
- Command: `pytest tests/test_auth.py -v`
- S