Iterative Retrieval

# Iterative Retrieval Pattern

Solves the "context problem" in multi-agent workflows where subagents don't know what context they need until they start working.

## When to Activate

- Spawning subagents that need codebase context they cannot predict upfront
- Building multi-agent workflows where context is progressively refined
- Encountering "context too large" or "missing context" failures in agent tasks
- Designing RAG-like retrieval pipelines for code exploration
- Optimizing token usage in agent orchestration

## The Problem

Subagents are spawned with limited context. They don't know:
- Which files contain relevant code
- What patterns exist in the codebase
- What terminology the project uses

Standard approaches fail:
- **Send everything**: Exceeds context limits
- **Send nothing**: Agent lacks critical information
- **Guess what's needed**: Often wrong

## The Solution: Iterative Retrieval

A 4-phase loop that progressively refines context:

```
┌─────────────────────────────────────────────┐
│                                             │
│   ┌──────────┐      ┌──────────┐            │
│   │ DISPATCH │─────│ EVALUATE │            │
│   └──────────┘      └──────────┘            │
│        ▲                  │                 │
│        │                  ▼                 │
│   ┌──────────┐      ┌──────────┐            │
│   │   LOOP   │─────│  REFINE  │            │
│   └──────────┘      └──────────┘            │
│                                             │
│        Max 3 cycles, then proceed           │
└─────────────────────────────────────────────┘
```

### Phase 1: DISPATCH

Initial broad query to gather candidate files:

```javascript
// Start with high-level intent
const initialQuery = {
  patterns: ['src/**/*.ts', 'lib/**/*.ts'],
  keywords: ['authentication', 'user', 'session'],
  excludes: ['*.test.ts', '*.spec.ts']
};

// Dispatch to retrieval agent
const candidates = await retrieveFiles(initialQuery);
```

### Phase 2: EVALUATE

Assess retrieved content for relevance:

```javascript
function evaluateRelevance(files, task) {
  return files.map(file => ({
    path: file.path,
    relevance: scoreRelevance(file.content, task),
    reason: explainRelevance(file.content, task),
    missingContext: identifyGaps(file.content, task)
  }));
}
```

Scoring criteria:
- **High (0.8-1.0)**: Directly implements target functionality
- **Medium (0.5-0.7)**: Contains related patterns or types
- **Low (0.2-0.4)**: Tangentially related
- **None (0-0.2)**: Not relevant, exclude

### Phase 3: REFINE

Update search criteria based on evaluation:

```javascript
function refineQuery(evaluation, previousQuery) {
  return {
    // Add new patterns discovered in high-relevance files
    patterns: [...previousQuery.patterns, ...extractPatterns(evaluation)],

    // Add terminology found in codebase
    keywords: [...previousQuery.keywords, ...extractKeywords(evaluation)],

    // Exclude confirmed irrelevant paths
    excludes: [...previousQuery.excludes, ...evaluation
      .filter(e => e.relevance < 0.2)
      .map(e => e.path)
    ],

    // Target specific gaps
    focusAreas: evaluation
      .flatMap(e => e.missingContext)
      .filter(unique)
  };
}
```

### Phase 4: LOOP

Repeat with refined criteria (max 3 cycles):

```javascript
async function iterativeRetrieve(task, maxCycles = 3) {
  let query = createInitialQuery(task);
  let bestContext = [];

  for (let cycle = 0; cycle < maxCycles; cycle++) {
    const candidates = await retrieveFiles(query);
    const evaluation = evaluateRelevance(candidates, task);

    // Check if we have sufficient context
    const highRelevance = evaluation.filter(e => e.relevance >= 0.7);
    if (highRelevance.length >= 3 && !hasCriticalGaps(evaluation)) {
      return highRelevance;
    }

    // Refine and continue
    query = refineQuery(e