iterative-retrieval

name	iterative-retrieval
description	Pattern for progressively refining context retrieval to solve the subagent context problem
origin	Multiversal

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:

// 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:

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:

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):

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(evaluation, query);
    bestContext = mergeContext(bestContext, highRelevance);
  }

  return bestContext;
}

Practical Examples

Example 1: Bug Fix Context

Task: "Fix the authentication token expiry bug"

Cycle 1:
  DISPATCH: Search for "token", "auth", "expiry" in src/**
  EVALUATE: Found auth.ts (0.9), tokens.ts (0.8), user.ts (0.3)
  REFINE: Add "refresh", "jwt" keywords; exclude user.ts

Cycle 2:
  DISPATCH: Search refined terms
  EVALUATE: Found session-manager.ts (0.95), jwt-utils.ts (0.85)
  REFINE: Sufficient context (2 high-relevance files)

Result: auth.ts, tokens.ts, session-manager.ts, jwt-utils.ts

Example 2: Feature Implementation

Task: "Add rate limiting to API endpoints"

Cycle 1:
  DISPATCH: Search "rate", "limit", "api" in routes/**
  EVALUATE: No matches - codebase uses "throttle" terminology
  REFINE: Add "throttle", "middleware" keywords

Cycle 2:
  DISPATCH: Search refined terms
  EVALUATE: Found throttle.ts (0.9), middleware/index.ts (0.7)
  REFINE: Need router patterns

Cycle 3:
  DISPATCH: Search "router", "express" patterns
  EVALUATE: Found router-setup.ts (0.8)
  REFINE: Sufficient context

Result: throttle.ts, middleware/index.ts, router-setup.ts

Integration with Agents

Use in agent prompts:

When retrieving context for this task:
1. Start with broad keyword search
2. Evaluate each file's relevance (0-1 scale)
3. Identify what context is still missing
4. Refine search criteria and repeat (max 3 cycles)
5. Return files with relevance >= 0.7

Best Practices

1. Start broad, narrow progressively - Don't over-specify initial queries
2. Learn codebase terminology - First cycle often reveals naming conventions
3. Track what's missing - Explicit gap identification drives refinement
4. Stop at "good enough" - 3 high-relevance files beats 10 mediocre ones
5. Exclude confidently - Low-relevance files won't become relevant

Related

• The Longform Guide - Subagent orchestration section
• continuous-learning skill - For patterns that improve over time
• Agent definitions bundled with Multiversal (manual install path: agents/)