multi-agent-patterns-advanced

name	multi-agent-patterns-advanced
description	Advanced multi-agent patterns — capability registry, durable state (Redis/DynamoDB), task decomposition, testing multi-agent systems, pattern quick-selection guide, failure handling, cost management, and worktree isolation. Extends multi-agent-patterns.

Multi-Agent Patterns — Advanced

See the core multi-agent-patterns skill for basics: orchestration vs choreography, intent classification routing, in-memory state, handoffs, fan-out/fan-in, Claude SDK loop, and observability.

When to Activate

• Building a capability registry for dynamic agent dispatch
• Implementing durable cross-agent state (Redis, DynamoDB event log)
• Decomposing complex goals into parallel and sequential sub-tasks
• Writing deterministic tests for multi-agent workflows
• Selecting the right pattern from the quick-selection guide
• Managing failure modes across agent boundaries
• Controlling token cost in large multi-agent systems
• Isolating parallel file-editing agents with git worktrees

---

Advanced Tool Routing: Capability Registry

interface AgentCapability {
  id: string;
  description: string;
  inputSchema: z.ZodSchema;
  agent: (input: unknown) => Promise<AgentResult>;
}

const capabilities: AgentCapability[] = [
  {
    id: 'analyze_code',
    description: 'Review code for bugs, style, and security issues',
    inputSchema: z.object({ code: z.string(), language: z.string() }),
    agent: codeReviewAgent,
  },
  {
    id: 'generate_tests',
    description: 'Generate unit tests for a function or class',
    inputSchema: z.object({ code: z.string(), framework: z.string() }),
    agent: tddAgent,
  },
];

// Pass registry to orchestrator as tools
const tools = capabilities.map(cap => ({
  name: cap.id,
  description: cap.description,
  input_schema: zodToJsonSchema(cap.inputSchema),
}));

---

Durable State

External Store (Redis)

// Redis for durable cross-agent state
import { createClient } from 'redis';

const redis = createClient();

async function saveAgentState(workflowId: string, state: WorkflowState): Promise<void> {
  await redis.setEx(
    `workflow:${workflowId}`,
    3600,  // 1 hour TTL
    JSON.stringify(state)
  );
}

async function loadAgentState(workflowId: string): Promise<WorkflowState | null> {
  const raw = await redis.get(`workflow:${workflowId}`);
  return raw ? JSON.parse(raw) : null;
}

Event Log (Append-Only, Auditable)

// DynamoDB event log for full audit trail
async function appendEvent(workflowId: string, event: WorkflowEvent): Promise<void> {
  await dynamodb.put({
    TableName: 'workflow-events',
    Item: {
      workflowId,
      timestamp: Date.now(),
      sequenceNumber: await getNextSequence(workflowId),
      event,
    },
  });
}

// Reconstruct state by replaying events
async function replayWorkflow(workflowId: string): Promise<WorkflowState> {
  const events = await queryAllEvents(workflowId);
  return events.reduce(applyEvent, initialState());
}

---

Task Decomposition Handoff

interface SubTask {
  id: string;
  description: string;
  dependsOn: string[];  // IDs of tasks that must complete first
  agent: string;
}

async function decomposeTasks(goal: string): Promise<SubTask[]> {
  const response = await claude.messages.create({
    model: 'claude-opus-latest',  // Opus for complex planning
    system: 'Decompose the goal into parallel and sequential sub-tasks. Output JSON.',
    messages: [{ role: 'user', content: goal }],
    max_tokens: 2048,
  });
  return JSON.parse(response.content[0].text);
}

---

Testing Multi-Agent Systems

// Mock sub-agents for deterministic tests
class MockAgent {
  constructor(private responses: Map<string, string>) {}

  async run(input: string): Promise<string> {
    const key = [...this.responses.keys()].find(k => input.includes(k));
    if (!key) throw new Error(`No mock response for input: ${input}`);
    return this.responses.get(key)!;
  }
}

describe('Orchestrator', () => {
  it('routes code review tasks to code-review agent', async () => {
    const mockCodeReviewer = new MockAgent(new Map([
      ['review this function', '{ "issues": [] }'],
    ]));

    const orchestrator = new Orchestrator({
      agents: { 'code-review': mockCodeReviewer },
    });

    const result = await orchestrator.run('Please review this function: ...');
    expect(result).toContain('issues');
  });

  it('handles partial failures gracefully', async () => {
    const failingAgent = { run: () => Promise.reject(new Error('timeout')) };
    const fallbackAgent = new MockAgent(new Map([['', 'fallback result']]));

    const result = await runWithFallback(
      () => failingAgent.run(),
      () => fallbackAgent.run(''),
      1
    );
    expect(result).toBe('fallback result');
  });
});

---

Worktree Isolation

When multiple agents edit files in parallel, use git worktrees to prevent conflicts:

# Create isolated worktrees for parallel agents
git worktree add ../feature-agent-1 -b agent/feature-1
git worktree add ../feature-agent-2 -b agent/feature-2

# Each agent works in its own directory — no conflicts
# After completion, merge results back
git merge agent/feature-1
git merge agent/feature-2

# Clean up
git worktree remove ../feature-agent-1
git worktree remove ../feature-agent-2

When to use: Any orchestration pattern where 2+ agents modify files concurrently (parallel feature development, multi-file refactors).

---

Pattern Quick-Selection & Token Budget

Task Type	Pattern	Agents	Context per agent
Multi-file review	Fan-Out → Fan-In	3–10	Minimal (target-specific)
Architecture decision	Split-Role	2–4	Full task context
Unknown codebase research	Explorer + Validator	2	Explorer: broad; Validator: targeted
Parallel feature development	Worktree Isolation	2–5	Feature-specific only
Full feature TDD cycle	Sequential Pipeline	3–7	Output of previous stage
Security + quality + tests	Parallel Fan-Out	3	Specialist context only

---

Failure Handling & Result Synthesis

Scenario	Action
Agent timeout	Retry with reduced scope; split task into smaller chunks
Conflicting results	Pass both to a tiebreaker agent; apply: security > quality > style
Partial failures	Complete successful agents; re-run failed with error context
Context overflow	Summarize intermediate results before passing to next stage

---

Cost Management

• Use Haiku for routing, summarization, and classification (high-frequency, low-complexity)
• Use Sonnet for main agent work (default)
• Use Opus only for planning and architectural decisions
• Pass minimal context per agent — compress handoffs with Summary Handoff pattern
• Set max_tokens budgets per agent tier to cap runaway costs
• Use parallelWithConcurrencyLimit (default: 5) to avoid rate-limit overages

For the base patterns (orchestrator loop, fan-out, error handling, observability) see multi-agent-patterns.