Multi-Agent Systems

Why Multi-Agent Systems?

Complex tasks often benefit from multiple specialized agents working together. Multi-agent systems enable:

• Separation of concerns: Each agent focuses on its specialty
• Parallel processing: Multiple agents work simultaneously
• Specialization: Different models for different tasks
• Collaboration: Agents can delegate and share information

Creating a Multi-Agent System

Define Multiple Agents

import { Agent } from 'zod-framework';

const researcher = new Agent({
  name: 'Researcher',
  model: 'gpt-4',
  skills: ['reasoning', 'web-search'],
  systemPrompt: 'You are a research specialist. Find accurate information on any topic.',
});

const writer = new Agent({
  name: 'Writer',
  model: 'gpt-4',
  skills: ['conversation', 'coding'],
  systemPrompt: 'You are a technical writer. Create clear, concise documentation.',
});

const reviewer = new Agent({
  name: 'Reviewer',
  model: 'gpt-4-turbo',
  skills: ['reasoning'],
  systemPrompt: 'You are a quality reviewer. Check for accuracy and completeness.',
});

Agent Communication Patterns

Sequential Pattern

Agents work in a sequence, each building on the previous output:

import { Pipeline } from 'zod-framework';

const pipeline = new Pipeline([
  researcher,
  writer,
  reviewer,
]);

const result = await pipeline.execute('Research AI trends and write an article');

Parallel Pattern

Multiple agents work simultaneously:

import { Parallel } from 'zod-framework';

const parallel = new Parallel([
  researcher,
  writer,
]);

const [research, draft] = await parallel.execute('Create a comprehensive report');

Collaborative Pattern

Agents work together, exchanging messages:

import { Collaboration } from 'zod-framework';

const collab = new Collaboration({
  agents: [researcher, writer, reviewer],
  strategy: 'round-robin', // or 'leader-follower', 'consensus'
  maxRounds: 5,
});

const result = await collab.execute('Write a technical whitepaper');

Agent Delegation

Agents can delegate tasks to other agents:

const manager = new Agent({
  name: 'Manager',
  model: 'gpt-4',
  skills: ['reasoning'],
  systemPrompt: `You are a project manager. Delegate tasks to specialists.`,
});

// Delegate from within an agent
await manager.task('Research the latest AI trends', { delegateTo: researcher });

Shared Memory

Agents can share state through a common memory space:

import { MemorySpace } from 'zod-framework';

const memory = new MemorySpace();

researcher.setMemory(memory);
writer.setMemory(memory);
reviewer.setMemory(memory);

// Any agent can read/write shared data
memory.set('research_data', researchResults);
const data = memory.get('research_data');

Coordination Strategies

Leader-Follower

One agent coordinates the work of others:

import { LeaderFollower } from 'zod-framework';

const system = new LeaderFollower({
  leader: manager,
  followers: [researcher, writer, reviewer],
});

Consensus

All agents must agree on decisions:

import { Consensus } from 'zod-framework';

const decision = await Consensus.vote({
  agents: [researcher, writer, reviewer],
  proposal: 'Use GPT-4 for this task',
  threshold: 0.66, // 66% agreement needed
});

Debugging Multi-Agent Systems

Use the Zod Studio visual interface to monitor agent interactions:

npx zod-studio watch

This provides:

• Real-time message flow visualization
• Agent state inspection
• Performance metrics
• Error tracking

Best Practices

1. Design clear boundaries: Each agent should have a specific role
2. Minimize dependencies: Reduce coupling between agents
3. Use appropriate models: Match model capability to task complexity
4. Monitor communication: Track message passing between agents
5. Handle failures gracefully: Implement retry and fallback logic
6. Test incrementally: Start with 2 agents, then add more

Continue to Skills & Tools to learn about extending agent capabilities.