Multi-Agent Systems

What is a Multi-Agent System?

Multiple agents working together (or against each other) to solve problems:

Single agent: One AI handling all tasks
Multi-agent: Specialized agents for different roles

Example (Book vacation):
- Researcher agent: Finds destinations, accommodations
- Budget agent: Tracks costs, suggests options within budget
- Coordinator agent: Combines recommendations, books everything
- Communication agent: Keeps user informed

Each agent specialized, communicating with others!

Agent Collaboration Patterns

Pipeline (Sequential)

Agent A → Agent B → Agent C → Result

A does step 1
B takes A's output, does step 2
C takes B's output, does step 3

Parallel (Independent)

Agent A → 
         → Coordinator →
Agent B →

A and B work simultaneously
Coordinator merges results

Hierarchical (Manager-Worker)

Manager Agent
  ↙        ↓        ↘
Worker1  Worker2  Worker3

Manager delegates tasks
Workers execute tasks
Manager coordinates results

Communication Between Agents

Agents share information:

Agent A: "I found these flights: [list]"
Agent B: "Given budget constraints, best option is: [flight]"
Agent C: "I've booked it! Confirmation: #123"

Agents parse each other's outputs!

Example: Research Paper Agent Team

Goal: "Write comprehensive literature review"

Agents:
1. Search Agent: Finds papers on ArXiv, Google Scholar
2. Read Agent: Extracts key concepts from papers
3. Organize Agent: Groups papers by theme
4. Writer Agent: Generates comprehensive summary
5. Reviewer Agent: Checks for completeness, accuracy

Each agent specialized, working together!

Challenges in Multi-Agent Systems

1. Coordination

Problem: Agents making conflicting decisions
Solution: Central coordinator or agreement protocol

2. Communication

Problem: Agents speaking different "languages"
Solution: Standardized message format (JSON, structured output)

3. Trust & Verification

Problem: Can you trust other agent's output?
Solution: Verification checks, human oversight

Competitive Multi-Agent Systems

Agents can also compete:

Scenario: Debate system
- Agent A: Argues for option X
- Agent B: Argues for option Y
- Judge Agent: Evaluates arguments

Better arguments = Better decisions!