agentsarchitecturemulti-agent

Multi-Agent Systems: When One AI Isn't Enough

Exploring the architecture and practical benefits of systems where multiple AI agents collaborate.

After building several single-agent systems, I've started exploring something more ambitious: multiple agents working together.

Why Multiple Agents?

Single agents work great for focused tasks. But complex workflows often benefit from specialization:

  • A researcher agent that finds information
  • A writer agent that produces content
  • A critic agent that reviews and improves
  • An executor agent that takes actions

Each agent can have different prompts, tools, and even different models optimized for their role.

Architecture Patterns

1. Sequential Pipeline

Query → Agent A → Agent B → Agent C → Output

Simple and predictable. Each agent processes and passes to the next.

2. Hierarchical

        Orchestrator
       /     |     \
    Agent   Agent   Agent

A supervisor agent delegates to specialists and synthesizes results.

3. Collaborative

Agent A ←→ Agent B
   ↑         ↑
   └────┬────┘
        ↓
    Shared State

Agents communicate and build on each other's work.

A Practical Example

I built a content creation system with three agents:

Research Agent

  • Tools: web search, document retrieval
  • Output: structured research notes

Draft Agent

  • Input: research notes + user requirements
  • Output: initial draft

Editor Agent

  • Input: draft
  • Tools: grammar check, style analysis
  • Output: polished final version

The result is consistently better than a single agent trying to do everything.

Key Insights

Specialization Improves Quality

A focused agent with a specific system prompt outperforms a generalist agent trying to do everything.

Communication Protocol Matters

Define clear interfaces between agents. What information gets passed? In what format?

Keep Humans in the Loop

Multi-agent systems can go off the rails in surprising ways. Build in checkpoints where humans can review and redirect.

Observe and Debug

When something goes wrong, you need to know which agent failed and why. Good logging is essential.

Challenges

  • Latency: Multiple agents = multiple API calls = longer wait times
  • Cost: More agents = more tokens = higher costs
  • Complexity: Debugging multi-agent systems is harder
  • Coordination: Agents can get stuck in loops or contradictions

Looking Ahead

I believe multi-agent systems are the future of complex AI applications. As models get faster and cheaper, the benefits of specialization will outweigh the coordination costs.

Currently experimenting with:

  • Agents that can spawn sub-agents dynamically
  • Shared memory systems for agent collaboration
  • Evaluation frameworks for multi-agent outputs

More to share soon.