The hype around AI agents has outpaced the engineering. Everyone wants "agentic" systems, but few have thought through what that means at scale.
After building multi-agent systems with 12-15+ specialized agents, here's what actually works.
What Makes an Agent
An agent isn't just a prompt with tools. A true agent has:
- Autonomy: Can make decisions without human intervention
- Persistence: Maintains state across interactions
- Goal-orientation: Works toward defined objectives
- Adaptability: Adjusts behavior based on feedback
Most "agents" are really just chains with tool calls. That's fine—but let's be honest about it.
The Coordination Problem
When you have multiple agents, coordination becomes the primary challenge:
Shared state management: How do agents know what others have done?
Resource contention: Multiple agents accessing the same data or services.
Deadlock prevention: Agents waiting on each other indefinitely.
Error propagation: One agent's failure affecting the entire system.
Patterns That Work
1. Orchestrator Pattern
A central coordinator manages agent lifecycle:
Orchestrator
├── Query Analyzer
├── Retriever Pool (parallel)
│ ├── Semantic Retriever
│ ├── Keyword Retriever
│ └── Graph Retriever
├── Synthesizer
└── Validator
Pros: Clear control flow, easier debugging Cons: Single point of failure, can become a bottleneck
2. Blackboard Pattern
Agents communicate through shared state:
- Each agent reads from and writes to a shared "blackboard"
- Agents activate when relevant data appears
- No central coordinator required
Pros: Loosely coupled, emergent behavior Cons: Harder to reason about, potential for chaos
3. Pipeline Pattern
Agents arranged in processing stages:
Input → Analysis → Retrieval → Synthesis → Validation → Output
Pros: Simple mental model, easy to parallelize stages Cons: Inflexible, hard to handle non-linear flows
4. Supervisor Pattern (My Preference)
Hierarchical supervision with specialized teams:
Supervisor
├── Research Team Lead
│ ├── Semantic Agent
│ └── Graph Agent
├── Analysis Team Lead
│ ├── Summarizer
│ └── Fact Checker
└── Quality Lead
├── Validator
└── Formatter
Pros: Scalable, clear responsibility, fault isolation Cons: More complex to implement
Production Essentials
Timeouts and Circuit Breakers
Agents can get stuck. Always implement:
- Per-agent timeouts
- Circuit breakers for repeated failures
- Fallback paths when agents fail
Observability
You need to see what's happening:
- Trace IDs across agent calls
- Token usage per agent
- Latency breakdowns
- Decision logging
Resource Management
At scale, you need:
- Agent pools with connection limits
- Rate limiting per agent type
- Priority queues for critical paths
The Hard Truth
Multi-agent systems are harder than monolithic ones. The coordination overhead is real. The debugging is complex. The failure modes multiply.
But for genuinely complex tasks—where you need specialized expertise, parallel processing, and adaptive behavior—agents are worth the investment.
Start simple. Add agents when you have a clear reason. And always, always build observability first.