tools-only Multi-Agent Patterns
Distribute work across multiple context windows for isolation and scale.
Core Insight
Sub-agents exist to isolate context, not anthropomorphize roles.
Token Economics
| Architecture | Multiplier | Use Case |
|---|---|---|
| Single agent | 1x | Simple tasks |
| Single + tools | ~4x | Moderate complexity |
| Multi-agent | ~15x | Context isolation needed |
Key: Token usage explains 80% of performance variance.
Patterns
Supervisor/Orchestrator
class Supervisor:
def process(self, task):
subtasks = self.decompose(task)
results = [worker.execute(st, clean_context=True) for st in subtasks]
return self.aggregate(results)
Pros: Control, human-in-loop | Cons: Bottleneck, telephone game
Peer-to-Peer/Swarm
def process_with_handoff(agent, task):
result = agent.process(task)
if "handoff" in result:
return process_with_handoff(select_agent(result["to"]), result["state"])
return result
Pros: No SPOF, scales | Cons: Complex coordination
Hierarchical
Strategy → Planning → Execution layers Pros: Separation of concerns | Cons: Coordination overhead
Context Isolation Patterns
| Pattern | Isolation | Use Case |
|---|---|---|
| Full delegation | None | Max capability |
| Instruction passing | High | Simple tasks |
| File coordination | Medium | Shared state |
Consensus Mechanisms
def weighted_consensus(responses):
scores = {}
for r in responses:
weight = r["confidence"] * r["expertise"]
scores[r["answer"]] = scores.get(r["answer"], 0) + weight
return max(scores, key=scores.get)
Failure Recovery
| Failure | Mitigation |
|---|---|
| Bottleneck | Output schemas, checkpointing |
| Overhead | Clear handoffs, batching |
| Divergence | Boundaries, convergence checks |
| Errors | Validation, circuit breakers |
Guidelines
- Use multi-agent for context isolation, not role-play
- Accept ~15x token cost for benefits
- Implement circuit breakers
- Use files for shared state
- Design clear handoffs
- Validate between agents