Most multi-agent tutorials showcase toy examples: three agents chatting politely in a console before producing a generic summary. In production, unconstrained multi-agent architectures often degrade into recursive hallucination loops, compounding token costs, and non-deterministic failures.
Moving from an experimental agent prototype to a resilient, production-grade system requires treating agent coordination like distributed systems engineering rather than conversational roleplay.
When you push a single LLM to analyze data, evaluate risks, formulate a plan, and format structured output simultaneously, two failure modes emerge:
Decomposing the problem into discrete agents—each with a scoped role, restricted tools, and isolated context—restores predictability.
[ Input Task ]
│
▼
┌───────────────┐
│ Coordinator │ ◄─── Decomposes task & routes state
└───────┬───────┘
│
───────┴───────
│ │
▼ ▼
┌───────────┐ ┌───────────┐
│ Extractor │ │ Risk/Code │ ◄─── Restricted tools & local context
│ Agent │ │ Analyzer │
└─────┬─────┘ └─────┬─────┘
│ │
───────┬───────
▼
┌───────────────┐
│ Critic/Judge │ ◄─── Enforces schema & quality thresholds
└───────┬───────┘
│
▼
[ Validated Result ]
The most common failure in multi-agent systems is allowing agents to pass unstructured markdown or natural language between one another. Every conversational hop introduces ambiguity.
Instead, enforce strictly typed contracts using schemas (such as Pydantic) at every transition boundary:
python
from pydantic import BaseModel, Field
from typing import List, Optional
class RiskAssessment(BaseModel):
threat_level: str = Field(description="LOW, MEDIUM, or CRITICAL")
indicators: List[str] = Field(description="Identified red flags or anomalies")
confidence_score: float = Field(ge=0.0, le=1.0)
requires_human_review: bool
class TaskResult(BaseModel):
task_id: str
status: str
assessment: Optional[RiskAssessment] = None
rejection_reason: Optional[str] = None
