How Splitting Tasks Across Multiple AI Agents Reduces Production Failures

A development team discovered the limits of single-agent AI systems when their document analysis agent hallucinated during validation roughly 15% of the time in production, caused by overloading one context window with too many roles. Their solution was to split the workflow into multiple specialised agents, each handling a single task such as research, validation, or summarisation. The team uses Celery for orchestration, with an orchestrator agent directing worker agents that operate independently without sharing conversation history. This isolation is intentional, as it prevents errors from cascading between steps and keeps each agent's instructions focused. Output between agents is schema-validated using Pydantic to catch silent failures before they propagate downstream.