Multi-Agent Orchestration in Production

The Limits of a Single Prompt

When you ask a single LLM to act as a researcher, writer, and editor simultaneously, performance degrades rapidly. The model loses focus, hallucinates details, and struggles with long context windows. The solution is specialization.

The Multi-Agent Paradigm

Instead of one monolithic prompt, we build systems using specialized agents. Each agent has a specific persona, a constrained set of tools, and a defined goal.

• The Researcher: Has access to web search and database querying tools. Its only job is to gather factual data.
• The Writer: Takes the researcher's raw data and drafts the content according to brand guidelines.
• The Critic: Reviews the writer's draft against the original prompt and forces revisions if criteria aren't met.

Choosing an Orchestrator

You need a framework to manage how these agents communicate. We evaluate orchestrators based on the complexity of the workflow:

For sequential tasks, CrewAI provides an excellent abstraction layer for defining roles and passing tasks down a line. For complex, non-linear workflows with loops and conditional logic, we prefer LangGraph. It treats the multi-agent system as a state machine, offering immense control over the flow of execution.

Production Considerations

When deploying multi-agent systems, observability is critical. You must trace every LLM call, token usage, and tool execution. We implement strict guardrails to prevent infinite loops (e.g., the Critic rejecting the Writer indefinitely) and utilize semantic caching to reduce API costs on repeated queries.