Designing Multi-Agent Workflows with LLM APIs

When I first started building with LLM APIs, most of my projects used one large prompt and one model response. It worked for demos, but it did not hold up for larger tasks. As soon as I needed memory, validation, retrieval, and tool calls in one loop, the system became difficult to reason about.

A better pattern is to design multi-agent workflows where each agent has one narrow responsibility. Instead of one overloaded prompt, I now use role-specific agents: planner, retriever, executor, and critic.

1) Start with a task graph

Before writing prompts, I define a task graph. Nodes represent operations; edges represent information flow. This helps answer key questions:

• Which components are deterministic?
• Which components need model reasoning?
• Where should validation happen?

If the workflow is written as a graph, debugging becomes easier because failures can be localized to one node.

2) Keep agent interfaces strict

Agent autonomy sounds exciting, but unconstrained agents create unpredictable outputs. I prefer strict input-output contracts:

export type PlannerOutput = {
  goal: string;
  subTasks: string[];
  dependencies: Record<string, string[]>;
};

Schema-first design keeps downstream systems stable. If one agent changes shape, the parser catches it before state is mutated.

3) Separate context by role

Not every agent needs the full conversation history. A retriever agent benefits from query context, but an evaluator agent may only need expected output criteria and the generated result. Smaller context windows reduce cost and often improve precision.

4) Use a critic, not a loop forever

I avoid unconstrained self-correction loops. Instead, I cap execution at one generation pass and one critic pass. If quality remains low, I return a partial answer with confidence metadata. This is safer than endless retries.

5) Instrument everything

Observability is what makes multi-agent systems practical. I log:

• Prompt versions
• Model identity and latency
• Token usage by node
• Validation failures
• User-visible errors

Without this telemetry, teams confuse model behavior with pipeline bugs.

6) Build for fallbacks

When an agent fails, route to a deterministic fallback whenever possible. For example, retrieval can fall back to lexical search if embedding search times out. User trust improves when systems degrade gracefully.

Closing thought

Multi-agent design is less about hype and more about software architecture. If each agent is treated like a service with contracts, observability, and bounded responsibilities, LLM systems become testable and maintainable.