Use Case
Multi-agent systems: when one AI is not enough
How to architect AI systems where specialized agents collaborate on complex workflows. The patterns, pitfalls, and production realities.
A single AI agent can handle a single workflow well. But real business processes span multiple systems, require different types of reasoning, and involve handoffs between specialized roles. Multi-agent systems mirror how human teams work: each agent has a defined role, they communicate through structured messages, and an orchestrator ensures the workflow progresses correctly.
When you need multiple agents
A single agent works when the task is self-contained: answer a question, process a document, classify an input. Multiple agents are needed when the workflow has distinct phases requiring different capabilities: research, then analysis, then action, then communication. Forcing a single agent to do all of these leads to longer prompts, worse accuracy, and harder debugging.
The sales pipeline is a clear example. The research agent is optimized for web search and data extraction. The enrichment agent is optimized for scoring and classification. The CRM agent is optimized for API interactions. The outreach agent is optimized for writing. Each does one thing well.
Orchestration patterns
Sequential pipeline: agents run in order, each passing output to the next. Best for linear workflows like lead qualification. Parallel fan-out: multiple agents work simultaneously on different aspects, then results are combined. Best for research tasks. Supervisor pattern: a coordinator agent decides which specialist to invoke based on the task. Best for support routing.
We use LangGraph for most multi-agent orchestrations because it provides stateful, graph-based agent management with built-in persistence and human-in-the-loop support. The graph structure makes complex workflows debuggable and observable.
Production realities
Multi-agent systems in production need observability, error handling, and cost management that proof-of-concept demos skip. Every agent call costs money (LLM tokens). Every network call can fail. Every handoff can lose context. Production systems need retry logic, circuit breakers, cost budgets per workflow, and comprehensive logging.
We build these systems to be maintainable, not just impressive. That means clear agent boundaries, typed message contracts between agents, and dashboards that show exactly what each agent is doing, how long it takes, and how much it costs.
FAQ
Fair questions.
Ask us directlyDo I need a multi-agent system or will a single agent work?
Start with a single agent. If the task is self-contained and the agent performs well, you are done. Add agents when you hit accuracy ceilings, when the prompt gets too long, or when the workflow has distinct phases requiring different capabilities.
What frameworks do you use for multi-agent systems?
LangGraph is our primary framework for stateful multi-agent orchestration. We also use LangChain for simpler chain-based workflows and custom frameworks when off-the-shelf does not fit the use case.
How do you manage costs in multi-agent systems?
Per-agent and per-workflow cost budgets, model selection by task complexity (use cheaper models for simple tasks, expensive models for complex reasoning), caching for repeated queries, and dashboards that track cost per workflow execution.
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