What is it?
Multi-agent systems orchestrate multiple LLM-powered agents that collaborate to solve complex tasks. Each agent has a specialized role (researcher, coder, reviewer, planner) and they communicate through structured message passing. Frameworks like AutoGen, CrewAI, and LangGraph provide the orchestration layer.
Why does it matter?
Single-agent LLM applications hit a ceiling with complex tasks that require different types of expertise. A multi-agent approach mirrors how human teams work: one agent plans, another researches, a third implements, and a fourth reviews. This decomposition can produce better results than a single prompt handling everything, especially for tasks requiring diverse knowledge and iterative refinement.
Trade-offs
Strengths:
- Breaks complex tasks into manageable, specialized subtasks
- Different agents can use different models optimized for their role
- Iterative agent-to-agent feedback improves output quality
- Mirrors natural team workflows and division of labor
- Framework ecosystem is growing rapidly (AutoGen, CrewAI, LangGraph)
Limitations:
- Debugging multi-agent failures is genuinely difficult
- Agent-to-agent communication can spiral into infinite loops
- Total token cost multiplies with each agent in the chain
- Latency compounds — each agent handoff adds response time
- Reliability at scale is still an unsolved research problem
Our take
Multi-agent systems are at Assess because the gap between demo and production is still wide. We’ve built internal prototypes that produce impressive results on happy paths but fail unpredictably on edge cases. The debugging story is terrible — when three agents collaborate and produce a wrong answer, figuring out where it went wrong requires tracing through multiple conversation logs. Worth experimenting with for well-scoped internal tools, but not ready for customer-facing production workloads.