What are the biggest challenges in multi-agent system development?

From what I’ve seen, the biggest challenge in multi-agent AI systems is not getting agents to “talk” to each other — it’s getting them to collaborate reliably and predictably in production. Some major problems developers usually face are: * Coordination complexity — agents may conflict, repeat work, or lose track of responsibilities * Context sharing — maintaining consistent memory and state across agents is difficult * Error propagation — one weak agent can create cascading failures through the workflow * Hallucinations between agents — agents can reinforce incorrect assumptions from each other * Latency and cost — multi-agent systems can become very expensive and slow quickly * Debugging difficulty — tracing why a workflow failed becomes much harder compared to single-agent systems * Tool orchestration — managing permissions, actions, retries, and dependencies across agents is complex * Evaluation — measuring whether the collaboration is actually improving outcomes is still an unsolved problem for many teams In practice, many production systems end up using a “controlled multi-agent” setup instead of fully autonomous agents. Usually there’s: * one orchestrator/planner agent * specialized worker agents * deterministic business logic around them * strong guardrails and validation layers I think the industry is still early here. Multi-agent demos look impressive, but making them stable, cost-efficient, and production-ready is a very different challenge altogether.