Building Multi-Agent Systems: Lessons from Production

Multi-agent systems sound great on paper. In practice, shipping them to production is a different challenge entirely. After building platforms like MSPPro and Webspike — both of which rely heavily on agent orchestration — we've developed a clear picture of what works and what breaks.

The Orchestration Problem

The hardest part isn't building individual agents. It's deciding how they hand off work to each other. We've tried centralized orchestrators (one agent routes everything) and decentralized meshes (agents negotiate directly). For most business use cases, a hierarchical model wins: a coordinator agent delegates to specialists, each scoped to a narrow task.

Failure is Inevitable — Design for It

Agents fail. An API times out, a model returns garbage, a tool call hits a rate limit. The difference between a demo and a production system is how gracefully you handle this. Every agent in our stack has:

• A defined fallback behavior
• A maximum retry budget
• An escalation path to a human operator

Keeping Humans in the Loop

For high-stakes actions — sending emails, modifying records, triggering billing — we always insert a confirmation step. The agent drafts the action, a human approves it. This isn't a limitation; it's a feature. Clients trust the system more when they see the agent pausing to ask.

What's Next

The next frontier is agents that learn from operator corrections. Every time a human overrides an agent decision, that's a training signal. We're building feedback loops that tighten agent accuracy over time without full retraining cycles.