Many teams build a multi-agent AI prototype in days but hit a wall in production. This post defines five production challenges (orchestration, per-agent model selection, reliable operation, secure code execution, and monitoring) and shows how Deep Insight, an open source multi-agent system on AWS, solves each.
Read the original at AWS machine learning blog: 5 production challenges in multi-agent systems and how Deep Insight solved them
How OpenSmartRoute helps
The router treats agents, tools and skills as targets in the same catalogue as models. An MCP tool or an A2A agent is one entry; the router picks it when it fits the request and learns from how it performs. The same guard and resource limits apply to a tool's arguments as to a prompt.
For a team that routes through the router, a release like this is one catalogue entry. The new model competes on the next request against what the team already runs, on quality, cost and speed. If it answers well it earns more traffic; if it fails it gets less, with no code change in the app.