Madrigal Pharmaceuticals turned production failures into their evaluation pipeline. They built this custom on LangSmith. Flow gives you the same feedback loop as infrastructure.
Flow validates agent outputs against schemas and business rules. But when 60% of agents fail the same rule on first attempt, the gate should be telling you why — and helping you fix it.
AWS published a framework for securing autonomous AI agents. We mapped every scope and security dimension to what Flow does today — and where the gaps are.
Your LangGraph agent works great in demos. But in production, every node's output needs to be validated before the next node acts on it. Here's how to add a validation step without writing custom checking logic.
CrewAI makes it easy to build multi-agent teams. But when your agents produce structured data — orders, invoices, reports — who checks if the data is actually correct before it reaches your systems?
Your agent writes great code. But when it generates an invoice, a support ticket, or a customer order — how do you know the data is correct before it reaches your database?
The standalone npm package served us well, but remote MCP is simpler, faster to set up, and works everywhere.
