Search for a command to run...
Series
How we built Rynko — a validation gateway and document generation platform for AI agents. Architecture decisions, MCP integrations, performance benchmarks, and lessons from shipping infrastructure that agents use autonomously.
Madrigal Pharmaceuticals turned production failures into their evaluation pipeline. They built this custom on LangSmith. Flow gives you the same feedback loop as infrastructure.
Your ESLint config catches 200 rule violations. Your tests pass. Coverage is above 80%. And the PR still has a function that does four things, catches generic errors, and string-concatenates a SQL query. Rules caught the mechanics. Nobody caught the architecture.
An AI agent receives a trade document. It needs to extract the HS codes, verify them against the Harmonized Tariff Schedule, check the vendor against an approved list, and flag anything that doesn't match. Before today, that was three systems and a lot of glue code. Now it's one pipeline with one API call.
Microsoft open-sourced a runtime governance platform for AI agents. It's the most comprehensive behavioral governance framework I've seen. Here's how Flow complements it by adding output validation to the pipeline.
IBM is solving real-time data for agents. AWS is solving inference speed. Both are foundational. Here's how Rynko Flow adds an output validation layer to complement what they're building.
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.
