Updates, tutorials, and deep-dives on building AI workflow tooling.
RSS FeedThe thing that makes your agent good isn't the model — it's the memory, context, and learned behavior it accumulates over time. When all of that lives in a vendor's database, switching doesn't just move your data. It resets your agent to day one.
A customer asks you to delete their data. Your retention policy says conversations expire after 90 days. A legal hold says keep some of them. None of that is your call if your agent's data lives in a vendor's black box. Here's the fix.
The EU AI Act asks for human oversight and record-keeping over high-risk AI systems. If your only control is an instruction in a prompt, you don't have an answer. Here's what real oversight looks like in an agent.
Before an AI agent goes anywhere near production, someone will ask for the record of what it decided and did. If that record lives in a vendor's system, you get whatever they choose to give you. Here's why the audit trail has to be yours.
An AI agent's memory is often the most sensitive data it touches — and the part teams control least. Here's the problem with renting your agent's memory, and what owning it changes.
Prompts are suggestions. When you need an AI agent to actually stay within limits, the rules have to live outside the model — in the system that runs its actions. Here's how that works in FlowDrop.
The moment you let an AI model run its own tools, the decision and the action happen in the same breath — with no chance to check it. Here's the problem that creates, and how FlowDrop closes the gap.
A visual workflow editor that your backend can own. Today we're releasing FlowDrop 1.0, a drop-in, backend-agnostic component for building AI workflow UIs.
Most AI workflow tools ship with their own cloud backend. Here's why that's a problem, and what a better approach looks like.
