Human Approval Middleware for AI Agents

AI agents autonomously executing tasks without clear human oversight is risky. Build a lightweight middleware layer that ensures humans explicitly approve critical agent actions before execution, creating audit trails and preventing costly mistakes.

The explosion of agentic AI frameworks—LangChain, AutoGen, CrewAI—has outpaced the tooling for governance, and enterprises adopting these stacks are already hitting compliance and liability walls that pure-autonomy architectures can't address. No clear incumbent owns this specific layer, though Humanloop and Weights & Biases orbit adjacent problems around observability and evaluation. The $2k–15k/mo band is believable as a developer-tier SaaS with seat-based or action-volume pricing, but it likely represents a ceiling unless the product moves upmarket into enterprise compliance workflows where budgets are meaningfully larger. The biggest risk is that the major agent orchestration frameworks build approval hooks natively—LangGraph already has interrupt and human-in-the-loop primitives—making a standalone middleware layer redundant before it reaches real distribution.