Notes from the path of every agent action.
Engineering, security, and research on governing AI agents — scope, guard, log, and everything we learn running our own company on the product.
What is an AI agent control plane?
A control plane sits in the path of every action your AI agents take — deciding what each one can do, enforcing your policy before the call runs, and recording an immutable trail. Here's the model, end to end.
Rajesh R
CTO · 8 min read
Why raw API keys are the biggest risk in your AI stack
A raw API key handed to an agent is a bearer token with no scope, no expiry, and no record. It's the single most dangerous primitive in modern AI systems — and the easiest to remove.
Governing MCP servers: scope, guard, log
MCP makes it trivial to give an agent new tools — and trivial to hand it powers nobody reviewed. Here's how to put any MCP server behind scope, guard, and log without slowing developers down.
Building an audit trail for autonomous agents
Reconstructed-after-the-fact logs don't survive an audit. Here's how to build an immutable, attributable, exportable trail for agents — recorded in the path, as actions happen.
RBAC for AI agents: roles, not keys
Per-agent permissions don't scale and don't survive an audit. Role-based access control — the model we already trust for humans — is how you govern a fleet of agents without drift.
Put every agent your company runs under one policy.
Watch HiveKey scope, guard, and block a live action on your own agents — 30 minutes, no slides, no commitment.