Why AI Coding Agents Need a Control Plane

The first wave of AI spend tools focused on API calls. That still matters, but coding agents introduced a different problem: long local sessions that read files, run tools, repeat work, and carry stale context.

A team can spend hours with Claude Code, Codex, or Cursor and still have no shared record of what happened, what got expensive, or which behavior should be fixed before the next session.

That is why Prismo starts locally. PrismoDev runs in the repo, watches for waste patterns, and keeps the privacy boundary clear: code, prompts, stdout, stderr, and file contents stay on the machine.

The Prismo connector turns local agent work into a cloud workspace. It syncs safe aggregate metrics, live guardrail events, repair status, and verified savings so teams can see what is happening while work is still in motion.

The dashboard is not just a chart after the fact. It shows connector status, repair queue, live controls, top waste causes, and whether Prismo has evidence that a fix saved tokens or dollars.

Paid workspace sessions make that history durable. Teams can create a session for a repo, rename it, switch to a new investigation, and come back later with context intact.

Avoidable waste is useful for diagnosis, but saved tokens and saved dollars are the launch metric. Prismo focuses on guardrail events and repairs that can be verified against later sessions.

That means the product has to show what it prevented, when it prevented it, and whether future sessions improved. A vague estimate is not enough for a team trying to trust an agent-control system.

The goal is simple: make AI coding agents measurable, repairable, and accountable without making developers change how they code.