AI is scaling faster than ever. Managing agents never kept up.
We are adopting AI systems faster than we are learning to understand or manage them. Agents conduct more work across accounts, environments, providers, and devices than teams can monitor. When the AI systems we rely on every day stop performing or hit runaway spend, we have no clear view of why. What is the root cause? Is it the prompt, the task, the model, the harness? All that is left is trial and error through guesswork or buying more tokens, without addressing the underlying cause.
We've learned this lesson many times in history: you cannot manage or improve that which you cannot measure. Revised AI is a system we developed to tackle these problems for ourselves. By tracking every token, message, model, and action we gain a deep view of agent behavior and results. And the very data that powers our analytics drives automations and real-time alerts.
Every session should be a feedback loop that
improves the next run.
Every new agent is a unique combination of prompts, inputs, harnesses, tools, and constraints. Every time we start a new session, we generate thousands of signals about what works and what needs to change. Across all of these sessions, we are generating valuable data that should unlock greater efficiency and productivity. Yet that very data is hard to gather across AI providers, devices, and deployment models, leaving teams unable to use these critical insights.
We are building Revised AI to turn that fragmented data into a continuous feedback loop for everyone: the people using AI, the organizations reporting on performance, and the agents problem-solving their own work. We structure and analyze sessions, prompts, performance, and agent behavior across all major AI providers and turn these into actionable insights via dashboards for humans and MCPs for AI. This way, each use of AI becomes a continuous feedback loop to improve our systems before the next session.
Human attention is rapidly becoming the
bottleneck to scaling AI.
If teams are going to unlock leaps in productivity through AI, then they need to be freed from having to constantly supervise their agents. As everyone's attention gets swarmed by agents, focus becomes fragmented, projects slow down, and people sit with the uneasy feeling that there are deeper problems lurking below the surface.
Revised AI is a system that is designed to reverse this dynamic. By combining analytics, automated supervision, and real-time alerts, agent behavior is corrected in real-time before people need to be notified, leaving alerts for when people's judgment is needed most. That way, teams are free to focus on bigger ambitions knowing that their agents and systems are supervised and revised in real-time.