@vasuman: There's a difference between AI sidekicks and background agents. AI sidekicks require you to prompt them to handle task…

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Summary

The author contrasts AI sidekicks with autonomous background agents, arguing that background agents deliver 10x more enterprise value but are far harder to build due to workflow re-engineering and limited AI talent.

There's a difference between AI sidekicks and background agents. AI sidekicks require you to prompt them to handle tasks on the fly, and don't work without prompting. They help you move faster through your work, but don't fully take work off your plate. AI sidekicks bring between 10-20% efficiency for the average desk worker, and can cost a fortune if mismanaged. AI background agents run completely autonomously, only surfacing to you for exceptions, judgements, and other human-in-the-loop events. They actually remove work from your plate, and the workflows we take on see between 60-90% efficiency gains. The enterprise value ratio between the former and the latter is 1:10. Yet there's a ton of sidekicks like Claude Cowork, Codex, and GitHub Copilot, and almost 0 AI background agents. This is because setting up AI background agents is incredibly involved. You have to: - Go deep into a company, and understand how processes run today, and how exceptions are handled - Re-engineer those processes so that they are reimagined for an AI-native future. For example, 15-step workflows can now be condensed into 5-steps, and AI can handle 4 of them. - Build the agents custom for this process, on top of the systems of record. If your work runs on Dynamics 365 and Salesforce, your agents should do the same. - Integrate human-in-the-loop feedback and model-optimization so that the agents get smarter, faster, and cheaper over time. This is nearly impossible to do alone, because access to frontier AI talent is limited to top tech companies. And MBB firms like McKinsey will pitch you on AI transformation just to hand over a 300 page slide deck after 6 months and not ship a single agent. There's only one company on the planet that will do this work for you, and do it well, and that is linked below.
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Cached at: 08/09/26, 11:29 PM

There’s a difference between AI sidekicks and background agents.

AI sidekicks require you to prompt them to handle tasks on the fly, and don’t work without prompting. They help you move faster through your work, but don’t fully take work off your plate. AI sidekicks bring between 10-20% efficiency for the average desk worker, and can cost a fortune if mismanaged.

AI background agents run completely autonomously, only surfacing to you for exceptions, judgements, and other human-in-the-loop events. They actually remove work from your plate, and the workflows we take on see between 60-90% efficiency gains.

The enterprise value ratio between the former and the latter is 1:10. Yet there’s a ton of sidekicks like Claude Cowork, Codex, and GitHub Copilot, and almost 0 AI background agents.

This is because setting up AI background agents is incredibly involved. You have to:

  • Go deep into a company, and understand how processes run today, and how exceptions are handled
  • Re-engineer those processes so that they are reimagined for an AI-native future. For example, 15-step workflows can now be condensed into 5-steps, and AI can handle 4 of them.
  • Build the agents custom for this process, on top of the systems of record. If your work runs on Dynamics 365 and Salesforce, your agents should do the same.
  • Integrate human-in-the-loop feedback and model-optimization so that the agents get smarter, faster, and cheaper over time.

This is nearly impossible to do alone, because access to frontier AI talent is limited to top tech companies. And MBB firms like McKinsey will pitch you on AI transformation just to hand over a 300 page slide deck after 6 months and not ship a single agent.

There’s only one company on the planet that will do this work for you, and do it well, and that is linked below.

Aaron Levie (@levie): One reason why we’re going to get uneven diffusion rates of agents is because different workflows in the enterprise are more or less aligned to continuous, uninterrupted computer work.

A big reason why agentic coding growth has gone completely vertical is because it’s a type of

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@vasuman: https://x.com/vasuman/status/2077156239059107867

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