Today's issue is about a converging trend, and how we as business leaders turn it to our advantage. Over the last six weeks, all four major AI platforms shipped updates that landed in the same place.
Microsoft made Copilot Cowork generally available worldwide on June 16. Google shipped a Gemini Spark desktop app on June 30. Claude Cowork opened its web and mobile beta on July 7. OpenAI launched ChatGPT Work on July 9.
Each of them does the same basic thing. You give it a goal, it works on its own across your files and connected apps, and it hands back a finished document, spreadsheet or deck.
As leaders we are navigating a complex landscape and constantly working out how to use AI to its full capability, and how to turn it into leverage for ourselves and our teams. That is the lens I want to apply here.
So, we will start with the high-level features these platforms now provide, which is where the trend becomes visible. Then we will walk through one example of what it means to actually use them, and what it means to have an agentic AI operating system of your own.
All four support MCP, the same open standard for connecting outside tools, so your setup work moves with you.
Core insight: The constraint has moved from the tool to the setup. Any of these platforms will now carry real work end to end, so what separates two leaders with the same headcount is whether one of them has built a system to hand work to. That is where the leverage sits: more output from the team you already have, and work you used to turn down because nobody had the hours for it. None of it requires code. It requires knowing what good looks like and being willing to write it down.
The task
I have just finished an AI audit of a client. Now I need the deck that synthesizes what I found.
Every business leader builds some version of this. The findings deck, the project readout, the end-of-engagement report. The pattern is same: material is scattered across everywhere you worked, and it has to become one argument in your own voice.
For this audit the material sits in four places. Emails with the client over several weeks. Notes from three calls, captured in Granola. A client folder holding their org chart, the tools they told me they use, and my working notes. And my own audit framework, which is the same every time.
What I actually type is this.
Build the findings deck for the client audit. Pull from the client folder, our email threads, and the Granola notes from all three calls. Tell me what is thin before you build. Every claim about their current state has to trace back to something they told me. Flag anything you had to assume.
In the ask above, I did not tell the agent who I am, what my audit framework is, how my decks are laid out, what my voice sounds like, or which tools it may use.
It already has all of that. That is the operating system, and everything below is what happens because it exists.
What the agent does
Eight steps run between that request and the finished deck. Four of them happen before it writes a single slide.
Step four is the one worth pausing on. It came back before building and told me that one number the CFO gave me in an email contradicted what the ops lead said on the second call. It left that for me, because that one needs a human.
Then I do the only job I have left, which is deciding whether it is right.
What that task just showed you
Five parts had to be in place, and the agent used every one of them.
Two of them are worth more than one line.
Context runs in two layers. The global layer is what stays true across everything you do, in instruction files that load every session. The task layer is the material for one piece of work, sitting in the folder for that client. And context lives wherever you actually worked, so on an audit most of it is in email and call notes rather than in files.
The loop is where the system gets built. The first run is rarely right, and the fix is rarely the deck. Check what came back against what you said good looks like, then send the correction to whatever actually caused it. A vague goal or a missing detail is the input. A guardrail that got ignored belongs in your standing instructions, so it holds every time after this. A deck that came back in the wrong shape is the skill. Fix the document and you have one better document. Fix the component and every run after this one improves.
Where the leverage actually shows up
Skills are how a task stops being rebuilt. My presentation skill is the accumulated answer to "how does a findings deck of mine work," saved once and reused on every engagement. The test for whether something should become a skill: you have run it three times and corrected the same thing each time.
Schedules remove you from the trigger. Put those together and a task you do weekly stops being on your list. It arrives finished, and your only job is the review.
And the whole setup transfers. Swap the findings deck for a prospect list and the trace is the same. Different task, identical architecture.
How much autonomy to give it
Autonomy is a dial, and most people leave it at the wrong setting in both directions.
Start every new task at "check with me." Move it up only after several clean runs. Two things stay there permanently, however well it performs: anything that leaves your company, and anything that spends money.
Key takeaway: An AI operating system is something you run, and it asks of you what a new team asks: clear direction, the right context, a standard for what good looks like, and feedback that carries forward. Build it once and it compounds across every objective you are accountable for. That is what makes this worth your time this quarter.
Want this built for your business?
If you want help setting up an agentic AI operating system for yourself or your company, book a 30-minute intro call.
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See you next week - Purti
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