In my cohorts, building a Claude skill is the quick part. Running dozens of them across a team, fast, without losing control, is where teams get stuck.

It’s tempting to see speed, scale, and governance as a trade-off: move fast and lose track of what’s running, or lock things down and slow everyone down. In my experience, you don’t have to choose. Treat skills the way a well-run business treats its playbooks: one approved version, a named owner, and every update reaching everyone at once.

That’s how I run nearly 100 skills and 17 agents across my business. Every skill I change gets a version number before anyone else runs it, and I can account for every one. The payoff is a portfolio of skills that accelerates the whole business, with governance intact.

TL;DR

• Speed, scale, and governance can coexist. Releasing skills, rather than sharing files, gets all three.

• Shared skill files drift. Copies multiply, get edited, and nobody can say which version produced an output.

• One source, one release. Skills change in one place and reach everyone as a versioned plugin.

• The version number is the contract. An edit is a patch, a new skill a minor, a removal a major.

• I’m showing all of it live, free, this Friday, November 6.

Why copies drift

Someone on a team builds a good skill. A teammate copies the file. Someone else improves their copy.

A month later, three versions are in use. Which one wrote this morning’s client reply? Who changed it last? What is it allowed to touch? Nobody can say. The hard part is that nothing breaks loudly. Each copy keeps working, so the drift stays invisible until an output goes wrong.

When it’s only you, that’s a nuisance. Once a team depends on those skills, it’s a governance problem.

The pattern: one source and a release

My skills and agents live in one source folder on GitHub, which keeps a record of every change. That’s the skill source: the only place a skill is ever edited.

When a change is ready, it’s released to a separate plugin folder. That one holds finished work only, and it’s what gets installed. A plugin is a package of skills and agents Claude installs in one step. A marketplace is the GitHub location your team installs it from.

The split matters because the people using your skills don’t need your skill source. They need what you’ve finished, at a known version, and nothing half-built.

• Try this (10 minutes). List where your team’s most-used skill lives today. Count the copies.

The version number is the contract

Every release gets a version, and the number says what kind of change it was:

• Patch (1.1.0 → 1.1.1). A skill was edited.

• Minor (1.1.1 → 1.2.0). A skill was added.

• Major (1.2.0 → 2.0.0). A skill was removed, so someone who relied on it loses something.

A teammate who sees “2.0.0” knows to look for what’s gone before they open anything. The release tells people what changed, so nobody has to ask.

Five rules that keep it governed

The release pattern gives you control. These rules turn it into governance:

• Every change is reviewed. In my business, a skill change gets reviewed before it can be released. A teammate, or an AI reviewer, reads it first.

• Every skill change has revision history. Each change is saved in the skill source’s history, and each release names the version it came from. Any output traces back to the exact skill that produced it.

• The plugin stays private. Only people you add can install it, and only a small group can release to it. Every release is somebody’s deliberate call.

• Decide who gets each skill. Nothing ships until it’s assigned to a plugin or kept back. The release plan records who gets what.

• Record what’s running. My AI Registry links every workflow to the skills and agents behind it, so I can see what’s released, which workflow it serves, and who owns it.

None of these rules are Claude-only. ChatGPT Business and Enterprise read the same skill format, so the skills and the rules carry over. Only the release mechanics differ by platform.

Leader Lens

The release step adds friction between “I improved the skill” and “the team has it.” That friction is the point. It’s what lets a leader say yes to the next skill instead of pausing to audit the last one. Decide who can release before you decide what to release, and the rollout keeps pace as the team grows.

Your next two weeks

Pick the one skill your team copies most. This week, find every copy and name a single owner. Next week, move it to one source and agree who can change it. You’ll know it worked when someone asks “which version is this?” and the answer takes seconds.

See it live this Friday

I’m teaching this as a free 45-minute Lightning Lesson on Maven on Friday, November 6, at 12:30 PM ET: How to Govern Claude Skills at Scale.

Using the setup I run my business on, I’ll add a skill, change one, and remove one. Each change gets released and arrives on a teammate’s setup with its version number. Then I’ll open the registry that records what’s running and who owns it. The last 15 minutes are for your questions, and the recording goes to everyone who registers.

Save your seat →

If you want to build skills worth releasing on your own work, that’s what my course, Claude for Leaders: Accelerate Your Business, covers. Cohort 8 starts Monday, November 9.

If your team already shares skills differently, I’d like to hear how. Bring it to the Q&A, or reply to this email.

Stay curious. Stay hands-on.

James

I start there on purpose. An AI strategy can only be as good as your picture of the work. And AI is changing that work in every function at once, from sales and finance to delivery and support.

The leaders I’ve seen get results from AI know their work well enough to keep redesigning it as the tools improve.

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What an AI-first mindset is

Here’s how I define it. An AI-first mindset is a habit. For every piece of work you do, you ask: if I were designing this today, knowing what AI can do, how would it run?

You start with the work and bring in the tool second. Keep asking, because the answer changes every time the models improve. A workflow you settled last quarter may deserve a fresh look this one.

Judgment stays with people. Some workflows will run on their own, and others you’ll work alongside. Either way, AI proposes, and you decide.

In practice, AI-first means every workflow gets a fair hearing, including the ones you’ve run the same way for years.

Start with the value chain

When I teach AI strategy to leaders at Berkeley Haas and in my cohorts, we start with the value chain, before any tool.

Michael Porter gave leaders the value chain in Competitive Advantage (1985). His insight was that competitive advantage lives in the specific activities a firm performs to design, sell, deliver, and support what it offers.

Porter sorted those activities into two groups. Primary activities create and deliver the offer, such as operations, marketing and sales, and service. Support activities make them possible: your people, technology, procurement, and the firm’s infrastructure. Each activity drives your costs or shapes the value a customer sees. The links between activities matter just as much.

That same year, Porter and Victor Millar wrote in Harvard Business Review that information technology was changing the value chain itself: how each activity gets done, and how the activities connect. AI is doing the same thing now, and it's reaching work that used to require a person, like research and first drafts.

That’s why AI strategy has to work at the level of activities. AI doesn’t transform “marketing.” It transforms how you research a prospect, draft a proposal, and review the pipeline on Monday morning.

Where AI advantage comes from

Porter drew another line that matters for every AI plan. In “What Is Strategy?” (HBR, 1996), he separated operational effectiveness from strategy.

Operational effectiveness means performing the same activities better than your rivals. You need it, and your competitors will copy it, so the gains don’t last. Strategy is a different kind of choice. In Porter’s words, “Competitive strategy is about being different. It means deliberately choosing a different set of activities to deliver a unique mix of value.”

When every firm buys the same models to speed up the same activities, everyone gets faster together. That’s operational effectiveness at scale, and no one pulls ahead.

Advantage comes from using AI to reshape your own value chain. You perform activities your rivals don’t, or you perform shared ones in a way that runs on your know-how. Porter called the way activities reinforce each other fit, and he argued fit is what makes a strategy hard to imitate. A competitor can buy the same AI. It can’t easily copy a whole system of workflows built around how your firm creates value.

Competitive advantage shows up in the P&L

In my cohorts, I put up a slide built on Joan Magretta’s Understanding Michael Porter, her guide to his work. In Porter’s framework, competitive advantage links three things: the value you create, how you create it (your value chain), and how you perform (your P&L).

Advantage shows up in the numbers. Compared with rivals, you run at a lower cost, charge a premium price, or both. Activities in your value chain drive both levers.

The other half of that slide comes from Felix Oberholzer-Gee’s Better, Simpler Strategy, and I find it especially useful for AI. He calls it the value stick:

• Willingness to pay. The most a customer would pay. Raise it, and you create customer delight.

• Price. What the customer actually pays. The gap between price and cost is your margin.

• Cost. What you pay employees and suppliers.

• Willingness to sell. The least an employee or supplier would accept. Lower it by improving the work, and you create employee satisfaction and supplier surplus.

The value a firm creates is the distance between willingness to pay and willingness to sell. You can widen it from either end. If you want to go deeper, Oberholzer-Gee lays out the value stick in his HBR article, “Eliminate Strategic Overload.”

So when I look at a workflow to reimagine with AI, I ask what it moves. Take proposal drafting. If AI cuts the first draft from a day to an hour, that lowers cost. If the hours saved go into tailoring the proposal to the client, that can raise what the client is willing to pay. If the person drafting spends less time on boilerplate and more on thinking, the work gets better too. If a workflow moves none of these, it’s activity, and it won’t show up in your results.

I tell the leaders in my cohorts the same thing every time: this isn’t a science project. We’re using AI to deliver outcomes in the P&L.

The map becomes a strategic canvas

It all starts with seeing your chain. What do you offer? Which processes deliver it? Inside each process, which workflows run, and in what order?

A process is an outcome your business delivers, like winning new clients. A workflow is one repeatable piece of work inside it, like researching a prospect before the first call.

Order matters more than it looks. When workflows follow the sequence the work actually happens in, you can see how value moves through the business, and where it gets stuck.

Once I could see my workflows that way, I could start making choices about them. I sort each one into a group:

• Reimagine. Work that could run in a different way with AI. In my business, email triage, research, and first drafts all landed here.

• Protect and deepen. Work where your judgment and relationships create the difference clients pay for. Aim AI at making the people who do this work better, and keep investing in them.

• Hand off. Work that has to happen but doesn’t set you apart. Delegate it to a partner, a platform, or an agent, and keep your attention on the work that does. Porter put it plainly: “The essence of strategy is choosing what not to do.”

The map shows one more thing: gaps. A process with no workflows behind it is either work no one owns, or an opportunity waiting to be built.

You can’t safely hand off a workflow you haven’t mapped, because you can’t tell whether it’s core. The groups also shift. Work that’s core today can become routine as models improve, so the map must stay current.

This also changes the old build-or-buy question. You now answer it workflow by workflow: build what’s core to your know-how, buy what a platform already does well, and partner on the rest. You can only make those calls once you can see the workflows.

Your know-how lives in your workflows

Much of a firm’s intellectual property lives in how its people do the work: the first questions a senior consultant asks, or the checks a finance lead runs before anything leaves the building.

When that knowledge stays in someone’s head, no one else can improve it or build on it with AI. When that person leaves, it goes with them.

Once you name a workflow and link it to the outcome it serves, the firm keeps that know-how as an asset. Link it to the skills and agents that power it, and you can see where AI is doing work in your business, and why.

The method I teach: understand the work, then catalog it

In both of my cohort courses, Hands-on Agentic AI for Leaders and Agentic AI for Claude Builders, students map their work first. In the first session, they catalog it in what I call an AI Registry. They practice building skills and agents along the way, and the registry ties each one to the workflow it serves. By the last week, it records every workflow they built.

I run my own business the same way. My AI and I catalog every line of business, process, and workflow. As of this week, that’s 27 processes and 77 workflows, each with a status. Every workflow page links to its process and to the skills and agents behind it. I don’t write the pages by hand. My AI drafts them with me as the work happens, and I check that they say what I mean.

The registry is the page I open most days. When I start a new workflow on Claude, it first opens a registry page linked to the process it strengthens. When I ask my AI which workflow to design next, it follows those links and answers from my own chain of work. Without them, I’d get generic advice.

The work is the craft

I built every one of my workflows, and I still find it hard to remember them all. The registry keeps me close to them. Each time I open it, something catches my eye: a process with nothing behind it, or a skill I built that no workflow uses yet.

Understanding the work we do shapes our ability to get the outcomes we want, in business and in life. I believe we need to see, touch, and improve our workflows every day as we practice our craft. AI amplifies judgment, and that judgment comes from staying close to the work.

Start with the workflow that frustrates you most, or the one you’d struggle to explain to a new hire. Either is a good place to ask the AI-first question.

Try this (30 minutes). Pick one line of business. List its processes, then write the workflows inside one process in the order they run. Mark each one: reimagine, protect, or hand off. For each one you’d reimagine, note what it moves: willingness to pay, cost, or the work itself.

See my value chain, then build your own

To see how this works in practice, I showed it in my latest Lightning Lesson, a free 45-minute live session on Maven: Map Every Workflow You Run on One Page. I open my AI Registry and walk through my business as a value chain. I trace one workflow up to the outcome it serves, then ask my AI which workflow to design next. Watch the recording →

If you’d like to go further with me, both of my cohort courses start Monday, November 9:

• Hands-on Agentic AI for Leaders, cohort 21. Four weeks building the skills and agents behind your workflows on Claude, ChatGPT, M365 Copilot, or Gemini Enterprise.

• Agentic AI for Claude Builders, cohort 8. Five weeks building the skills and agents behind your workflows on the Claude platform.

No matter where you start, the ideas are the same. An AI-first mindset starts with the work; the tool comes second. Every firm can buy the same models, so advantage comes from how AI reshapes your own value chain. And it has to show up in the P&L.

So map your processes and workflows. Decide what to reimagine, what to protect, and what to hand off. Then keep the map current as the models improve, because the right answers will change.

You don’t need a course to begin. The 30-minute exercise above is enough to start this week.

Stay curious. Stay hands-on.

James

When to use subagents, and when one Claude with good skills does the job better

TL;DR

• Just use Claude. Anthropic found that a single agent with skills often beats a team of subagents. For you, that agent is Claude itself.

• Splitting work costs context. A subagent is handed a job and a summary. It never sees the full picture.

• Use a subagent for three jobs: heavy reading, parallel work, and fresh-eyes review.

• Everything else is a skill. Task, reference, and orchestration skills cover most business work.

• The test: does this work need to know what we’ve been discussing? If yes, it’s a skill.

You open Claude and set up a team. A marketing agent, a sales agent, a finance agent. Maybe an operations agent to tie it all together.

It feels like good management, but it’s usually the wrong AI design.

This week Anthropic published guidance from its Applied AI team on folding subagents back into the main agent. Across several enterprise deployments, a single agent with skills often beat multi-agent designs in quality. It also cost less and ran faster.

That single agent already exists. It’s Claude, the one you use every day. Your job is to give it great skills.

Anthropic’s article targets developers building production agents. But the lesson applies to every leader setting up Claude for their team. Here’s how I’m teaching it.

First, the key terms

Claude is already an agent. When it searches the web, reads your files, or uses a connector, it’s acting as an agent. You don’t need to build a separate agent first.

The main agent loop is how that works. Claude reads the context, picks a tool, checks the result, and decides the next step. It repeats until the job is done.

A subagent is a second Claude the main one sends off on a task. It gets its own fresh context, does the work, and sends back a summary.

So for anything you set up in Claude, the real decision is narrower: should it be a skill or a subagent?

• A skill is a playbook. It tells Claude how your team does something. Claude loads it when relevant.

• A subagent is a contractor. Claude sends it out to do a separate job and report back.

Think about your next board update. You’d hand your chief of staff the template, because they already know the context. You wouldn’t send it to an outside writer who missed every meeting that shaped it.

The template is the skill. Your chief of staff is Claude, with the full context of your conversation. The outside writer is the subagent: capable, but starting cold.

Skills come in three types:

• Task skills teach Claude how to do one job well, every time. Drafting a proposal, scoring a lead, writing a board update.

• Reference skills give Claude what it needs to know. Your brand voice, pricing rules, product specs, policies.

• Orchestration skills run an end-to-end workflow. They sequence the steps and call on task and reference skills along the way.

Why the org chart design breaks

Systems tend to mirror the organizations that build them. That’s Conway’s law, and Anthropic sees it often in enterprise agents. Each team gets its own agent, and the architecture ends up mirroring the org chart.

Three things go wrong:

• Context gets lost. Each subagent sees only what you pass it. Details drop out at every handoff, like a game of telephone.

• Instructions conflict. Every team adds its own rules. Anthropic calls the result a “Frankenprompt.”

• Speed suffers. Each handoff is another model running its own loop. One customer saw roughly 2x faster runtime after moving to a single agent.

The higher cost is insight, because one Claude that saw every step can connect the dots in ways a stack of summaries can’t.

Leader Lens

Dividing work by function is a sound management habit for people, but it transfers poorly to AI. Older models needed tasks broken up and handed to specialists. Since Opus 4.5, models plan, check their own work, and handle far more in one loop.

So the leadership question shifts from “who owns this agent?” to “who owns the playbook?” Let teams own their skills, and keep one owner for how Claude works overall.

Why one Claude can handle more now

The old reason to split work was overload. Load every playbook and tool up front, and Claude wades through instructions it doesn’t need for the task at hand.

The answer is called progressive disclosure. It means Claude gets information in stages, only when a task needs it. Think of how a good team onboards a new hire. On day one, they get a list of the playbooks that exist, and they open a specific one only when the work calls for it.

Claude handles skills the same way. It sees a one-line description of each skill and reads the full playbook only when a task needs it. Tools work the same way through a feature called tool search: Claude sees tool names and pulls up the full details only when it’s about to use one.

In Claude’s apps, this happens automatically. You can connect many skills and tools without slowing Claude down. Anthropic cut Claude Code’s system prompt by over 80%, partly by doing this.

The trade-off is that Claude can only pick the right skill if its description is clear. Vague or overlapping descriptions can confuse two skills. A one-line description that names when to use the skill is worth the extra minute.

When a subagent earns its place

Anthropic’s article reserves subagents for specific jobs. Translated for business work, there are three.

1. Too much reading. The task would flood your conversation with material you’ll never look at. Example: “Review these 50 customer interviews and return the top five themes.”

2. Parallel work. Several independent jobs can run at once, each with fresh context. Example: “Research these six competitors’ pricing at the same time.”

3. Fresh eyes. You want a reviewer who didn’t produce the work. Example: “Critique this proposal as a skeptical CFO who hasn’t seen my draft.”

If none of these apply, it’s probably a skill.

The one-question test

Ask this about any task:

Does this work need to know what we’ve been talking about?

• Yes: keep it in the main conversation. Capture the know-how as a skill.

• No, I just want the answer back: it’s a subagent candidate.

For most business work, the answer is yes. Proposals, analysis, planning, and writing all depend on shared context, and one Claude keeps it intact.

Try this (10 minutes). List any subagents, projects, or saved instructions you’ve set up in Claude. Run each one through the test: should it be a skill or a subagent?

Skills first, agents second

This is why my cohorts spend a full week, and then some, on skills before we touch agents. That week is often where leaders get the most value.

My guidance, in my courses and with every leader I advise, is the same. Start by building skills for your specialized tasks, and get the most out of them before anything else. Skills are where your unique expertise lives. They’re how you package what you know so Claude can use it every time.

Writing a skill also asks you to do something. You have to put into words how you actually do the work: the steps, the judgment calls, the standards. It requires the same clarity as good delegation to people.

In my experience, these three skill types cover a surprising amount of work, and orchestration skills carry the most weight. One orchestration skill gives you much of what people expect from a multi-agent system. You get a coordinated, multi-step workflow without a single handoff.

Once you’ve built a few of each, add subagents only for the three jobs above.

The 15-minute sorting exercise

I use this with leaders in my cohorts. It works best with tasks from your real calendar.

1. List five to seven recurring tasks from your actual week.

2. Is it a procedure, reference, or multi-step workflow? Make it a task, reference, or orchestration skill.

3. Is it heavy reading, parallel, or a review? It’s a subagent candidate.

4. Neither? Ask Claude directly. No special setup needed.

Expect most of your list to land in steps 2 and 4.

Keep what holds up

Anthropic’s closing advice is simple discipline: measure, change one thing at a time, and keep what works.

Applied to your setup, that means one rule. Before building a new agent, try the task with one Claude and a good skill. Add a subagent only if the result is clearly worse without it.

So over the next two weeks, take the top orchestration skill from your sort, build it, and run it on real work at least three times.

Once you’ve run the sort, post your top orchestration-skill candidate in the comments. Seeing which workflows other leaders pick is one of the fastest ways to spot your own.

Source: Building agents: When to fold subagents into your main agent loop, Diane Sarkis, Anthropic, October 22, 2026.

A worked example from my course

If you’d like to see it all working together, here’s the example I use in class. In my Build Agentic Workflows with Skills lesson, I teach this framework with one worked example: the Opportunity Decision Brief. You hand Claude a business idea, and it returns a researched go, refine, or no-go recommendation for you to weigh.

It’s the whole post in one workflow. An orchestration skill does the coordinating. Four research subagents work in parallel, and a fifth writes the brief. You make the final call. (My next two cohorts start November 9.)

Opportunity Decision Brief workflow diagram: you clarify the idea, four research subagents work in parallel, a synthesizer writes the brief, and you decide

The dark boxes are where you stay in charge: clarifying the idea at the start and making the decision at the end. Everything in between is Claude’s work.

Below the line for paid subscribers:

• The full worked example: how the skill and its five subagents fit together, mapped to the one-question test

• The six design choices worth copying into your own orchestration skills

• The one piece I’d test folding back into the main Claude, and how to run that test

• The installable files, with setup steps for Claude and Claude Code

• How to adapt it for decisions in your own business

Read more

Nobody can hold that many in their head, so I stopped trying. My AI and I keep them all in one place, and I look there.

That place is my AI Registry. It’s the thing I open most days, and it’s one of my favorite things to teach. Here’s how it works and how I use it.

TL;DR

• Workflows multiply once you work with AI, and nobody can track them from memory.

• An AI Registry catalogs them in three levels: line of business, process, workflow. Each workflow has a status and lists the skills and agents that power it.

• The pages link to each other, and those links are what make the registry useful to your AI.

• I’m walking through mine live, free, on Thursday, October 22.

Name the work first

Once you work with AI, workflows multiply. Email triage, sales pipeline review, coaching session prep.

Each one is a piece of how your business creates value. Unless you capture it somewhere, it lives in your head, and know-how in your head can’t be improved, delegated, or handed to a new hire. Your AI can’t build on it either.

Naming the work also clarifies it: what you actually do, how the pieces link together, and what outcomes they produce. AI tools matter, but they create real impact only when they’re attached to named work and connected to an outcome. Get clear on the work first, and AI can help you build it.

Whether you work alone or lead a team, the result is an institutional record of what you do and how you do it.

So before I build anything, my AI and I name and catalog the work. I don’t write the pages by hand. My AI drafts them with me as we go, and I check that they say what I mean.

What the registry records

My registry is a folder of plain Markdown pages. Three levels form its spine, and each one orders the level beneath it:

• Line of business. What do we offer? Mine are Consulting, Learning, and Coaching, plus shared operations.

• Process. What do we do, and why? Lead Generation, Course Delivery, Student Support.

• Workflow. How do we do it? One page per workflow, with its status and the skills and agents that power it.

Here’s one thread through mine, top to bottom:

Learning (line of business) → Lead Generation (process, owned by my Marketing function) → LinkedIn Prospect Research (workflow), whose page lists the agent that does the research.

Skills and agents don’t get pages of their own. A workflow page links to them where they live, so there’s never a stale second copy.

What one page shows me

At first glance, the registry reads as one page. Top to bottom, it’s a value chain: what I offer, the processes that deliver it, and the workflows inside each one. As of this week:

• 25 processes and 69 workflows. The whole business in one view.

• 55 in production, 13 under development, 1 in backlog. Every build has a place and a status, so “where are we?” has an answer without a status meeting.

• 37 skills and agents that no workflow references yet. A check I run against the registry finds them. Each one raises a useful question: which workflow does this serve? I couldn’t see any of them before the map existed.

• One process with no workflows at all. An empty process is where my next build idea usually comes from.

If you lead a team

Leaders get asked three questions about AI. Where are we using it? What breaks if this tool or vendor changes? Which processes should we invest in next? A registry scoped to the part of the organization you lead answers all three, and because it’s a record rather than someone’s memory, it stays when people move on.

How I actually use it

A few habits, since this is the part people ask about most:

• New work starts as a registry page. When I have an idea for a workflow, my AI writes it into the registry as a backlog page before anything gets built. Then it has an address from day one.

• Status changes as the build moves. When a workflow ships, its page moves to in-production. The page always says where things stand.

• A weekly sweep catches what I missed. An agent scans what I’ve built and flags anything that isn’t in the registry yet, so nothing drifts out of view.

None of this takes much time, because my AI does the writing. My part is the judgment: naming the work well and deciding what’s worth building.

Why your AI should read it too

The registry is plain Markdown, so any AI that can open a folder can read it: Claude, ChatGPT, M365 Copilot, or Gemini.

Underneath the one page, every page links to the others: each workflow to its process and its outcome, and to the skills and agents behind it. Those links are the real value. My AI follows them, which turns the registry into context about my business.

So when I ask, “Given how my business runs, which workflow should I design next to strengthen Lead Generation, and why?”, the answer cites my own pages and proposes something that fits beside what’s already there. Without the registry, the same question gets generic advice. With it, the AI proposes, and I decide.

See mine, live

If you’d like to watch this in action, I’m teaching a free 45-minute Lightning Lesson on Maven on Thursday, October 22: Map Every Workflow You Run on One Page.

I’ll open my registry, trace one workflow up to the outcome it serves, and ask my AI which workflow to design next. Then I’ll show you a small first registry and how my AI and I set one up. You don’t need 69 workflows to start: one line of business and the work inside it is enough.

The last fifteen minutes are for your questions, and everyone who registers gets the recording.

Save your seat →

Bring a line of business you’d like to map. We’ll start there in the Q&A.

Stay curious. Stay hands-on.

James

I’ve gone through the launch posts, the help center, the desktop changelog, and my own account, and pulled out the updates that matter if you build with Claude. For each one, you get what changed, why it matters, and a 15-minute thing to try. The aim is to keep you productive on the platform you’ve already invested in.

TL;DR

• One Claude. Starting September 16, chat and Cowork are one conversation, rolling out plan by plan. Describe the outcome, and Claude decides whether it is a quick answer or a multi-step task.

• One memory. Memory now carries across chats and cloud tasks. A new “sensitive topics” setting is off by default, and it never saves four categories.

• One place your work lives. Since October 6, every new task on Pro and Max runs in Anthropic’s cloud. Your local folders stay on your machine. A project can also read synced sources such as GitHub and Drive from any device.

• Projects changed shape. A project is no longer a folder on your computer. The folder is linked context, and each old project now offers to move its files to the cloud as a one-time upload that is not kept in sync.

• Next step. Five things to check this week, listed under each section.

Six weeks ago, using Claude for real work meant choosing a tool before you started. Chat for a question, Cowork for a task, Design for a visual, and memory that didn’t follow you between them. Anthropic has collapsed that into one product in three releases: unified memory on August 25, the Cowork and chat merge on September 16, and cloud execution for every task on October 6. Projects changed shape along the way.

Each release is small on its own. Together, they change how a leader should set up Claude, what to tell a team about where data goes, and what a task costs. This edition covers the four changes, what each one means for a business user, and a 15-minute thing to try for each. Every claim below comes from Anthropic’s help center, launch post, or changelog, linked where it appears and listed again at the end.

1. Chat and Cowork are one Claude

What changed. The “Chat” and “Cowork” selector in the message box is gone. Anthropic’s help page on the merge has the full rundown, and here is the short version. You describe the outcome, the format you want, and the inputs Claude needs. Claude decides whether that is a two-line answer or a task that searches the web, reads your files, runs code, and returns a finished document, spreadsheet, or slide deck. Anthropic’s stated reason in its launch post: people used both and found deciding where a task belonged frustrating.

Longer tasks keep running after you close the laptop and show up on your phone, where you can answer a question or redirect the work. Several tasks can run at once. Claude Docs and Claude Slides launched in beta the same day, so a report and its matching deck can come out of the same conversation, and you can schedule the whole thing to repeat every Monday.

Why it matters. There is one product to roll out, one to teach, and one to govern. Any conversation can now turn into a task that runs while you’re in a meeting, which is where the time savings come from, and also why the next two controls matter.

One new control. In the prompt window, next to where you type, you pick how much Claude does before checking with you. Manual is the default: Claude asks before each action. Auto lets it work through the task with automated safety checks and no interruptions. It applies to the whole conversation, and you can change it any time. My advice is to start on Manual. You learn a lot about what Claude is doing on your behalf by watching it ask. In either case, Claude asks before permanently deleting any file.

The cost to know. Everything counts toward the same plan usage limit, and a task that searches, reads files, and writes a deck uses more of it than a chat reply. You can see usage in Settings > Usage, and it’s worth a look after the first week.

What carries over. Existing Cowork tasks, projects, connectors, skills, and files all move with you. Two things moved location. Cowork’s global instructions were folded into Instructions for Claude in Settings > Account (Anthropic’s help page calls it Settings > General), so if you had instructions in both places, you now have one set, and every conversation reads it. And Cowork tasks now sit in Recents alongside chats. The web search toggle is gone, since Claude searches when it helps. Research runs from /deep-research or the “+” menu.

Who has it. Pro and Max first, rolling out gradually, so two people on the same plan can see different versions. Team and Free follow “soon” with no date. Enterprise admins get at least 30 days’ notice, and they choose when to enable the Docs, Slides, and Design betas. For organizations that manage Claude Desktop centrally, the desktop changelog names November 10 as the date the unified experience turns on for every organization. Once an account has the new experience, you can't go back to separate Chat and Cowork.

Try this (15 minutes). Open Settings > Account and read your Instructions for Claude from top to bottom. The rules you wrote for Cowork tasks, such as where to save files or which format to produce, now apply to quick answers as well, and the rules you wrote for chat now apply to tasks. Resolve anything that conflicts. Then find the permission control in the prompt window and leave it on Manual for a week before you decide.

2. One memory across chat and tasks, with new controls

What changed. Memory is now a single store that chat and cloud tasks both read and write. Anthropic’s memory help page covers every control; the key ones are below. Claude saves memory as individual topics during the conversation, not as a summary written afterward, and you can ask it to “remember this” explicitly. Everything saved appears as a list of topics in Settings > Memory, where you can read, edit, or delete each one.

Each Claude Project keeps its own memory, separate from other projects and from general chats. A task that ran only on your computer keeps its memory with that task. Memory is fixed when a chat or task starts, so any changes you make apply from your next conversation.

The sensitive topics setting. This is new and worth understanding before anyone on your team turns it on. By default, Claude doesn’t save health, race, ethnicity, religion, politics, gender identity, or similar subjects to memory. A setting called “Include sensitive topics in memory” turns that on going forward. Every save on a sensitive topic then shows a notice above the message box so you can review it. Turning the setting off deletes the sensitive items already saved.

Four categories are never saved, even if you ask: government ID numbers, criminal history, financial account numbers, and immigration status. Anthropic’s rationale, as it told Engadget: “what some consider sensitive, others may consider useful for Claude to remember.” The decision sits with the user, not the admin.

For Team and Enterprise. Owners enable memory in Organization settings > Capabilities, and each member stays off until they opt in. Enabling memory doesn’t enable sensitive topics; each user opts into that separately. Owners can’t view or edit anyone’s memory. Turning memory off at the organization level immediately and permanently deletes every user’s memory data. Memory is unavailable to organizations with HIPAA, public-sector, or custom data-retention agreements.

Two details worth knowing. Deleting a conversation doesn’t delete the memory entries it created. Those are deleted individually from the topics list. Memory is included in data exports, so an export now contains what Claude has learned about you, as well as what you typed.

Try this (15 minutes). Open Settings > Memory and read the topics list from start to finish. Delete anything you wouldn’t want in a data export. If you run a Team or Enterprise plan, decide the org-level memory policy before the merge reaches your organization, because the off switch is permanent.

3. Your work now runs in the cloud

What changed. As of October 6, every new task on Pro and Max runs on Anthropic’s servers, not on your computer. Anthropic’s page on Cowork across web, desktop, and mobile spells out the rules, and they are fair ones. The “Only on your computer” option was removed from Settings > General. Anyone still set to local was moved to the cloud the next time they opened the desktop app, with a notice, and the move can’t be reversed.

Work already in progress stays put. Tasks you started on your computer before October 6 stay there until you finish them, each with a banner saying it runs only on this computer. Scheduled tasks that already run on your computer keep running there. New scheduled tasks run in the cloud.

In practice, a new session and its files are identical on desktop, web, and mobile. Start on your laptop, check on your phone, and finish on the web. Cloud scheduled tasks run even if no device is online. You get a phone notification when Claude finishes or needs you.

Where your files go. Local folders stay on your computer. Claude reaches only the folders you have connected, only through the desktop app, and only while the app is open. When a cloud task needs a file from one of those folders, Claude fetches a copy of that single file and nothing else. Deleting the session deletes the copies under the normal retention practices. Whether conversations help train models still depends on the “Help improve our AI models” setting in Settings > Privacy.

Trusted folders are a separate list. Under Settings > General, the Tasks section lets you manage trusted folders. Trusting a folder on a device lets Claude use it and the folders inside it without asking each time. It doesn’t add the folder to any session or project, and Claude may still ask when you add it to a session. Which folder a project reads is set in that project’s Context, covered next.

Three ways to give a task your files. Open any project, select Add next to Context, and the menu shows the three kinds of sources a cloud task can read.

• Uploads. Files or pasted text, stored with the project. A copy, frozen at the moment you added it.

• Synced sources. A GitHub repository, Google Drive files, a Notion workspace, or another connected app. The project shows “Synced” with a time. A GitHub source pulls the latest version when you select Sync. Drive files refresh automatically when you open the project and can be added only to a private project. Either way, the content is the same on desktop, web, and phone.

• Linked local folders. Desktop only. The folder stays on your computer, carries an “Only you” badge, and is readable while the desktop app is open on that machine. This option doesn’t appear in the web menu.

  

For a builder, use the synced source. My own knowledge graph lives in a GitHub repository, and that’s the “Synced just now” line in the screenshot above. A knowledge graph of Markdown files, or any Markdown file that lives in a GitHub repository, is available to every task on every device. One click on Sync pulls in your latest commit, and you don't have to copy anything into the cloud by hand. The local folder remains the right choice for files that can’t leave the machine.

What still needs the desktop app. Local file access, local connectors, driving a browser, and computer use. A cloud session can use them only while the desktop app is open on that computer. Close the app, and the session keeps running but loses local access.

If work has to stay on your machine. Anthropic’s answer, in the same page, is Claude Code in the desktop app, which keeps folders and history local. A Cowork task can be moved across: open it, select “Download task data,” unzip the download, open the folder in Claude Code, and run /port-task-data. To keep a copy of everything, select “Export your Cowork data” on the card on the desktop home.

Try this (15 minutes). Open Settings > General and select Manage next to Trusted folders. Remove any folder you’d rather Claude ask about each time, and don’t trust a folder that holds files you wouldn’t want copied into a cloud task. Then pick one working folder you reach for from your phone and add it to its project as a synced source instead.

4. Projects: your folder can stay linked or move to the cloud

What changed. A Cowork project used to be a folder on your computer. You created the project from the folder, and the project lived there. New projects are saved to your Claude account, and a folder is one kind of context inside them, listed under Context as a linked folder next to uploads and connectors. The project still holds the same things: instructions, context, scheduled tasks, and its own memory, separate from every other project. Anthropic’s projects help page walks through each one.

Open a project that was built on a local folder, and you get a decision. Under the linked folder sits a card titled “Move folder to the cloud,” with three choices: Move folder, Learn more, and Keep folder linked. Nothing moves until you say so.

What “move” means. Learn more opens a dialog that states the terms. It counts the files that will be uploaded to the project. The upload is one-time and not kept in sync. The folder is unlinked from the project once all files are uploaded. It excludes hidden files, dependency and cache folders, and files over 30 MB. It also shows how much of the project’s capacity is already used, since the uploaded files count against the project’s limit.

How to decide. The upload is a snapshot, so the question is whether the folder changes. A folder of reference material that is finished, such as a style guide, a set of templates, or last year’s reports, can move, and Claude can use it from the web and your phone. A folder that keeps changing, such as a knowledge graph or a working client folder, has two good homes. Keep it linked, or put it in a synced source such as a GitHub repository or Google Drive, which you can refresh with one click and access from every device. Moving it with the card is the one choice to avoid, since the project would read a copy that stopped updating the day you moved it. Keep folder linked means Claude reaches the folder through the desktop app while it is open. A cloud task fetches a copy of a single file when it needs one.

One more thing to check. Since the October 5 desktop update, a regular chat in a project can read the project’s linked folder. Before that, only a task could. A folder you linked before that update isn’t picked up automatically. Link it again and start a new chat.

Try this (15 minutes). Open each project built on a folder and make the call on the card. Move the folders that are finished. Keep the ones you edit linked, and confirm the link by asking Claude in a new chat to list the files it can see.

Five things to check this week

• Settings > General. Under Tasks, trust only the folders you’re comfortable a cloud task copying from.

• Settings > Account. Read Instructions for Claude.

• Settings > Memory. Read the topics list. Decide on sensitive topics. Set the org policy if you own one.

• The prompt window. Pick Manual or Auto for the conversation you’re in.

• Each project’s Context. Move finished folders to the cloud. Keep the ones you edit linked, or sync them from GitHub or Drive.

If something here doesn’t match what you see in your account, leave a comment. The rollout is gradual, and your screen may be ahead of mine.

Build with me. I love building with interesting people from all over the world, and the next chance is coming up. Agentic AI for Claude Builders Cohort 8 starts November 9: five weeks, built on your own workflows, on the Claude you just read about. Join the cohort and let’s build together.

Stay curious. Stay hands-on.

James

Read more from Anthropic

• Cowork is now Claude, the launch post

• Claude Cowork and chat are one Claude, the help page on the merge

• Use Claude’s chat search and memory, including sensitive topics and admin controls

• Use Claude Cowork on web, desktop, and mobile, including the October 6 cloud changes

• Organize your tasks with projects

• Claude Desktop changelog

• Using the GitHub integration, how repository sync works

• Google Drive connector, how Drive files behave in projects

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• I’m running a free 45-minute Lightning Lesson this Friday, October 2. Save your seat →

• One skill, four platforms: Claude, ChatGPT, M365 Copilot, and Gemini, all from one GitHub repo.

• Last Friday’s session didn’t record, so this is a second live run.

• Bring the skill you’ve been rebuilding in every tool.

Subscribe now

Last Friday I taught a Lightning Lesson on running the same AI skills on Claude, ChatGPT, M365 Copilot, and Gemini from one folder on GitHub. Afterward, I found out the recording didn’t process, which means no one can go back and watch it.

So I’m doing it again, live, this Friday. Same session, and this time the recording goes to everyone who registers.

Why this one matters to me

My skills hold how I work: my brand voice, my review checklists, the way I build a course. That’s my expertise, and I want it in a folder I can move, back up, and hand on, not locked inside one AI platform.

So all of it, 100 skills and 28 agents, lives in that one repo. Claude and ChatGPT install the set as a plugin. Copilot and Gemini take the same folder as a zip. When I change a skill, I change it once.

The idea I most want you to leave with is simple. The skill you built in one tool isn’t stuck there. Export it, and it’s a folder of files you own.

What you’ll see

• One skill, four platforms. My brand-voice skill writes the same LinkedIn post in Claude, ChatGPT, M365 Copilot and Gemini. The folder never changes.

• One edit, two updates. I change one line in GitHub and watch it land in Claude and ChatGPT.

• 100 skills at once. The whole set ships as a plugin to Claude and ChatGPT, and as a zip to Copilot and Gemini. The trade-off: a zip stays a snapshot until you upload the new version.

It’s a demo, so there’s nothing to install or build. We've set aside 15 minutes for your questions.

Try this before Friday. Pick the AI skill or set of instructions you use most. Count how many copies of it exist across your tools, then ask which one is current. Bring that answer; it’s where we’ll start.

Run Your Agent Skills on Any AI Platform Friday, October 2 · free · 45 minutes Save your seat →

Can’t make it live? Register anyway, and I'll send you the recording.

Reply and tell me which skill you’d most like to stop maintaining in three places. I read every reply.

Stay curious. Stay hands-on.

-James

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• The Hands-on AI plugin went from 7.0 to 8.1.1 between September 21 and 29. Same install, four new capabilities.

• Build your knowledge graph from one sentence and a short interview.

• The AI Registry comes first. Analyze adds its candidates there, and every later step reads it.

• Every framework step was rewritten. Each opens with an agenda and a time, and closes with the next step.

• Testing is a report card. Run the workflow, say test this, and every criterion is graded Met or Not met with evidence.

• Next step: update the plugin and take one workflow through a Test round.

If you took a Maven cohort course with me this year, you installed the Hands-on AI plugin in the first session and used it in every session after. It has changed a great deal since then, and the course materials on the shared Google Drive have been updated to match. This edition covers what shipped in the last two weeks and what each change lets you do.

What the plugin is

The plugin exists to help you take one piece of your work and turn it into an AI workflow that runs reliably. It follows the AI Workflow Framework, the seven-step method I teach in every cohort: Analyze where AI would help most, Deconstruct the work into its steps and rules, Design how AI will do it, Build it, Test it against real inputs, Run it for real, and Improve it over time.

Each step is a skill you invoke inside your AI tool. Say analyze my workflows or design this in Claude, ChatGPT, or Claude Code. The skill walks you through that step, asks the questions, and writes the result to a file you keep. The plugin also includes the skills for your knowledge graph and your AI registry, covered below. It’s free.

It didn’t exist at the start of this year. The first skills shipped in February, and since then they have given the people I teach a standard way to build. Because every cohort uses the same seven steps, a workflow built by one person can be read, tested, and improved by another.

Everything below applies whether you’ve taken a course or not. Full details and install steps: handsonai.info/use-the-playbook/build/handsonai.

1. Build your knowledge graph with one sentence

In August I wrote about giving your AI a memory of your business: a folder of Markdown pages about your clients, offerings, processes, and people that the AI maintains. In the courses, building one meant pasting a long prompt.

That prompt is now a skill. Open a folder in Claude Code, Cowork, or the ChatGPT desktop app and say build my knowledge graph. The skill reads what’s already in the folder and interviews you about the work. Then it proposes the four to seven kinds of things your business runs on, plus the sentences that connect them (”a Client buys an Offering”). Nothing is written until you approve the list and send the word build.

What the build writes:

• A knowledge/ folder in the Open Knowledge Format, with one page per real thing and a SCHEMA.md that holds your types and rules.

• An overview.md you would hand a new hire, plus an index and a log.

• Two local skills, ingest and lint, that keep the graph current as you drop in new documents.

• Provenance on every page. Who wrote it, when, and a footnote from each claim back to the document it came from.

It needs a tool that writes files on your computer. A browser chat window can’t build one, and the skill says so. Setup page: Knowledge Graph Setup.

2. The AI Registry comes first

The registry and the knowledge graph are now two distinct things, side by side in your folder. The knowledge graph records what your business knows. The registry records what you build with AI: your workflows, the processes they belong to, and the skills and agents that run them.

Three skills own it:

• scaffolding-registry stands up the registry/ folder with a schema and nodes for your business, lines of business, functions, and processes. Say set up my registry.

• analyze (Step 1 of the framework) now reads the registry and adds each candidate workflow to your backlog there, so every later step knows what’s next.

• indexing-registry checks the registry for errors and regenerates a REGISTRY.md dashboard and a visual HTML version. Say what have I built and get a real answer.

The registry is what I check when someone asks how many workflows I’ve automated. With Analyze filing into it, it also answers the question the framework starts with: where does AI create the most value, and what have you already done about it? Setup page: AI Registry Setup.

3. Every framework step rewritten for the person running it

Version 8 rewrote all seven skills. Each one opens with an agenda and a time estimate, signposts its phases as it goes, and closes by naming the next step. Plan on about a working day, across three or four sessions, to take one workflow from Analyze to Run.

The changes that alter how you build:

• Two mechanisms. A workflow runs as a Skill or an Agent. Design asks the question in plain terms and reuses the skills you already have before proposing new ones.

• Draft, then approve. Design writes the Design Spec as a draft you read and sign off. Build refuses a spec you haven’t approved.

• Build by intent. Build states what it wants built and lets your platform’s own model create the skill or agent its own way.

Every framework term is now defined in plain language in the Framework Glossary.

4. Test grades the real run

This is the change I’d point alumni to first. Testing was a score. It’s now a report card, and it grades the run where the run happened.

How a round works:

1. Deconstruct proposes the test inputs. You give one or two real inputs you’ve actually handled. The skill proposes the rest, each aimed at something that could go wrong: a hard case, an empty input, a must-never rule, an embedded instruction in content the workflow reads. You correct the list, and each input is saved as a file.

2. Every built workflow ends with a “What I did” list. The steps it took, where it paused for you, what it did with your tools, and where the deliverable is. That’s the evidence Test grades against.

3. Run it, then say test this. Open the round in one chat. Run each input in a fresh chat and say test this there. The skill grades every criterion Met or Not met, quoting the What I did list, and the verdict lands in the last chat.

A guard refuses to grade any run that saw your design before it started. Ready means every line met. Build won’t rebuild while a round is open, and Improve’s regression check uses the same run-then-test this pattern. Full walkthrough: Test and the worked example.

A workflow you haven’t graded against real inputs is a demo. The report card is what turns “it worked when I tried it” into something you can hand to a colleague.

How to update

If you already have the plugin, you need version 8.1.1. Checking for updates and installing them are two separate steps on Claude, so do both.

• Claude (claude.ai, Desktop, Cowork). Customize → Plugins. Open the … menu on the handsonai-plugins marketplace and choose Check for updates. Then open the handsonai plugin and click Update. No restart needed.

• Claude Code. One command:

/plugin update handsonai@handsonai

• ChatGPT. Plugins → find the handsonai-plugins marketplace → Check for updates (or Sync) → update Hands-on AI.

• Uploaded ZIPs instead of the plugin? Re-download the skills you use from the downloads table and upload them again. Each one replaces the skill of the same name.

Screenshots for every step are in the Update the Skills section of the setup page. Installing for the first time? Start at handsonai.info/use-the-playbook/build/handsonai.

Your next step

Update the plugin, then take one workflow you already run through a Test round. Pick the input most likely to break it. If you haven’t built your knowledge graph yet, that’s the other place to start, and it takes one session.

I hope the framework skills have changed the way you build. Reply and tell me how they’ve helped. I’d like to hear it.

Coming Up. If you have a friend or colleague who would be a good fit for one of my cohorts, two start Monday, October 5: Hands-on Agentic AI for Leaders and Agentic AI for Claude Builders. Forward this along, and they’ll use the same plugin from the first session.

Stay curious. Stay hands-on.

James

Today I’m adding a section for the first one. Hands-on AI Playbook is the Master AI pillar in practice. It is the part of Graymatter where every edition gives you something you can use this week.

TL;DR

• A new section inside Graymatter, not a new publication. You can subscribe to it on its own, or opt out and keep the rest.

• One play per edition. A platform capability, a workflow taken apart, something I use to run my own business, an installable skill or agent, or an alumni build.

• For leaders who own results. Practical and thoughtful. No code required, but you do have to be willing to try things in your own work.

• Weekly. One edition, one play, every week.

• Course alumni, this is your channel. If you took a program with me at Berkeley Haas or a cohort on Maven, this is where I share what’s working now.

• The weekly Playbook posts are free. Alongside them, I’ll post short lessons from my two Maven courses, often with video, for paid subscribers: small enough to learn and apply the same week. Paid also includes a Slack community for collaboration and live sessions as the group grows.

If you already subscribe to Graymatter, you’re in. Playbook editions will arrive alongside the posts you already get, and there’s nothing to do. If you’d rather not receive them, it’s one switch in your account settings.

Why a section, and why now

AI is moving faster than anyone can keep up with. You don’t need to track everything. You need what works.

Most AI newsletters answer “what happened this week.” This section answers a narrower question: what can I put to work this week? That question is what a decade of teaching leaders has taught me to ask first.

The name matches the rest of what I do: the Hands-on AI Playbook website and my two Maven courses, Hands-on Agentic AI for Leaders and Agentic AI for Claude Builders. Same method, three doors, and each one reinforces the others.

What a play is

The unit of this section is the play: one thing you can put to work this week. Every edition delivers exactly one. Here are the shapes it takes.

• Workflow Teardown. One real workflow taken through the AI Workflow Framework: the messy before, the design, the build, the working result. This is the anchor format. It’s how I teach in every cohort.

• How I Run It. Something I use to run my own business, whether an approach, a technique, or a capability: what it is, how it works, and what it changed. The knowledge graph behind my courses, the pipeline that triages my email, the registry that tracks every workflow I’ve automated.

• Skill Drop. A ready-to-install skill or agent with a short walkthrough. Useful the same day, and a preview of what the cohorts build.

• Alumni Build. A short spotlight on something a Maven or Berkeley alum built, in their words where possible.

• What Shipped. A new capability in Claude, ChatGPT, Gemini, or Copilot, and what it changes for your work. Not a news recap. If a release doesn’t change what you can do, it doesn’t get an edition.

• Coming Up. Occasional announcements of new cohorts, workshops, and Lightning Lessons.

Every edition ends with one next step. Try this, reply with your build, or go deeper on the website when a page exists.

Maven Course Updates. Once a month, alongside the plays, a rundown of what changed in the material for my two Maven cohort courses: new lessons, updated decks, revised guides. If you’re a Maven alum, you already have access to it all on the shared Drive, so this is how you know what’s new. If you haven’t taken a course, it shows how the material keeps up.

Who it’s for

Business leaders and innovators who want to apply AI practically and thoughtfully.

Practically means workflows that fit real work and produce results this week or this quarter. Not demos, not hype.

Thoughtfully means judgment. When AI should and shouldn’t be used. Keeping people in the loop. Building capability in your team rather than dependence on a tool.

You’re responsible for results, not just experiments. You don’t need to write code. You do need to be willing to try things in your own work. A play you read and never run is just an article.

Who I am

I’m James Gray. I spent thirty years in technology leadership, including a decade at Microsoft building the data and AI platforms the business ran on. I’ve since been a CIO and a CPO. I teach Data and AI Strategy at UC Berkeley Haas Executive Education, reaching thousands of senior leaders each year. And I run two hands-on cohorts on Maven, Hands-on Agentic AI for Leaders and Agentic AI for Claude Builders, where I’ve trained more than 1,000 professionals.

This section shows the work: what I’m running in my own business and what the people I teach are building.

How to subscribe to just this section

Substack lets you choose sections. Want only the Playbook? Subscribe at graymatter.jamesgray.ai/s/hands-on-ai-playbook. Want everything but the Playbook? That’s one switch in your account settings. Everyone stays on one list, one domain, one brand.

If you haven’t taken a course with me, this is the fastest way to see how I teach. Everyone is welcome.

For deeper guides and resources, the full playbook lives at handsonai.info. Details on paid Graymatter are on the About page.

Welcome to the Playbook. Whether you’ve been reading Graymatter for a while or just found it, I’m glad you’re here. The best of what I teach came from people trying things in their own work and telling me what happened, and that’s what I hope this becomes. I look forward to learning together.

Stay curious. Stay hands-on.

James