Hello Reader
You’ve been meaning to set up AI correctly.
Then a campaign needs approving, a client asks for changes and three meetings appear on your calendar. The setup gets pushed to next week while you continue dragging work through the same collection of chats, files and tools.
You may already have several useful pieces. A Project containing your brand documents or a prompt that produces a decent brief. Maybe you even have an agent with a name and job title.
But every task still begins with another briefing.
You find the relevant files, explain the goal, paste in the background and correct the same mistakes. By the time you’ve done all that, using AI can feel like another task to manage.
Every time I show this cartoon at a workshop people laugh and I know they’re laughing because it’s the exact situation they find themselves in.
If you relate then it’s possible your AI agents skipped their company onboarding
Think about what happens when someone joins your team.
They learn about the company, its customers and the work already underway. They get access to the systems and documents required for their role. Someone teaches them the processes they’ll follow. They also learn the rhythms of the team, from Monday meetings to Friday reports.
An AI agent needs the same kind of onboarding.
Many agents begin with a name, a job title and a broad instruction such as, “You’re a marketing strategist. Help me plan campaigns.”
That gives the agent very little to work with. It has no company history, customer context or examples of what your team considers good. It can’t see the latest campaign results or meeting decisions. It also has no defined process for turning that information into work you can use.
You're stuck with generic ideas, repeated corrections and an agent that needs constant supervision.
How to Train an Agent
Jack Piro builds AI systems for companies through Juno Automations. His team gives AI the business context, tool access and workflows it needs to work inside a company.
He calls the framework MAST:
- Memory
- Access
- Skills
- Tempo
You can build these layers in ChatGPT, Claude, Gemini, Copilot or whichever platform your company supports.
Setting up Your Agent Layers
MEMORY
Memory is everything your agent needs to know about your business and the way you work.
This might include:
• What your company sells
• Who your customers are
• Your offers and priorities
• Brand voice and writing standards
• Examples of good work
• Decisions that should carry into future work
• Common mistakes the agent should watch for
Setup: Put this information in a saved workspace, folder, project, custom assistant or a small collection of reference documents. Keep it focused on the job you’re giving the agent.
As an example, a content agent may need your audience profiles, editorial strategy, voice guide and several strong examples. A campaign reporting agent will need your goals, channel definitions and reporting standards.
What happens without it: Your agent fills the gaps with generic assumptions. You spend your time correcting the same voice, audience and business errors.
Test it: Open a fresh conversation inside the workspace and ask the agent to explain your business, audience and priorities. Check whether the answer reflects the context you provided.
Paste this into your agent:
What do you know about my business, audience and priorities?
Check the response:
✓ Is the business accurate?
✓ Is the audience specific?
✓ Are the current priorities correct?
Anything missing? Add it to the agent’s memory before moving on.
Here’s what that looks like in Claude:
ACCESS
Access includes the information your agent needs to do the job.
Memory is where you host your long term and consistent context. Access covers the files and information that change from one week or project to the next.
This could include:
• Campaign results
• CRM records
• Meeting notes
• Project files
• Customer research
• Tasks and deadlines
• Email or calendar information
• Client-specific materials
Setup: Use a connection provided by your AI platform, a shared folder, an uploaded document or a defined handoff from a person. Start with the smallest amount of access the agent needs for one job.
For example, a weekly campaign agent might receive the campaign brief, the latest performance report and notes from the most recent review meeting.
What happens without it: The agent works from old or incomplete information. You become the connection between every system because you’re moving the material by hand.
Test it: Ask the agent to find and summarize the information required for the job. Note anything you still have to locate or supply yourself.
Paste this into your agent:
Find and summarize the information you need to complete [name of task].
Check the response:
✓ Did it find every required source?
✓ Is the information current?
✓ What did you still have to locate or upload?
Anything missing? Give the agent access before moving on.
Here’s what this looks like in Codex:
SKILLS
A skill is the repeatable process your agent follows to complete a specific piece of work.
It explains:
• When to use the process
• Which sources to consult
• Which steps to follow
• Which decisions the agent can make
• How to format the result
• How the work will be checked
• When a person needs to review it
A skill could turn meeting notes into a campaign brief, review copy against brand standards, prepare a weekly client update or repurpose a newsletter for several channels.
Setup: Complete the task with AI once. Correct the weak parts as you go and save the process in whatever reusable format your tool supports.
The finished skill might be a set of saved instructions, a template, a reusable workflow or a documented process inside a custom assistant.
What happens without it: The agent approaches the job differently each time. The quality depends on how much detail you remember to include in each request.
Test it: Start a fresh conversation and give the agent a short request. Check whether it can follow the saved process and produce work in the right format.
Paste this into your agent:
Complete [name of task] using the saved process.
Check the result:
✓ Did it follow each step?
✓ Is the format correct?
✓ Is the work ready for review?
Something went wrong? Update the saved process and test it again.
Here’s what skills look like in ChatGPT:
TEMPO
Tempo defines when the agent begins working.
The agent might start:
• Every Monday morning
• After a meeting ends
• When a form is submitted
• When a file enters a folder
• When someone requests the work
• When a metric reaches a set threshold
Tempo can begin with a recurring calendar reminder. You can add scheduled or event-based triggers after the workflow has produced good work several times.
Setup: Define what starts the work, where the result should appear, who reviews it and what would cause the workflow to stop.
For example, every Friday afternoon, a campaign agent could gather the week’s results, follow the saved reporting process and prepare a draft update for the marketing lead.
What happens without it: The process may be well built, but someone still has to remember when to start it.
Test it: Check the trigger or schedule for the task. Does it run when it needs to?
Paste this into your agent:
When does this workflow begin, what does it produce and who reviews it?
Check the answers:
✓ Is the starting point clear?
✓ Does everyone expect the same output?
✓ Does one person own the review?
Any confusion? Define the trigger, output and reviewer before the first run.
You can schedule tasks on all the platforms. Here’s what it looks like in Gemini:
Build the layers in this order because each one gives the next something to work with. Tempo comes last. Putting weak work on a schedule only gives you weak work more often.
Meet my chief of staff, Piastri
I recently built my own Agent Workspace in Codex.
I named my chief of staff Piastri. If I’m building a team of agents, they’re obviously getting an F1 lineup.
Piastri helps me manage work across Perry Co, Driver Labs and AI at Work. His workspace shows his role, current assignment, status, next action and working files.
The visual interface makes the system far more enjoyable to use, but Piastri’s racing suit doesn’t make him useful. His onboarding does.
- His memory includes context about my brands, audiences, priorities and ways of working.
- His access points him toward the files and materials he needs for each assignment.
- His skills define the work he owns and how he should complete it.
- His tempo comes from the cadence I’ve set for assignments, check-ins and follow-up.
I started by deciding what Piastri needed to know, reach and do, then when he should step in. He takes care of the rest.
Done for you Agents
If you would rather have the four layers already in place, Jack's team recently productized his entire personal AI Operating System into an web application called greatroom.ai.
It's a done-for-you AIOS with the MAST frameworks already built out. It works on the web and your agents run in the cloud when you aren't at the computer. It has a task board to assign tasks to agents and track their progress, as well as a built-in chat rooms so you and your team can use the same agents together, with the same context.
Jack says: "An issue I deal with consistently when building AI Operating Systems is setting up permissions for organizations. People typically want their managers and employees to have different levels of permission and access to documents even in a 5-10 person org. That typically involves using Github and other technical things you shouldn't have to deal with as a business owner. That's the core issue they built greatroom to solve."
Getting Started with Your First Agent
Your first agent can be much simpler. It can live inside a Project or custom assistant and handle one recurring piece of work.
Block one hour on your calendar and choose one recurring task.
By the end of the hour, you’ll have the first version of an onboarded agent and a short list of anything you still need to add.
Minutes 0–10: Give the agent one job
Choose a task that happens at least once a week, follows a recognizable process and produces a clear result.
Good starting points include:
• A weekly client update
• A campaign performance summary
• Meeting follow-up
• A content brief
• Newsletter repurposing
• A Monday priority report
Ask yourself: Which task makes me reopen the same files and repeat the same instructions every week?
Minutes 10–25: Build its memory
Create one short reference document covering:
• What the business or client does
• Who the audience is
• The goal of the work
• Relevant brand or voice rules
• Two examples of good output
• Current priorities
• Anything AI regularly gets wrong
Add the document to your chosen AI workspace. Ask the agent to summarize what it knows and identify the information it’s missing.
Minutes 25–35: Give it access
List the sources used in the workflow.
Choose one source that you can provide now through an upload, shared folder, copied data or an existing connection.
Ask the agent to locate or summarize the information it will use. Add anything that requires account access, IT approval or more setup to your build list.
Minutes 35–50: Create the skill
Complete the task once with the agent.
As you work, define the required inputs, steps, decisions, finished format and review criteria. Decide where a person should check the work before it moves forward.
Save the instructions in the reusable format your tool provides. Start a fresh conversation and test the saved process with a short request.
Minutes 50–60: Set the tempo
Decide what starts the workflow and how often it should run.
Choose where the result will appear, who will review it and what would cause the work to stop.
After several good runs, you can decide whether scheduling the work inside your AI platform makes sense.
After one hour, your agent will have one job, the context required for that job, access to one source, a saved process and a defined starting point.
You’ll finally ditch those square wheels!
What agent are you going to build? Hit reply and tell me.
Tahnee