Quick check-in before we get into it —

Where are you right now with building your first AI workflow?

No judgment wherever you are. This issue is built to move you one step forward from wherever you're standing. Let's go.

This was a big week. And I want to cut through the noise on what actually matters.

In the last seven days:

That last number is the one I want you to sit with. Twelve agents per organization — average.

Not at the bleeding-edge companies. Not at the AI-native startups. Average.

So here's the question nobody's asking loudly enough:

If the average org is running 12 agents, who owns them? Who's governing them? Who knows what they have access to?

Gartner just updated its predictions for I&O teams: the interaction model is shifting from scripts and CLI to prompt engineering, policy definition, and workflow orchestration. That's not a 2028 problem. That's a right-now problem dressed up in analyst language.

The moat isn't building agents anymore. The moat is knowing how to govern, monitor, and operationalize them at scale — before your leadership asks someone else to figure it out.

If you're in IT or ops, that someone should be you.

What it is: Google's new no-code agent builder, launched this week at Cloud Next. It lets you build AI agents directly inside Google Workspace — Gmail, Docs, Sheets, Drive, Meet, Chat — by describing what you want in plain language. No API. No code. No engineering ticket.

Why it matters right now: This is the fastest path from zero to a deployed agent for anyone who lives in Google Workspace. You describe the agent, Workspace Studio builds it, and it runs inside the tools your team already uses every day. It also connects to 200+ models in Google's Model Garden — including Anthropic's Claude — so you're not locked into one model.

The IT use case: Build an agent that monitors your support inbox, categorizes inbound requests by type and urgency, and surfaces a summary to your team each morning. What used to require an n8n workflow and a few hours of setup is now a 20-minute build.

Who it's for: IT leads, ops managers, anyone who manages workflows inside Google Workspace.

Cost: Included in Google Workspace Business and Enterprise plans.

Try it: workspace.google.com

This week's template is the Agent Governance Audit Prompt — use this to build a clear picture of every AI agent your team is running (or should be running) and who owns what.

It's the conversation most teams aren't having. Use it to get ahead of the one your leadership is about to start.

For IT leads and ops managers:

For individuals building their first agent:

The full Agent Governance Playbook — with an agent inventory template, access control checklist, and a leadership briefing outline — is coming to the AI Templates Library at launch.

👉 Get Early Access → agenticcollective.co

You're going to hear "managed agents" a lot more from here on. Here's what it actually means.

An agent you build yourself: You set up the infrastructure, manage the state, handle errors, and keep it running. Full control. Full responsibility. Requires either engineering resources or significant setup time.

A managed agent: The platform handles the runtime — the memory, the state, the uptime, the sandboxing. You define the goal and the tools. They keep it alive and running. Think of it like the difference between self-hosting a server and using cloud hosting. Same concept, different layer of abstraction.

Why Anthropic's launch matters: Claude Managed Agents at $0.08 per session-hour means a long-running agent that works inside your environment for eight hours costs about 64 cents in runtime — before tokens. A team can now run what used to be enterprise-only infrastructure for a few dollars a day.

The tradeoff: Less control over the environment. More speed to deployment. For most IT and ops use cases — especially at the start — that tradeoff is worth it.

The short version: managed agents lower the barrier to your first deployment. Build something with one. Once you understand the pattern, you'll know when it makes sense to build your own infrastructure.

Next week: I'm walking through the specific agent stack I'd build for an IT ops role in 2026 — tool by tool, decision by decision.

II'll be honest — this week's announcements hit different for me personally.

I've been saying for months that the window to get ahead of this is closing. Not because agents are getting harder to use. Because they're getting easier. And when something gets easier, everyone does it — and the people who were slow to move lose the advantage they had.

The IT pros I respect most aren't the ones who know every model. They're the ones who already have a working agent in production, a governance framework in progress, and a pitch ready for when leadership inevitably asks "what are we doing about AI?"

That's exactly what AI Edge is built around. Not theory. Not demos. Deployable systems you can show in a meeting.

Waitlist is open. If you're in IT and you've been thinking about it — now is the time.

Next week I'm breaking down:

👉 Join the AI Edge Waitlist → agenticcollective.co

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