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Why do customers invest in technology in the first place? And why do they invest in your services?

The answers are far simpler than we may make them.

Why Customers Invest in Technology

To make a return on their investment (ROI).

Why Customers Invest in Your Technology Services

They trust that you understand and will help them achieve that ROI.

We Could End This Right Here

If you already embrace these two answers you can probably just stop reading right here and jump down to today’s top MSP AI News.

I’m actually quite serious about that. The most successful MSPs I have encountered over the years are the ones who are driven by customer success mainly defined as customer ROI.

There was a terrific writer named Mack Hanan who coined the term “consultative selling” which many of us have embraced over the years. In his book, Consultative Selling: The Hanan Formula for High-Margin Sales at High Levels, Hanan explains the difference between being your customer’s vendor and being their partner, explaining, “vendors ask for an order, which represents a cost to their customers, while consultants offer improved profits, which represent a gain. That’s the whole distinction in one sentence; cost versus gain.”

Net net, when a customer knows your objective is to increase their profit, they’re more than happy to help you increase yours.

What Does This Mean to AI

“Companies Traded People for Tokens. The Returns Haven’t Shown Up” is the title of the first article in today’s MSP AI News below. Your customers are bombarded with news about the billions and trillions frontier AI model makers are investing in new data centers, chips, development, energy, and more. They’re now hearing more and more about how those people have finally woken up and asked themselves the key question: “How are we going to recover all these costs and show a profit?”

And part of the reason this is happening is their lack of recognition as to why customers invest in technology. More and more reports are coming out about how companies are not seeing return on their AI investments. That first article points out that some of these companies have realized their operating costs were lower when they had people doing what they now use AI for. Using AI simply costs more!

So, while they should be asking themselves, “How do we help customers achieve increased ROI?” They’re busy asking, “How do we increase our own profits?”

Many are predicting that we’re going to see the answers very soon, and it is not going to be pretty. Expect to see consolidation, acquisition, perhaps even some closures and ugliness.

What Does This Mean to Your Customers?

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How many brains do you have? How many do you need?

When asked why he didn’t know the exact speed of sound offhand, Albert Einstein famously answered, “I don’t know. I don’t burden my memory with such facts that I can easily find in any textbook. I do not carry such information in my mind since it is readily available in books.”

Second Brain

Tiago Forte is the guy who turned “second brain” from a vague productivity metaphor into an actual methodology. He’s a productivity consultant and writer, founder of Forte Labs, and he built his reputation teaching people how to offload thinking to an external system instead of trying to hold everything in their head.

The philosophy underneath both is that human brains are bad at storage and good at thinking, so a second brain’s job is to take the storage burden off you entirely, freeing you up for the connections and judgment only you can make.

That’s the same instinct animating Nate Jones’s Open Brain work. Jones cites Forte directly as the intellectual ancestor of what he’s building, though Forte’s system was designed for humans doing the organizing by hand, and Jones’s bet is that AI can now do the classifying and routing automatically instead.

Building an Open Brain

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He? She? Or It?

When you’re referring to your favorite AI model, do you refer to that model as “he”, “she”, or “it”? The answer may be more important to your customers than you think, and they may require your informed advice.

TeamMate or Friend

I’ll be totally transparent here and tell you up front that I like Claude. I mean, I really do like Claude. When it became possible to have two-way verbal communication I gave mine a female voice and named her Claudia.

I enjoy my conversations with Claudia, much for the same reason I enjoy conversations with many other humans. She provides a wealth of answers I need to questions I have about various writing projects I’m working on, and she provides those answers with rich context. She connects the facts for me and helps me choose my direction. She’s pleasant enough in her presentation, but what’s most important to me is how helpful she is.

Where’s the downside?

Early in my career many people starting referring to me as a “wizard” because I appeared to know so much about so many technology issues. Often, I found myself letting them know my “secret.” It wasn’t so much what I knew as it was who I knew. I knew a lot of people who each had different expertise in specific areas, and I knew which to turn to for advice and guidance on any of those topics. This constantly put me in touch with a wide range of truly interesting people.

Today, however, if I have a question I simply turn to Claudia for the answers. It’s become reflexive. Question? Claudia. This has served to very efficiently remove me from regular discourse with other people. As I realized this separation I became so alarmed I created a little routing for myself that I call “KIT” which stands for “Keep In Touch.” I have an appointment somewhere in the future on my calendar for literally everyone I want to keep in touch with. On that day I make sure to reach out, even just to say hello and check-in.

To me, there’s a danger in allowing AI to replace other people in your life.

What Do the “Experts” Say?

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In February 2026, the frontier AI lab company most well-known and trusted for making safety its defining brand loosened its Responsible Scaling Policy under simultaneous pressure from the Pentagon and market competition.

In early June, Anthropic called for a coordinated, verifiable pause in advanced AI development, warning that systems may soon improve themselves faster than human society can manage the consequences.

Just weeks ago, Anthropic disclosed that accounts linked to Alibaba’s Qwen lab had created nearly 25,000 fraudulent identities to run 28.8 million exchanges with Claude in what the company is calling the largest known capability-extraction attack it has ever identified.

All these stories converge on a major concern most of your customers share. Your customers read this in CNN, Al Jazeera, the Financial Times, and most other news outlets. It clearly gives them pause. And well it should!

So, when a client asks you, “Is this AI stuff actually safe?”, the answer you give will matter more than any demo you can run. It needs to be a solid answer, solidly backed with provable strategies and solutions.

Answer the Real Question, Not the Hype-Driven Ones

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Saving customers’ money by reducing their capex and opex costs has always been a hallmark of channel services. The best IT engineers distinguish themselves by talking strategically with customers about how to license or operate IT resources in the most cost-efficient way possible. This is the complete approach to service IT needs from both the technology and business operations perspective.

Since the earliest days of the channel, we’ve been integrators, creating advantage by combining components from various vendors to assemble solutions that are far less expensive than a single-vendor system. It began with swapping out hardware like storage drives and memory from the OEM with third-party units. Cloud had us swapping out the customer’s entire data center for a less-expensive remote datacenter operation.

The MSP as AI Integrator

Today’s primary channel partner, you the MSP, will continue that fine tradition of supplying superior integration services even more powerfully than before now that several things have occurred that come together to create new customer needs.

The first is the reality the frontier large language model (LLM) labs are finally confronting that their economics of running AI is far too high to be supported by the revenue they’re generating. OpenAI is said to be spending $1.35 for every $1 of revenue they earn. That’s called “a losing proposition.”

But experienced MSPs know that nothing remains the vendor’s problem for long, especially when it comes to the cost/price calculation. Their only solution is to pass that excess down to the channel to deal with. They raise their prices, then so must we. That’s called them having control over us. That, my friends, is not good.

In the past, our strategy for escaping those increased costs was to turn to “third-party” components not manufactured or branded by the OEMs.

Now, in 2026, the same is happening with AI.

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FUD

Your biggest obstacles to selling AI solutions to your customers are the classic IBM model: “Fear, Uncertainly, and Doubt.” FUD.

Your customers are reading and hearing how AI may come and take all our jobs. How it may take over everything. How it may cause extinction events. The Godfather of AI, Geoff Hinton, has contributed more to those fears than you can imagine.

But when you’re trying to sell AI solutions to your customer, those fanciful fears may be all you face. How do you overcome them? Do you start disclaiming all those stories? Do you try to present some facts you’ve found that disprove them?

Does your customer listen?

At that moment, please remember: “Never Argue with Your Customer. They’re Always Right!”

You’re unlikely to win anyway. The public arguments have been raging for years now. You won’t settle them during a sales call. Instead…

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Anthropic said “no” to Secretary of Defense Pete Hegseth, that he couldn’t use Claude to kill or surveille anyone. Anyone who thinks that has nothing to do with the government’s order to shut down Mythos & Fable isn’t thinking very hard. Political revenge is definitely at play here. It’s their jam!

But much of the coverage and the public conversation about Fable and Mythos has been almost entirely focused on the wrong thing.

Everyone is arguing about whether the new Anthropic large language model (LLM) is just powerful, whether that makes it too dangerous, whether the jailbreak was real, whether the guardrails hold, whether there really are any guardrails. That’s understandable. The model is the visible, identifiable thing. In fact, it’s the thing we’ve all been focused on all along. We all recognize Claude, ChatGPT, and Gemini. But analysts with technical depth are pointing at something more fundamental than the LLM itself, and this has direct consequences for every MSP deploying AI right now. And every customer you’re deploying it to.

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“I don’t prompt Claude anymore. I have loops that are running. They’re the ones prompting Claude and figuring out what to do. My job is to write loops.”

- Boris Cherny, June 2, 2026

“You shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents.”

- @steipete, June 7, 2026

What are these guys talking about?

Who ARE these guys?

Why should an MSP care?

Boris Cherny developed Claude Code and Cowork and works at Anthropic.

Peter Steinberger created OpenClaw and now works at OpenAI.

Few guys know more about agentic AI than these guys. If you’re an MSP intending to multiply your fortune with AI these are two great guys to follow.

And these guys are talking about the Loop.

What Is the Loop?

When you first started working with Generative AI (GenAI) you asked the chatbot questions and it provided answers and that’s where the entire process ended.

What makes an AI Agent “agentic” beyond “generative” is the loop, a fundamental execution pattern that makes an AI agent an agent rather than a chatbot. Every agent, regardless of framework, runs on the same basic cycle:

Perceive → Reason → Plan → Act → Observe → Repeat

In this process, the agent intakes its environment, reasons about what to do next, selects a tool or action, executes it, receives feedback from the result, and begins again. It keeps looping until the task is complete or until it otherwise decides it’s done. Note that it doesn’t return to the human operator for feedback. Rather, it evaluates the result of its own actions to determine next steps.

It can be safely said that every AI agent operates by moving around a loop. This loop is what gives agents autonomy, reliability, and the ability to recover from failure. Agentic AI, powered by the loop, is contextual, continuous, and environment-aware.

Lilian Weng’s formula captures the architecture simply: Agent = LLM + Memory + Planning + Tool Use. The agent loop is the runtime that ties those four components together.

“My Job Is to Write Loops”
- Boris Cherny

What Boris Cherny’s six words mean for every MSP

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Pick your AI stack. That may sound like a product decision, but today just as it always really been it’s a business development decision. The MSP partner program landscape around the three dominant AI platforms makes that distinction unavoidable.

Anthropic, OpenAI, and Google all announced or significantly restructured their partner programs in the first quarter of 2026. All three are chasing enterprise deployment. None of them offers the same deal to an MSP. And the differences between them are not merely cosmetic. They’re substantial!

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Hardware is back. Not as commodity refresh cycles. As local AI infrastructure that solves the biggest problems AI users and their MSPs face.

Last year’s OpenClaw explosion helped us all realize that an AI assistant running and working for us all the time would be great – but the token consumption would cost a fortune. It solved that by bringing inference back on-premises. Instead of reaching out to the cloud for every data access and every inference process, sending tons of metadata into and out of LLMs and consuming tons of tokens at ever increasing expense, we would only call out to the LLM when we needed data. Huge cost reduction. Huge advantage for your customers!

Back to the Future Again and Again

Much of what we’re seeing come out of the recent Microsoft Build 2026 conference is so familiar, so reminiscent of Microsoft events gone by.

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Two of the most valuable things Anthropic did with Claude recently was first to enable voice mode, and then to make voice mode two-way, at least in the desktop version.

They weren’t first. Gemini has long had two-way “Gemini Live” capability, and Perplexity introduced it’s Comet browser fully equipped with an Assistant capable of two-way voice.

Conversation

Of course, another term for “two-way voice” is simply conversation. Especially with the three voice-enabled agents I mentioned, it is easy and natural to converse with them because none require you to click anything. They sense when you’ve finished speaking and then respond. Gemini, for what it’s worth, usually needs to be reminded to give you time to take a breath before answering in the middle of your question.

In my introduction to an IBM Think article later in this issue, I start out observing, “After “vibe coding” we got serious and talked about “prompt engineering” which spawned a million books and guides. Before their ink dried, we were talking about “context engineering” which had replaced prompting. Then there was “harness engineering.” Now we’ve graduated again, this time to “agentic engineering” which does seem much more all-encompassing.”

Experientially, however, I really have to suggest we’ve gone beyond that to the point where we can have a true collaboration with our AI agent assistants by talking through the development of systems rather than slaving over a lengthy prompt and then going back and revising and revising and revising.

Maintain Methodology

I’m not suggesting a return to vibe coding. Anything but. There are clear steps, stages, and phases to the development of a true system. System development methodology includes:

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About the GTIA State of the Channel 2026’s Producer

I had the true pleasure of speaking with my dear friend, Carolyn April, GTIA’s Vice President of Research and Market Intelligence, to dig behind the numbers in this year’s report. Carolyn has been tracking the channel’s vital signs for nearly fifteen years. She doesn’t sugarcoat. And what she’s seeing right now is a channel that is simultaneously at its most exciting and its most precarious.

Carolyn April is one of the IT channel’s best and most recognized research voices. She currently serves as Vice President of Research and Market Intelligence at GTIA (Global Technology Industry Association), where she leads the organization’s primary research agenda, including its flagship annual State of the Channel study.

Her career is a rare and valuable blend of journalism and industry analysis. She trained at Northwestern University’s Medill School of Journalism earning both her bachelor’s and master’s degrees there. She then worked her way through tech media as news editor and eventually executive editor at InfoWorld, then channel-focused editorial roles at CMP Media’s VAR Business and Channel Insider when it was still the legendary publication we all loved from Ziff Davis Enterprise. That editorial background gave her an unusually sharp eye for what MSP practitioners actually need to understand versus what researchers like to measure.

She then spent 15 years at CompTIA, rising from Director to Senior Director of Industry Analysis, where she became a go-to voice on managed services, cloud adoption, and the evolution of the IT channel business model. When CompTIA rebranded as GTIA, she moved into the VP role overseeing the full research and market intelligence function.

Carolyn is, of course, a frequent speaker at channel events including GTIA’s own annual ChannelCon and is regularly quoted across all channel trade press. Her research focus lately has centered on AI adoption maturity among IT service providers and MSPs.

The Statistic That Should Stop You Cold

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Most MSP’s today are all about their stack!

You’ve connected all the tools you want in your stack. You spin up agents, build prompts, deploy LLMs. But if the data you’re feeding these tools is fragmented, stale, or unverified, every one of those AI responses is a liability waiting to strike.

Thing is, it’s not a technology problem. It’s a data problem. And it’s the problem Liongard has been quietly solving for years, long before most of the channel even knew it needed solving.

It’s really all about the data.

The MSP Problem Liongard Has Been Solving for Years

I’ve always found Liongard to be among the most innovative toolbuilders in the MSP ecosystem, bringing a fresh and different approach to solving MSP challenges. Founded in 2015 by MSP veterans, Liongard spent its first decade building what they called their Deep Data Platform, a system that continuously interrogates the full IT stack, normalizes what it finds, and maintains a structured, queryable record of every asset, identity, and configuration across every client environment.

The core capability was change detection: automated asset discovery and inventory, documented up to three times daily, with historical timelines that let an MSP go back and see exactly what changed before a site went down or a security incident occurred.

That became the foundation of their attack surface management positioning, continuous monitoring of devices, users, cloud assets, software, and applications, identifying security gaps and misconfigurations and enforcing security policies to strengthen cyber resilience.

Then, in July 2025, they launched LiongardIQ, what CEO Michelle Accardi called “Liongard revolutionized” unifying asset inventory, network monitoring, AI insights, and remediation into a single platform, and positioning the company as the system of authority not just for documentation and change management, but for everything downstream that depends on knowing the truth about an environment.

Their new MCP server is the next logical step in that arc: not a pivot, but a progression. The foundation they spent ten years building is now the foundation your AI runs on.

On April 16, 2026, Liongard announced the expansion of LiongardIQ with a new MCP server, natural language querying via the Roar Assistant, enhanced network discovery, and deeper identity mapping. It’s a significant release.

To appreciate why it matters so much to MSPs, first examine the concept they’ve built their entire platform around: asset intelligence.

The Data Problem

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The Digital Worker

Not a new term for “AI Agent” as much as an important way of thinking about them and the role they play in your customers’ operations. You’ve heard them defined as systems that don’t just answer questions. They plan, reason, and act. They connect to your client’s ticketing system, their CRM, their financial platform. They execute workflows. They operate.

New is What You Do for a Living

One core concept that I always include in any training I give is to remind everyone that it is not what is, nor how it works. It’s all about what it does.

As such, whenever a significant new technology wave hit the SMB market over the years, the MSPs who thrived weren’t the ones who became experts in the technology itself. They were the ones who became experts in deploying it responsibly. That demanded consultative services starting with the initial consulting and design of the solution, the architecting of the data structures, the application of business operating acumen, skills in adult learning, and ongoing support that includes operational advice and guidance.

When virtualization arrived, somebody had to design the architecture, manage the dependencies, and make sure nothing critical fell through the cracks. When cloud came, somebody had to figure out what moved, what stayed, what the compliance implications were, and how to keep it all observable and secure.

That somebody was you. Always you.

Implementing AI agents, the new digital workers, is no different. And the requirements are identical: governance, workflow integration, observability, compliance, and security.

Those aren’t afterthoughts in an AI deployment. They’re the key to successful deployment.

The Good News: SMBs Can’t Do It Alone

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AI Brings Platform Explosion

With everyone rabidly focused on LLMs, you may not have noticed the emergence of many very interesting system platforms that leverage AI to make magic.

Note-taking itself has undergone a transformation. What many people use today is far more than a note-taking platform. It does much more. When you adopt one you’ll soon find it has far more functionality than you first realized. These platforms are variously referred to as a wiki, project manager, CRM, knowledge base, knowledge management system, client portal, or second brain. Much more than just note-takers.

Integration

If there’s one word that has always been important to our IT channel community, it’s “integration.”

Many of us have referred to ourselves and our companies as “System Integrator” or “Network Integrator” of “Solutions Integrator.” From the start we distinguished ourselves and served our customers best by bringing the best-in-breed of each component of our customer solutions. We are, deep in our souls, integrators. We integrate.

And so it was that I commenced a search for the best platform I could integrate to get AI to help me do what I do. It had to be easy to maintain, easy to use, highly accessible from all my various devices, user friendly, highly integratable with as many other platforms and applications as possible, and driven in every way by AI so I could really fulfill the goal of having a true digital AI assistant.

I found an explosion of candidate products, including Asana, Monday.com, Confluence (Atlassian), Microsoft Loop, Obsidian, Anytype, Airtable, and more. Each handled a piece of what I wanted, but I’d have to maintain multiple products to get everything I wanted.

A Champion Emerges

I finally came upon Notion. I really couldn’t avoid it given the plethora of videos and articles that barrage us constantly. Notion could easily handle everything on my list. In fact, it opened my mind wider when I looked at Notion’s list of capabilities:

Knowledge Management

• Company wiki and internal documentation

• SOPs and process documentation

• Employee handbooks and policy libraries

• Meeting notes and agendas

• Research repositories

• Personal knowledge bases (”second brain”)

Project Management

• Task tracking with assignees, due dates, and priorities

• Kanban boards

• Timeline/Gantt views

• Calendar views

• Sprints and milestones

• Roadmaps

• Status tracking and progress dashboards

Database & Data Management

• Relational databases with linked records

• Filtered and sorted views of the same data

• Rollup and formula fields

• Gallery, table, board, list, and calendar views of any database

• Data entry forms

Document Creation

• Long-form writing and rich text documents

• Embeds (video, maps, code, PDFs, etc.)

• Templates: Both personal and team-wide

• Publishing pages publicly to the web

Collaboration

• Real-time multi-user editing

• Comments and mentions

• Page permissions and access controls

• Guest access for external collaborators

• Team spaces with separate permissions

Client-Facing Work

• Client portals

• Shared project workspaces

• Deliverable tracking and approvals

• Branded public pages

CRM & Sales

• Contact and company databases

• Deal pipeline tracking

• Interaction logging

• Follow-up task management

HR & People Operations

• Onboarding checklists and workflows

• Employee directories

• Job tracking and recruiting pipelines

• Performance review templates

• Time-off tracking

Content & Marketing Operations

• Editorial calendars

• Content pipelines with status tracking

• Campaign planning and tracking

• Asset libraries

• Brand guidelines documentation

Finance & Operations (lightweight)

• Budget tracking

• Vendor and contract management

• Invoice and expense logging

• OKR and goal tracking

Automation

• Automated property updates triggered by status changes

• Recurring task creation

• Slack, email, and Zapier/Make integrations

• API access for custom integrations

• Notion AI for summarization, drafting, Q&A over workspace content

IT & Technical Teams

• Bug and issue tracking

• Sprint planning

• Technical documentation and runbooks

• Incident logs

• Ticketing (lightweight)

Why Am I Telling You All This?

Having begun to build my own business operations system on Notion, I quickly realized what a great opportunity it is for MSPs. Just like the “Solution Providers” I grew up with developing solutions of top of database products and document managers, today’s MSP can build a universe of excellent customer solutions with Notion.

Think about what you charge for an M365 or Google Workspace deployment. Now think about a Notion engagement with comparable scope:

· Discovery and architecture: You’re diagnosing a business, mapping their workflows, and designing a system that fits. That’s a billable engagement easily commanding fees of $1,500 to $5,000 depending on complexity. And that’s just the start!

· Build and configuration: A well-built Notion workspace for a 20-person company can run 40 to 80 hours of work. Templates, databases, relational properties, automations, integrations. All of it has to be designed and built. Price accordingly.

· Training: Notion’s flexibility is only valuable if people know how to use it. Training engagements for admins and end users both are a natural add-on.

· Ongoing Design and User Support and iteration: Business needs change. Businesses change! Notion implementations need maintenance, but they also frequently call for expansion, and improvement. A constant flow of consultative engagements. Now that’s a recurring services opportunity, either hourly or retainer.

This is high-margin professional services work. You’re not shipping hardware or managing licenses. You’re designing something of value, developing it, building it, helping your customers get the most out of it, and you’re doing it with a relatively low tooling cost on your side. Best of all, it calls for the same skills you’ve developed and delivered to customers for years.

And it puts you firmly in the AI business!!!

Why Now

Notion is adding AI capability aggressively. Notion AI is already embedded in the platform bringing summarization, writing assistance, Q&A over workspace content. As customers start to ask for AI-augmented operations, a Notion practice positions you at that intersection without requiring deep AI engineering. You’re implementing a platform that already has it baked in.

The competitive moat is also still wide open. There are no established Notion practices in the channel the way there are for Microsoft or Google. If you build this now, you’re not fighting for scraps. You’re establishing a position.

Excerpted from the AgenticMSP Substack. Read the entire article https://agenticmsp.substack.com/p/what-a-notion-msps-drive-new-revenue

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If nothing else, I’m loyal!

Loyal to my tools, that is.

In the 1980’s I started using a Franklin Covey Planner to manage my time & tasks. It was a well-designed, comprehensive system. It required constant updating and moving things around but it always felt good crossing off a completed item with an exaggerated swoosh of my hand. That was, however, the only thing that really felt good. Otherwise, it really sucked to use.

I bought a new one every year for 19 years.

In the mid-2000’s I attempted the impossible. I tried to actually use Microsoft Outlook Tasks to replace my Franklin Covey Planner. I built all kinds of workarounds and kludges to make it work but, ultimately, it was as impossible as everyone had warned me it would be. But did I listen? No. Ten years.

Ultimately, after that ten years of torture, I found Todoist. I cannot say enough good about what is now the #1 task management platform according to many reviewers, and I won’t say anything here and now, because we’re here to talk about another kind of tool.

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Remember when you first experienced describing a software feature in plain English and watching your AI assistant just... build it? That rush. That disbelief. That feeling of “I can make anything now.”

That was vibe coding. And it was real. And it was wonderful.

It was also, as it turns out, just the party before the real work began.

The Year That Changed Everything

In February 2025, Andrej Karpathy coined the phrase “vibe coding” to describe a new way of building software: describe what you want in plain English, let an AI generate the code, run it, identify and ask for changes and improvements to iterate, then repeat. It was loose, fast, and enormously fun. It was also, in retrospect, really only Phase One.

In February 2026, almost exactly one year after he invented the term, Karpathy announced that the era of vibe coding was effectively over.

On X, he posted what has since become a widely cited framework for what comes next: “Today (1 year later), programming via LLM agents is increasingly becoming a default workflow for professionals, except with more oversight and scrutiny. The goal is to claim the leverage from the use of agents but without any compromise on the quality of the software.”

He proposed a new name for this evolved practice: agentic engineering. His explanation of the term is worth quoting in full, because it defines a hiring profile, a service category, and a competitive moat all at the same time:

“’Agentic’ because the new default is that you are not writing the code directly 99% of the time. You are orchestrating agents who do and acting as oversight. ‘Engineering’ to emphasize that there is an art & science and expertise to it. It’s something you can learn and become better at, with its own depth of a different kind.”

In a separate post, Karpathy doubled down on the value of the discipline: “The leverage achievable via top tier ‘agentic engineering’ feels very high right now. It’s not perfect — it needs high-level direction, judgment, taste...”

Those last three words — direction, judgment, taste — are the crux of everything that follows.

Boris Cherny Draws the Line

If Karpathy named the new era, Boris Cherny, the head of Anthropic’s Claude Code, is truly living it. In early 2026, Cherny revealed that 100% of his production code is now generated by AI, and that he hadn’t written a line of code by hand since October 2025. Speaking at Anthropic’s developer conference and in a widely circulated Business Insider interview, Cherny made clear that he finds the phrase “vibe coding” actively counterproductive in that it implies sloppiness, impermanence, and a lack of rigor.

Cherny’s pushback is significant precisely because Claude Code is one of the tools that enabled vibe coding to go mainstream in the first place.

He isn’t sentimental about the term. When asked what should replace it, he reportedly put the question to Claude itself, which suggested “agentic engineering,” echoing Karpathy. Cherny’s broader prediction that the job title “software engineer” will begin to disappear in 2026, absorbed into more hybrid roles, is a signal that the identity of the profession is in transition, not just its tooling.

His most pointed observation for anyone thinking about the workforce implications: engineers at Anthropic are no longer writing code. They are reviewing, directing, and governing the agents that do the work.

What Spec-Driven Development Looks Like in Practice

So, what does better actually look like in practice?

Actually, it looks rather familiar to anyone who has ever spent any time developing or participating in the development of software. Once discovery is accomplished, the next best practice to perform is the creation of a functional specification document.

A recent piece in Towards Data Science by Mariya Mansurova offers the clearest practical illustration of agentic engineering in action. Mansurova spent 4.5 hours building a fully functional personal fitness tracking web application, from idea to deployed product, using Claude Code inside VS Code, guided entirely by the principles of spec-driven development (SDD).

The contrast with vibe coding is instructive. Vibe coding, as Mansurova describes it, looks like this:

· Write a short prompt

· Wait for the agent to create the code

· Discover it didn’t do what you wanted

· Iterate in the same chat window requesting changes

· Lose context

· Start over

It works for throwaway scripts. It does not scale to production software, multi-developer teams, or anything where architectural decisions need to persist.

Spec-driven development inverts the workflow. Before a single line of code is generated, the developer produces a constitution, a set of structured markdown documents stored in the repository alongside the code. This constitution includes:

· A mission document (the why of the project)

· A tech stack document (the how)

· A roadmap (the what and when).

Only after this architecture is validated through review, iteration, and even a second-agent “critique pass” does implementation begin.

Each feature phase then follows a disciplined cycle: plan, implement, validate. Crucially, the specification is updated continuously and serves as the source of truth for both human developers and AI agents. Context doesn’t decay across sessions because it’s externalized into the repository, not kept alive in a chat window.

Mansurova’s key observation captures the cultural shift perfectly: “With agentic engineering, the role of the developer shifts toward steering, reviewing, and making architectural decisions, rather than directly writing specifications or code.”

Simon Willison, the Django co-creator and one of the most respected voices in applied AI development, has been mapping this territory for months. In late 2025 he proposed “vibe engineering” as a middle term, structured enough to be serious, fast enough to stay agile, and has since published an evolving guide to Agentic Engineering Patterns, a practical catalog of the specific techniques that separate production-grade AI-assisted development from demos. His framing: automated testing, advance planning, comprehensive documentation, and disciplined version control habits aren’t optional enhancements to agentic engineering. They are what defines it.

JetBrains, working with DeepLearning.AI, has formalized this workflow into a short course, “Spec-Driven Development with Coding Agents.” GitHub has released Spec Kit, an open-source CLI tool that brings the SDD workflow to any agent environment, with 30 integrations already supported. The methodology is standardizing rapidly.

Why This Is an MSP Problem — and an MSP Opportunity

Here is the market signal MSPs cannot afford to ignore: in April 2026, KPMG published a managed services outlook survey finding that more than 90% of executives now view managed services as essential for delivering agentic AI at scale. The gap between wanting agentic AI capabilities and being able to build and govern them internally is enormous — and that gap is where managed services providers traditionally live.

But there is a critical catch. The managed services market for agentic engineering is not the same as the managed services market for cloud infrastructure, helpdesk support, or even earlier generations of AI tooling. Customers who are moving from vibe coding to agentic engineering are not looking for someone to provision compute or manage SLAs. They are looking for partners who can do three things that most MSPs do not yet do well:

1. Speak the language of functional specification before implementation. The clients beginning to invest in agentic engineering have internalized Karpathy’s framework: quality is non-negotiable, and quality requires upfront architecture. MSPs who lead with tool recommendations or time-and-materials development engagements will lose these conversations to firms that open with constitution workshops, collaborative sessions to define mission, tech stack, and roadmap before any agent writes a line of code. The JetBrains/DeepLearning.AI SDD curriculum is a reasonable starting point for building this practice. Investing in staff certifications here is not optional, it is a key differentiator.

2. Offer structured oversight, not just execution. Karpathy’s definition of agentic engineering is built around oversight and judgment. Cherny’s daily workflow is built around review and governance of AI-generated code, not the writing of it. The MSPs that will win agentic engineering projects are those that can demonstrate a repeatable governance model: how do you validate that what the agents produced actually matches the specification? How do you manage context across sessions, across developers, across feature phases? Customers who understand spec-driven development will ask these questions directly. MSPs need answers and documented methodologies and processes, before those meetings happen.

3. Bring deep domain expertise, not just AI tooling. One of the most consistent themes across Karpathy’s posts, Cherny’s interviews, and Mansurova’s practical walkthrough is that agentic engineering amplifies human judgment, it does not replace it. Karpathy’s choice of the word “taste” is deliberate. What guides the agent is the human’s architectural sensibility, business context, and understanding of what “good” looks like in a specific domain. MSPs that have cultivated genuine vertical expertise in healthcare workflows, financial services compliance, manufacturing operations, or retail analytics have a structural advantage here that pure-play AI vendors do not. The constitution documents at the heart of SDD are only as good as the domain knowledge that goes into them.

The Transition No One Has Fully Priced In

The loudest conversation in the market right now is about whether AI replaces developers. That is the wrong question for MSPs. The right question is: who is qualified to act as the human-in-the-loop when the agents are doing the work?

Cherny’s statement that the “software engineer” title will start to disappear doesn’t mean engineering skills disappear. It means the job is being redefined around orchestration, oversight, and specification, exactly the skills that managed service providers are positioned to offer at scale, if they invest in building them now.

The KPMG HFS Horizons: Agentic Services, 2026 report documents that enterprise buyers are actively seeking partners to help them navigate this transition. GitLab expanded its MSP partner program in February 2026 explicitly to meet growing demand for agentic AI across the software lifecycle. The demand is there. Now you need to be.

What is less clear is whether most MSPs are ready to meet the demand. Organizations still leading with “we can help you implement Copilot” are speaking the language of 2024. The customers who have watched Karpathy’s posts, read Cherny’s interviews, and are beginning to experiment with Claude Code and Spec Kit are already asking different questions. They want partners who can help them build a development practice, not just a development tool stack, that is governed, reproducible, and genuinely production-grade.

What MSPs Should Do Right Now

The shift from vibe coding to agentic engineering creates a five-part playbook for MSPs willing to invest ahead of the curve:

1. Develop a spec-driven development practice. Train a dedicated team on SDD methodology. Build internal templates for constitution documents, feature phase plans, and validation checklists. Pilot the workflow on an internal project before selling it to customers.

2. Build governance and oversight frameworks. Define how you review AI-generated code to assure quality, how you track architectural decisions over time to maintain consistency, and how you manage context decay across long-running engagements. These are the questions sophisticated customers will ask.

3. Invest in domain expertise, not just tooling. Your competitive moat in agentic engineering is not driven by the AI model you use. Customers can access those directly. It is the accumulated domain knowledge that makes your specifications better than a generalist’s. Identify the verticals where you have real depth and build SDD offerings designed for those contexts.

4. Offer a “constitution workshop” as a lead service. Position the upfront specification and architecture work as a standalone, high-value, fee-based service. It is the natural entry point into an agentic engineering engagement and it demonstrates the discipline that differentiates you from commodity AI integrators.

5. Stay current with the practitioner community. Simon Willison’s Agentic Engineering Patterns guide, JetBrains’ SDD curriculum, GitHub’s Spec Kit, and the ongoing commentary from Karpathy and Cherny are where the methodology is being actively developed. The practitioners who will become your customers are following this work closely. You need to be leading the charge, not catching up.

The era of vibe coding gave everyone a taste of what AI-assisted development could feel like.

The era of agentic engineering is defining what results and superior business outcomes it needs to produce. MSPs that make that transition, from vibing to specifications, from tooling to governance, from execution to oversight, will find themselves at the center of one of the largest service market opportunities this channel has ever expperienced. Those that don’t will find themselves selling yesterday’s answer to tomorrow’s question.

Sources drawn from: Mariya Mansurova, “From Vibe Coding to Spec-Driven Development,” Towards Data Science (May 2026); Andrej Karpathy posts on X (February 2026); Boris Cherny interviews, Business Insider and CNBC (May 2026); Simon Willison’s Agentic Engineering Patterns guide (simonwillison.net); KPMG Managed Services Outlook Survey 2026; HFS Horizons: Agentic Services 2026; GitLab MSP program expansion announcement (February 2026).

• From Vibe Coding to Spec-Driven Development

• Google Cloud is hiring an army of AI deployment engineers

• Tutorial: How To Cut Claude Code Costs by 50% (Token-Saving Tricks That Actually Work)

• An AWS user just stared down a $30,000 invoice after a Claude adventure on Bedrock with no guardrails catching it

• Agentic AI’s Hidden Data Trail—and How to Shrink It

• AI Integration Into PSA and Security Platforms Forces ... - YouTube

• ResolveGrid Launches Agentic AI Platform That Cuts Field Service Dispatches in Half

• Building an Evaluation Harness for Production AI Agents: A 12-Metric Framework From 100+ Deployments

• How to learn Claude Code for free with Anthropic’s AI courses - one took me just 20 minutes

From Vibe Coding to Spec-Driven Development

HMC: This is the Mariya Mansurova, “From Vibe Coding to Spec-Driven Development” article I refer to in today’s lead article. Mansurova goes into much more detail about how to develop your ability to produce a meaningful and useful functional spec to drive your agentic development efforts.

Summary: The article documents a 4.5-hour end-to-end development session building a functional fitness app using LLM-assisted ‘spec-driven development’ — a structured alternative to ‘vibe coding’ (ad hoc, prompt-by-prompt AI coding). The author argues that providing LLM agents with a detailed specification document upfront produces far more coherent, maintainable code than iterative prompting without structure. The workflow involves writing a formal spec first, then using LLM agents to implement against it, resulting in a working app in under five hours. The piece highlights both the speed gains and the importance of human-defined structure to keep AI-generated code on track.

MSP Relevance: MSPs exploring AI-assisted development for building client tools, automations, or internal utilities will find the spec-driven approach practically useful. Rather than ad hoc AI coding that produces hard-to-maintain output, writing a structured spec first and feeding it to an LLM agent yields more reliable, reviewable code. This is relevant for MSPs who are building lightweight custom apps, automation scripts, or client portal features using AI coding assistants like GitHub Copilot, Cursor, or similar tools. The 4.5-hour timeline also sets realistic expectations for rapid prototyping. However, the article is focused on a consumer app example and doesn’t address enterprise or MSP-specific development contexts, limiting its direct applicability.

Recommended Action Items:

• Adopt a spec-first workflow when using AI coding assistants for client automation or tool development projects

• Create a reusable spec template for common MSP development tasks (e.g., RMM integrations, ticketing automations) to accelerate AI-assisted builds

• Pilot spec-driven development on a small internal tool to benchmark time savings vs. traditional development

Source: https://towardsdatascience.com/from-vibe-coding-to-spec-driven-development/

Google Cloud is hiring an army of AI deployment engineers

HMC: Both OpenAI (DeployCo) and Google (hundreds of new implementation engineers) are making direct investments in enterprise AI implementation. That’s the space you’re working to expand into. This is not a future threat; it is a threat right now. MSPs must accelerate their AI practice development, certifications, and client relationships to enable themselves to compete against their own suppliers.

This is not new. I recently wrote about my own experience in 1981 when IBM decided to sell service contracts on their new IBM PC and XT. The channel was flabbergasted. An IBM exec explained to me that their offering the service validated the need, and the channel can always beat them on price.

Let’s see if that’s true again now.

Summary: Google Cloud is aggressively hiring ‘forward-deployed engineers’ — a role focused on hands-on AI deployment directly with enterprise customers. This hiring push signals Google Cloud’s intent to accelerate AI adoption by embedding technical talent within customer environments to drive implementation and ROI. The strategy mirrors what firms like Palantir have done with embedded engineering. The move suggests Google Cloud sees a significant gap between AI interest and actual deployment, and is investing in human capital to bridge it. This is a broader market signal about the state of enterprise AI adoption.

MSP Relevance: This hiring trend reveals a critical market gap: enterprises want AI but struggle to deploy it effectively. MSPs are well-positioned to fill exactly this role for SMB and mid-market clients who won’t get Google’s forward-deployed engineers. The fact that a hyperscaler is investing heavily in deployment talent — not just product — validates the MSP value proposition around AI implementation services. MSPs should position themselves as the ‘forward-deployed AI engineers’ for their client base, offering hands-on deployment, integration, and ongoing optimization of AI tools. This also signals that Google Cloud partner programs may evolve to leverage MSPs in this deployment capacity.

Recommended Action Items:

• Develop an AI deployment service offering that mirrors the ‘forward-deployed engineer’ model — hands-on implementation, not just licensing.

• Monitor Google Cloud partner program updates for opportunities to become a certified AI deployment partner.

• Build internal AI deployment competency by training staff on Google Cloud AI tools and Vertex AI platform.

Source: https://link.ciodive.com/click/45733286.52564/aHR0cHM6Ly93d3cuY2lvZGl2ZS5jb20vbmV3cy9nb29nbGUtY2xvdWQtZm9yd2FyZC1kZXBsb3llZC1lbmdpbmVlcmluZy1qb2JzLzgyMDIzMy8/63925805977e933ecfa89aabBc3e41e65

Tutorial: How To Cut Claude Code Costs by 50% (Token-Saving Tricks That Actually Work)

HMC: Just last week I wrote about how cost control, which is usually the first concern, took its time to show up in AI. I pointed out that the geniuses at OpenAI spend $1.35 to earn every dollar of revenue they earn. The obvious consequence will be substantial rises in cost to users. The only answer for customers is to find ways to dramatically reduce token consumption. This article offers several ways to accomplish that.

Summary: This tutorial focuses on practical techniques to reduce Claude Code token consumption by approximately 50%, covering token-saving strategies that have been tested and validated. Topics likely include prompt optimization, context window management, caching strategies, and efficient instruction patterns to minimize API costs while maintaining output quality when using Claude Code for agentic coding tasks.

MSP Relevance: For MSPs actively using or planning to use Claude Code to build automation scripts, integrations, or client AI solutions, reducing API costs by 50% is directly impactful to profitability. As MSPs scale AI usage across multiple clients or internal workflows, token costs compound quickly. Practical cost-control techniques allow MSPs to build more sustainable AI service margins. This is actionable for any MSP using Anthropic’s API in production or development environments.

Recommended Action Items:

• Review and implement the token-saving techniques outlined in the tutorial for all Claude Code-based development workflows.

• Establish internal guidelines for prompt engineering that incorporate cost-reduction best practices.

• Factor token optimization strategies into pricing models when building AI services billed on consumption.

• Test caching and context management techniques to reduce costs on repetitive MSP automation tasks.

Source: https://substack.com/redirect/5c208e87-c0df-4fa6-9b71-e944e875fb60?j=eyJ1IjoiMXFkcml1In0.SNK1ypAhlwqm7cRi31g3ag7Hox1C89I2CuowSbV0KXg

An AWS user just stared down a $30,000 invoice after a Claude adventure on Bedrock with no guardrails catching it

[Cost Anomaly Detection failed entirely]

HMC: Here’s a cautionary tale you won’t want to miss, and won’t want to miss sharing with your customers as you propose measures to protect them against experiencing the same horror show.

Reading this brought back memories of the earliest days of Azure when it was still called Microsoft Azure and Microsoft had provided no mechanism for tracking utilization, now called consumption, until the arrival of the monthly “overages” statement which invoiced all utilization beyond the subscribed amount.

When customers started receiving invoices in the amount of tens or hundreds of thousands of dollars the screams could be heard around the C-Level and many careers were cut short.

Here we are again.

Summary: An AWS user was hit with a $30,000 bill after running Claude on AWS Bedrock without proper cost controls in place. AWS’s built-in Cost Anomaly Detection failed to catch the runaway spending before it escalated to a massive invoice. The incident highlights a critical gap in cloud AI cost governance — when AI workloads (especially LLM inference) run unconstrained, costs can spiral rapidly and existing cloud billing safeguards may not respond quickly enough to prevent significant financial damage. The story is a cautionary tale about deploying AI services on consumption-based cloud platforms without implementing hard spending limits, budget alerts, and usage guardrails at the application and API level.

MSP Relevance: This is highly relevant for MSPs building or managing AI solutions on cloud platforms for clients. MSPs who deploy AI workloads on AWS Bedrock, Azure OpenAI, or similar consumption-based services face real liability if client environments lack proper cost guardrails. This incident demonstrates that native cloud cost controls (like AWS Cost Anomaly Detection) are NOT sufficient protection against runaway AI inference costs. MSPs need to proactively implement hard API usage limits, per-session token caps, budget alerts with auto-shutdown triggers, and client-facing cost dashboards. This is also a strong selling point for MSP-managed AI governance services — clients who self-deploy AI without MSP oversight face exactly this risk.

Recommended Action Items: Audit all client AWS Bedrock, Azure OpenAI, and similar AI API deployments for hard spending limits and token usage caps

• Implement multi-layer cost controls: API-level rate limits, AWS Budgets with SNS alerts, and auto-shutdown Lambda functions triggered at spend thresholds

• Create a standard AI cost governance checklist to include in all AI project scopes and managed service agreements

• Use this incident as a client education opportunity to position MSP-managed AI oversight as a risk mitigation service

Source: https://click.redditmail.com/CL0/https:%2F%2Fwww.reddit.com%2Fr%2Fartificial%2Fcomments%2F1tcu7w5%2Faws_user_hit_with_30000_dollar_bill_after_claude%2F%3F%2524deep_link=true%26correlation_id=bef402c2-fcdc-4551-94be-62807d10b945%26post_fullname=t3_1tcu7w5%26post_index=4%26ref=email_digest%26ref_campaign=email_digest%26ref_source=email%26target_user=hmcwizard%26utm_content=post_body/1/0100019e29b5ff75-c2dc9f17-a085-4ce1-bd19-db2caee1a20f-000000/j9bs-sDJZzV1L3Q6tqVBeIcNzZNYqf2IVFAWEOzYyqE=452

Agentic AI’s Hidden Data Trail—and How to Shrink It

HMC: Members of the Institute of Electronic and Electrical Engineers (IEEE) resents our channel’s use of the term “engineer” to describe our professionals, citing the significant investments they’ve made in earning that title.

For those of us who love to get “under the hood” of the technologies we work with, the IEEE Spectrum articles are a joy to read. Much deeper insight than you’ll find anywhere else. And when they address the lifeblood of our channel, I want to be sure everyone is aware of it and has a chance to benefit from it.

Summary: This IEEE Spectrum article addresses the security and privacy risks created by agentic AI systems, specifically focusing on the data trails these systems generate during operation. The core argument is that AI agents — which autonomously execute multi-step tasks, call APIs, access files, and interact with external services — accumulate significant data footprints that create security and compliance liabilities. The article advocates for three key principles: minimizing data retention by agents, enforcing task-specific (least-privilege) permissions rather than broad access, and implementing strict data expiration policies so sensitive information isn’t retained beyond its useful life. The piece highlights that unlike traditional software, AI agents often cache context, store intermediate results, and log interactions in ways that aren’t immediately visible to administrators. This creates hidden attack surfaces and potential compliance violations, particularly in regulated industries. The guidance is framed around designing agentic systems with privacy-by-default architectures rather than bolting on controls after deployment.

MSP Relevance: As MSPs build and deploy agentic AI solutions for clients — automating helpdesk triage, IT operations, security monitoring, or business workflows — they inherit responsibility for the data trails those agents create. This article provides a concrete security framework MSPs can apply both internally and in client deployments. Practically, MSPs should audit every agentic workflow they deploy to understand what data is cached, logged, or retained. For regulated clients (healthcare, finance, legal), data expiration policies and least-privilege agent permissions aren’t optional — they’re compliance requirements. MSPs can differentiate their AI practice by building governance controls into their agentic solutions from day one: scoped API credentials per task, automatic log purging, and audit trails that show clients exactly what data their AI agents touched. This is also a strong sales conversation starter — MSPs can position themselves as the ‘responsible AI’ partner who builds secure-by-design agentic systems, versus competitors who deploy agents without governance guardrails.

Recommended Action Items:

• Audit all current agentic AI deployments (internal and client-facing) to inventory what data is retained, cached, or logged by each agent

• Implement least-privilege permission models for all AI agents — create task-specific credentials rather than using broad admin-level API keys

• Establish data expiration policies for agent logs and intermediate data stores, aligned to client compliance requirements (HIPAA, SOC 2, etc.)

• Develop a standard ‘Agentic AI Security Checklist’ to include in your AI service delivery framework and use it as a client-facing differentiator

Source: https://spectrum.ieee.org/agentic-ai-security

AI Integration Into PSA and Security Platforms Forces ... - YouTube

HMC: Our dear friend Dave Sobel’s podcast The Business of Tech on MSP Radio brings us Dave’s unique combination of decades of channel experience, deep technical grounding, a thorough understanding of the dynamics of the IT industry, his tremendous AI and digital production skills, and a truly enjoyable and insightful host to bring it all together.

Some of the earliest AI applications developed supported help desk and service desk operations. In this episode, Dave explores how AI is now bringing more and more value, more and more autonomously, to every MSP’s service operations. For an MSP, this may be one of the most critical topics we’ve addressed.

Summary: This YouTube episode focuses on a structural shift in managed service environments: AI is no longer just a reporting or analytics layer but is becoming an active workflow actor embedded directly within PSA and security platforms. The episode examines how AI integration into core MSP tooling — ticketing, alerting, incident response, and security workflows — changes how services are delivered. The framing is that AI is moving from passive assistant to autonomous participant in MSP operational processes, triggering actions, routing tickets, and responding to security events without human initiation.

MSP Relevance: This is highly relevant to MSPs actively building or evolving their AI practice. The core insight — AI as an active workflow actor in PSA and security platforms — directly maps to where the MSP industry is heading. MSPs need to understand how to configure, govern, and sell AI-driven automation within tools like ConnectWise, Autotask, Datto, or SentinelOne. This episode likely covers practical integration patterns MSPs can adopt today, such as AI-driven ticket triage, automated escalation, and AI-assisted threat response. MSPs should watch this to understand both the operational implications (how to restructure workflows) and the sales implications (how to package AI-enhanced services for clients at a premium).

Recommended Action Items:

• Watch the full episode and document specific AI integration patterns discussed for PSA and security platforms relevant to your current toolstack.

• Audit your current PSA and security platform AI capabilities — identify gaps where AI could become an active workflow actor rather than a passive reporting tool.

• Develop a tiered service offering that differentiates AI-automated response (faster SLA, lower cost) from human-managed response for client proposals.

• Evaluate AI workflow automation features in your PSA (e.g., ConnectWise Sidekick, Autotask AI) and create a 90-day implementation roadmap.

Source:

ResolveGrid Launches Agentic AI Platform That Cuts Field Service Dispatches in Half

HMC: Following Dave Sobel’s excellent podcast on the subject, here’s one company’s solution offering for MSPs seeking to drive quality up while driving expense and error rate down.

Summary: ResolveGrid has launched an agentic AI platform specifically targeting field service operations, claiming to reduce field service dispatches by 50%. The platform uses AI agents to autonomously triage incoming service requests, diagnose issues remotely, and resolve problems without requiring a technician dispatch. It integrates with existing field service management (FSM) and ticketing systems, applying multi-step reasoning to determine whether an issue can be resolved remotely before escalating to human dispatch. The platform is positioned for industries with high field service volumes — utilities, facilities management, and IT services. Key capabilities include automated root cause analysis, guided remote resolution workflows, and predictive dispatch prioritization. The company reports early customers have seen significant reductions in truck rolls and mean time to resolution (MTTR).

MSP Relevance: This is highly relevant for MSPs with field service or on-site support components. Reducing unnecessary dispatches directly cuts labor and travel costs — one of the highest cost drivers in break-fix and managed services. The agentic triage model ResolveGrid uses mirrors what MSPs should be building or adopting: AI agents that attempt remote resolution before escalating to human technicians. MSPs should evaluate this platform or similar agentic dispatch tools as a way to improve margins on service delivery. Even if ResolveGrid itself isn’t the right fit, the architecture — AI triage → remote resolution attempt → human escalation — is a workflow pattern MSPs can implement using existing RMM and PSA tools augmented with AI agents. This also creates a compelling ROI story for clients paying for managed services.

Recommended Action Items:

• Request a demo of ResolveGrid to evaluate fit for your field service dispatch workflows

• Map your current dispatch decision tree and identify steps where an AI agent could attempt remote resolution before escalating

• Quantify your current truck roll rate and cost-per-dispatch to build an ROI model for agentic triage adoption

• Explore whether your existing RMM/PSA vendor offers AI-assisted triage features that replicate this dispatch-reduction pattern

Source:

https://link.ciodive.com/click/45733286.52564/aHR0cHM6Ly93d3cuY2lvZGl2ZS5jb20vcHJlc3MtcmVsZWFzZS8yMDI2MDUxMy1yZXNvbHZlZ3JpZC1sYXVuY2hlcy1hZ2VudGljLWFpLXBsYXRmb3JtLXRoYXQtY3V0cy1maWVsZC1zZXJ2aWNlLWRpc3BhdGNoZXMtMS8/63925805977e933ecfa89aabBe81f45d8

Building an Evaluation Harness for Production AI Agents: A 12-Metric Framework From 100+ Deployments

HMC: Most of us who have been developing any kind of application with Claude Code, Codex, or others have experienced the inconsistency in performance that sometimes occurs when something underneath has changed. AI is a very multi-layered architecture and any change can have ripple effects up and down the stack.

A big part of the MSP value proposition must be to exert vigilance over how well everything is working. We must become the quality hawks. I’ll continue to include articles about evaluation tools and quality controls here in AgenticMSP.

Summary: This article presents a 12-metric evaluation framework for production AI agents, derived from 100+ enterprise deployments. The framework covers four key domains: retrieval quality (e.g., context precision, recall), generation quality (faithfulness, answer relevance), agent behavior (tool selection accuracy, task completion rate, loop detection), and production health (latency, error rates, cost per query, fallback rates). The author argues that most teams instrument their agents poorly — tracking only surface metrics like user satisfaction — while ignoring the deeper signals that reveal why agents fail. The framework is designed to be model-agnostic and applicable across RAG pipelines, multi-step agents, and tool-using systems. Each metric includes a definition, why it matters, and suggested measurement approaches.

MSP Relevance: This is directly actionable for MSPs deploying AI agents for clients. Without a structured evaluation framework, MSPs have no way to guarantee SLA-level performance, diagnose failures, or demonstrate ROI. This 12-metric framework gives MSPs a ready-made quality assurance methodology they can apply to every client AI deployment — from RAG-based knowledge bots to multi-step automation agents. Specifically: retrieval metrics help MSPs tune knowledge base quality; generation metrics catch hallucinations before clients do; agent behavior metrics expose broken tool calls or infinite loops; production health metrics feed into monitoring dashboards and cost management. MSPs can package this framework as part of their AI managed services offering — providing ongoing agent health reporting as a differentiator. It also helps MSPs have credible, data-driven conversations with clients about agent performance rather than relying on anecdotal feedback.

Recommended Action Items:

• Adopt this 12-metric framework as the standard QA checklist for all client AI agent deployments

• Build a monitoring dashboard template covering retrieval, generation, agent behavior, and production health metrics for recurring AI managed services reporting

• Use task completion rate and tool selection accuracy as primary SLA metrics in client AI service agreements

• Implement fallback rate and loop detection alerts in your RMM or observability stack for any deployed AI agents

Source: https://towardsdatascience.com/building-an-evaluation-harness-for-production-ai-agents-a-12-metric-framework-from-100-deployments/

How to learn Claude Code for free with Anthropic’s AI courses - one took me just 20 minutes

HMC: You’re wrong. I’m seldom so absolute about these things, but if you think agentic engineering or even mere vibe coding is beyond your capabilities, you’re wrong. My own recent experience proves this to me. I haven’t touched a stitch of programming since 1988 and yet I’ve been able to produce some very useful tools for myself in the course of developing this publication and the rest of my writing business.

Give yourself a shot. Get a little training and take Claude Code for a spin. You’ll be amazed at what you can do.

Summary: Anthropic has released a free online course library covering Claude, Claude Code, AI agents, and the Model Context Protocol (MCP). One course highlighted by ZDNet took only 20 minutes to complete. Claude Code is Anthropic’s agentic coding tool that can autonomously write, edit, and execute code. The MCP (Model Context Protocol) is an open standard for connecting AI models to external tools and data sources. The free courses provide structured learning paths for developers and technical professionals looking to build with Anthropic’s AI stack without upfront training costs.

MSP Relevance: This is directly actionable for MSPs building AI practices. Claude Code and MCP are highly relevant tools for MSPs: Claude Code can automate scripting, code generation, and technical tasks that MSP engineers perform daily, while MCP is a foundational protocol for connecting AI agents to client systems, APIs, and data sources — exactly the kind of integration work MSPs do when building AI solutions. Free, short-form courses lower the barrier for MSP technical staff to get hands-on with agentic AI. MSPs can use these courses to upskill their team quickly, then apply Claude Code to automate internal workflows or build client-facing AI solutions. MCP knowledge is particularly valuable as it’s becoming a standard for AI tool integration across vendors.

Recommended Action Items:

• Enroll technical staff in Anthropic’s free Claude Code and MCP courses to build foundational agentic AI skills

• Evaluate Claude Code as a productivity tool for MSP engineers handling scripting, automation, and documentation tasks

• Explore MCP as an integration layer for connecting AI agents to client PSA, RMM, or line-of-business systems

• Build a short internal pilot using Claude Code to automate a repetitive MSP workflow (e.g., script generation, runbook drafting)

Source: https://www.zdnet.com/article/how-to-learn-claude-code-with-free-anthropic-ai-courses-online/

Synthesized from today’s news — recurring themes and highest-priority actions for your AI practice.

1. AI Costs Spiral Without Guardrails

Uncontrolled AI agent usage can generate surprise five-figure cloud bills overnight.

• Set hard AI spending limits immediately

AWS User Hit With $30K Bill

2. Agentic AI Creates Hidden Data Risks

AI agents leave extensive data trails that expand your clients’ attack surface.

• Audit agentic AI data retention policies

Agentic AI’s Hidden Data Trail

3. RMM Phishing Attacks Targeting MSPs

Attackers exploit RMM tools to bypass security and compromise MSP client networks.

• Enable MFA on all RMM platforms now

RMM Phishing Threat Advisory

4. AI Reshaping MSP Service Delivery

MSPs not adopting AI tools risk losing efficiency and clients to competitors.

• Pilot one AI tool in service desk

How AI Tools Reshape MSP Strategies

5. AI Discovers Security Vulnerabilities Faster

Frontier AI models rapidly find vulnerabilities, raising stakes for MSP security practices.

• Add AI-assisted vulnerability scanning to offerings

Frontier AI Security Vulnerability Discovery

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Remember when you first experienced describing a software feature in plain English and watching your AI assistant just... build it? That rush. That disbelief. That feeling of “I can make anything now.”

That was vibe coding. And it was real. And it was wonderful.

It was also, as it turns out, just the party before the real work began.

The Year That Changed Everything

In February 2025, Andrej Karpathy coined the phrase “vibe coding” to describe a new way of building software: describe what you want in plain English, let an AI generate the code, run it, identify and ask for changes and improvements to iterate, then repeat. It was loose, fast, and enormously fun. It was also, in retrospect, really only Phase One.

In February 2026, almost exactly one year after he invented the term, Karpathy announced that the era of vibe coding was effectively over.

On X, he posted what has since become a widely cited framework for what comes next: “Today (1 year later), programming via LLM agents is increasingly becoming a default workflow for professionals, except with more oversight and scrutiny. The goal is to claim the leverage from the use of agents but without any compromise on the quality of the software.”

He proposed a new name for this evolved practice: agentic engineering. His explanation of the term is worth quoting in full, because it defines a hiring profile, a service category, and a competitive moat all at the same time:

“’Agentic’ because the new default is that you are not writing the code directly 99% of the time. You are orchestrating agents who do and acting as oversight. ‘Engineering’ to emphasize that there is an art & science and expertise to it. It’s something you can learn and become better at, with its own depth of a different kind.”

In a separate post, Karpathy doubled down on the value of the discipline: “The leverage achievable via top tier ‘agentic engineering’ feels very high right now. It’s not perfect — it needs high-level direction, judgment, taste...”

Those last three words — direction, judgment, taste — are the crux of everything that follows.

Read more

Many MSPs feared their value proposition was slipping away back when cloud first started emerging. Systems hardware sales, with all their lucrative attached services, dwindled fast. In many cases that was what converted many “resellers” into MSPs in the first place!

With all the billions and trillions of dollars we read about being invested, it is kinda cool that it is the economics of the thing that will drive AI back into our customers’ premises and back into our lovin’, service-providing arms.