Step 7: We Almost Broke Everything

📍 Part 7 of 8 · Becoming Agent-Native
An 8-part series on going from delivery team to agent-native organization — lessons earned, not borrowed.
Genesis · Anxiety · Names Matter · Proof of Value · The Pivot · Co-Creation · → The Garage · The Flywheel

Let me describe the period just before we built the Garage.

High creativity. Great energy. Agents proliferating across the team.

Underneath it: loose operational foundation at best. Governance ideas and concepts.

Flailing.

Here’s what “flailing” actually looked like:

Seven agents doing roughly the same thing. No design pattern to rationalize them. A delivery team member builds an agent, it works great, she goes on vacation, the model updates, it breaks, nobody knows who to call.

Some agents didn’t log to the dashboard. Some logged multiple entries. Some logged events for failed runs. Agents that hadn’t been through a security review. Agents that got stuck in loops and churned resources. Agents without error handling.

None of it malicious. All of it predictable.

Momentum without infrastructure eventually produces exactly this.

What the Garage is.

The name is intentional. The Garage is where things get built right; maintained properly, run reliably, tuned up occasionally, extended thoughtfully.

The core insight: every agent, regardless of who built it or how fast it came together, needs to meet a consistent baseline.

Usage logged to the central dashboard
Security parameters and data governance verified
Responsible AI framework compliant
Clear ownership with a maintenance plan
No unnecessary overlap with existing agents
Fit into the software development lifecycle

Before the Garage, those things happened by luck. After the Garage, they happen by design. They were all documented in the Garage Green Book.

The thing we were most careful not to break.

The creative momentum from Phase 5.

The worst version of an agent ops team is one that becomes a bottleneck. Submit your agent request here. Six-week review cycle. Fill out this form in triplicate. You missed section 17 of the BRD. How did you come up with that ROI. We need LT buyoff. Thank you for your patience.

That kills everything. Literally everything. Momentum, creativity, usage, passion; everything.

So we designed what we think of as a barbell.

One side is the delivery team: the ideation, the domain expertise, the speed, the ideas born from real work. To quote my mentor “In the business, not on the business”

The other side is the Garage: the rigor, the telemetry, the governance, the scale.

Both sides need each other.

Delivery without ops builds fast and breaks things. Ops without delivery builds carefully and solves the wrong problems. Together: fast and right.

What good agent ops actually enables.

It’s not just about catching problems.

Operational infrastructure is what makes scale possible. When the process for onboarding a new agent is defined, you can move faster. When agents share a common architecture, you can combine them. When the foundation is solid, extensions are fast. When your telemetry is accurate, you get buy in and more funding.

The Garage isn’t friction for innovation. It’s the launchpad.

One more thing the Garage gave us: a real answer to “what agents do we have, what do they do, and who owns them?”

That’s not glamorous. It’s essential. It’s an incredibly valuable asset. You need a library of agents, an agent to search that library, and an agent to simplify the process for getting more agents into the library. That’s agent ops. That’s The Garage.

Agent ops is a real thing. If you’re scaling agents without it, you’re not running a program. You’re running a mess.

Next: What the whole system looks like when it’s actually working.

Step 6: The Phase Nobody Plans For

📍 Part 6 of 8 · Becoming Agent-Native
An 8-part series on going from delivery team to agent-native organization — lessons earned, not borrowed.
Genesis · Anxiety · Names Matter · Proof of Value · The Pivot · → Co-Creation · The Garage · The Flywheel

When we started, the model was simple.

Squad builds. Delivery uses.

That division felt right. Clean organizational lines. Clear ownership. A good model.

By Phase 5, it was gone, and it was the best thing that could have happened.

Delivery became builders.

Not because we told them to. Not as a program or initiative.

Organically…as the natural result of everything that came before.

People moved through the anxiety, saw agents make their days better, made the mindset shift from threat to tool to teammate. They started showing up with more than feedback. They showed up with half-built ideas. With sketches of agents they needed. With “I figured out how to make this work”

And with a low-code toolkit they built them. Themselves.

Why this works better than centralized roadmaps.

The people doing the work every day know the friction better than anyone. They know which task is genuinely painful versus just annoying. They know which data lives in the wrong place. They know what “good output” actually looks like for their specific context. They aren’t looking at a PowerPoint slide or a Figma, they are living the experience.

When they build the agent, it fits because it was designed by someone in the workflow it’s automating.

Mona is a great example. She didn’t come from a squad roadmap, she came from a delivery team member who was tired of the back and forth with humans scheduling meetings. She understood the problem completely. She had opinions about exactly what the output should look like. She came to demo days and the engineers said “that’s a great idea but it won’t work” and then she showed them the MVP…working. That day changed our world.

That’s the model. You can’t push it from the top down. It has to grow

The propagation effect.

When everyone in delivery is creating, velocity compounds. One agent spawns an idea for three more. A tool that worked for one person gets adapted for the whole team. The surface area of “problems we’ve automated” expands faster than any squad sitting in an ivory tower could imagine.

There’s not some special team that’s the only one creating. Everyone is. That’s the whole point.

The catch.

There is one. And it’s significant enough to become its own post.

When everyone is building, you get overlap. You get orphaned agents when the person who built them goes on vacation and something breaks. You get agents that don’t log to the dashboard, don’t meet governance requirements, don’t fit the responsible AI framework.

We hit all of this. And more.

Seven agents doing roughly the same thing. Nobody quite sure who owned what. A model update quietly breaking something nobody was watching.

Citizen development at scale without operational infrastructure eventually leads to chaos.

Which is exactly what led us to build the Garage.

Creativity without ops is a mess waiting to happen. Ops without creativity is a very well-governed nothing. You need both.

Next: The mess — and what we built to fix it.

Step 5: The Day Anxiety Became Curiosity

📍 Part 5 of 8 · Becoming Agent-Native
An 8-part series on going from delivery team to agent-native organization — lessons earned, not borrowed.
Genesis · Anxiety · Names Matter · Proof of Value · → The Pivot · Co-Creation · The Garage · The Flywheel

There isn’t a single moment. It’s more like a temperature change.

Gradual. And then all at once. Exactly like Hemingway described bankruptcy.

The signal: someone stops asking “is this going to replace me?” and starts asking “what else could they do for me?”

That question – unsolicited, forward-looking, a little excited – is the pivot. And everything after it is different.

What caused it.

Not a single thing. An accumulation.

The email draft that was perfect. The research that came back before they’d finished their coffee. The weekly summary that was just there without anyone asking for it.

When those moments pile up, the mental model flips. The agent stops being a threat and starts being an asset.

And once it’s an asset, a very natural question follows: how do I get a better one?

That question is the whole game. Because it means your delivery team has become an active participant in the quality of their own AI teammates. They want them to improve. They have opinions about how. They’re invested.

The frame that accelerated it.

Our team always has more work than capacity. There are always more customers to serve, more research to run, more value we haven’t gotten to yet.

We are not, and have never been, trying to reduce headcount.

What we’re trying to do is amplify the headcount we have. Get more high-value work. Free people from the repetitive work that agents handle better anyway. Work on the hard stuff. Grow your career.

It’s like the tractor replacing the hand plow. You didn’t lose the farm. The farm got bigger.

When people understood that frame, agents as multipliers, the math became obvious. More impact, same team, better work.

That’s not a threat. That’s a competitive advantage for every person on the team.

What the pivot looked like in practice.

Feedback volume jumped. People who had never commented on an agent suddenly had opinions. Feature requests started flowing. Someone said “could Reese do this if we gave him this additional context?” and “I think George would be even better if he also pulled from this system.”

That’s not tool usage. That’s coaching. And you can’t coach something you’re afraid of.

When you see this shift starting, lean in fast. Turn that spark into a fire. Prioritize the feature requests that come from delivery. Make it visible that their input is landing in the roadmap. Create the fastest possible feedback loop.

The pivot is fragile at first. Feed it.

The moment your team starts coaching their agents instead of tolerating them, the phase change is real.

*Next: What happens when delivery stops requesting agents and starts building them.

Step 4: The Agent Dashboard

📍 Part 4 of 8 · Becoming Agent-Native

An 8-part series on going from delivery team to agent-native organization – lessons earned, not borrowed.
Genesis · Anxiety · Names Matter · → Proof of Value · The Pivot · Co-Creation · The Garage · The Flywheel

Early in Phase 2, before we knew if people would even use these agents, we built a usage dashboard and a small component that every agent had to include – Power Automate, Copilot Studio, M365 Copilot – it was table stakes to onboard.

It felt a bit like overhead at the time.

It became the foundation of everything.

What the dashboard tracked: which agents were being used, how often, by whom, and with what outcome.

Simple. But surprisingly revealing.

Some agents were hits. Usage climbed. Feedback was positive. The team became genuinely dependent on them. These got investment: more features, deeper integration, wider rollout.

Some were mediocre. Usage below expectation but not zero. The dashboard made us ask the right question: is the agent underperforming, or is there an onboarding gap? Is there a better design? Those are different problems. You can’t diagnose without the data.

Some just didn’t work out. And the dashboard gave us permission to retire them. No politics, no ego, just “the numbers say this isn’t earning its place.”

The data showed us insight about people, not just agents.

We saw a clear split emerge: pro users and skeptics.

Some team members were all in. They used agents daily, sending feedback all the time, acting like internal product managers for the agents they’d adopted. Others were lukewarm.

That visibility mattered. It let us find the right internal champions. It let us understand the gap between those two groups. It let us have a business conversation, with real numbers, about what was working.

ROI reporting doesn’t only justify the investment. It shows your team you’re taking this seriously. And them.

But the most important proof never showed up in the dashboard.

It was the moments.

The team member who realized they hadn’t manually changed that case status in weeks. Not because they forgot, but because Theo handled it.

The person who got their Friday afternoon back because George was doing the weekly summary.

The quiet relief of: oh, that’s just handled now.

When those moments accumulate, something shifts. The agent stops being an experiment and starts being infrastructure. The dashboard tracks the what. The moments explain why it matters.

If I were advising someone starting this today, I’d say: Build the measurement layer early, at the business group level, not deep in IT.

Once you have 10 agents and a skeptic asking “what’s the ROI on all this?” you’ll be very glad you have an answer.

“Measure early. The dashboard will make decisions for you that would otherwise become arguments.”

Next: The inflection point, when the team stopped worrying about agents and started wanting more of them.

Part 3: Words Matter, This Is Not a Branding Exercise

📍 Part 3 of 8 · Becoming Agent-Native

An 8-part series on going from delivery team to agent-native organization – lessons earned, not borrowed.
Genesis · Anxiety · → Names Matter · Proof of Value · The Pivot · Co-Creation · The Garage · The Flywheel

I wrote about this in August Agents: Names Matter and it was the right instinct. Now, on the other side of the full journey, I understand why it worked even better than I thought at the time.

Reese. Casey. Nabha. Elliot. Gibbs. Mona. George. Winston. Axel. Lily.

These aren’t product names. They’re teammates.

That distinction (which can sound like fluff the first time you hear it) turned out to be one of the most operationally significant decisions we made.

Here’s the simple version of why it works.

When something is a tool, your relationship to it is passive. You use it or you don’t. You don’t give a shovel feedback. You don’t coach a dashboard. Tools sit in a drawer until someone reaches for them.

With a teammate, your relationship is active.

You onboard them. You give them context. You tell them when they’re missing the mark. You notice when they improve. You advocate for them when someone else doesn’t understand what they do.

When your team has a stake in an agent’s success, they do something invaluable: they give feedback. Quality feedback. The kind that makes agents genuinely better.

We did regular performance reviews with our agents. More frequent right after onboarding or a new capability release (just like a new hire or after a promotion), less frequent as they found their footing. Same discipline as the rest of the team.

George is the clearest example.

Before George, Friday afternoon meant pulling together the week’s work, surfacing the key insights, formatting it, and getting it to the right people. Valuable. Tedious. The first thing that got cut when Friday got busy.

George does it automatically. He looks across everything you have produced, finds the biggest insights, and delivers them without anyone asking.

But here’s what made George good: feedback. Lots of it. Specific feedback from people who saw George as a teammate they were invested in making better.

That feedback loop exists in part because of the naming. Because of the personification. Because of the decision to onboard rather than launch.

There’s a governance benefit too, which we didn’t anticipate.

Naming forces clarity. When Mona graduated from a personal helper to a team-level role, we treated it like a promotion. Clarified scope. Assigned an owner. Eliminated overlap with other agents who’d been doing adjacent things.

Without that discipline, you get what I’ll describe in Part 6: seven agents doing roughly the same thing, disjoint responsibility, things slowly breaking.

Personas force you to think like an org designer, not just a builder. And be very clear, you are building an org.

The question your team will stop asking once agents have names:

“Do we need this?”

The question they’ll start asking:

“How do we help them thrive?”

That’s a completely different organization.

Giving your agents personas isn’t just adoption strategy. It’s how you build a team that includes humans and agents and actually functions like one.

Next: How we knew any of this was working — and what happened when we looked at the data.

Part 2: The Thing Nobody Warns You About

Part 2 of 8 · Becoming Agent-Native

An 8-part series on going from delivery team to agent-native organization — lessons earned, not borrowed.
Genesis · → Anxiety · Names Matter · Proof of Value · The Pivot · Co-Creation · The Garage · The Flywheel

Most AI transformation stories skip Phase 2.

They go from “we built some agents” straight to “adoption soared and everyone loved it.”

That’s not what happened for us.

When we introduced the first agents to the delivery team, the reaction wasn’t excitement. It wasn’t curiosity.

It was anxiety. Real anxiety. The kind that doesn’t announce itself clearly. It comes out as skepticism, low usage, polite questions with an edge underneath them.

The edge was: is this going to replace me?

Nobody said it that way. But it was in the room.

There was a second layer too. A small squad had gone off and built things, and now those things were showing up in workflows that had been their workflows. It kind of felt like change being done to them.

“I feel like I’m on the outside looking in at my own job being replaced.”

That’s not a technology problem. That’s a trust problem. Technology solutions don’t fix trust problems.

We made two structural choices. Both matter.

The first choice: we doubled down on agents being teammates, not tools; we gave them all personas and personalities.

This sounds like semantics. It isn’t.

A tool is something you use, or don’t. It sits there. It doesn’t get better. It doesn’t respond to coaching. It doesn’t care if it’s valuable or not.

A teammate is different. A teammate can be given feedback that actually changes how they work. You can advocate for them. You can push for them to be more capable. You have a stake in whether they succeed.

When people have a stake in something, they engage with it differently.

The second choice: every agent got a name.

Reese. Casey. Theo. Mona. George.

Not product names. Not “AI Assistant v2.3.” Real names, each one tied to the function, each one introduced the way you’d introduce a new hire; with context, with expectations, with a clear path to give feedback.

More on this in the next post, but the short version: named agents get coached. Unnamed tools stay static and get ignored. When was the last time your garden rake got an upgrade?

The anxiety didn’t disappear overnight. But it had somewhere to go.

The question shifted from “is this replacing me?” to “how do I best work with this?”

That’s the crack in the door. That’s what Phase 3 is about.

You can build the best agent in the world. If your team doesn’t trust it, you’ve built nothing of value.

Next: Why naming your agents isn’t branding; it’s adoption strategy.

Part 1: Becoming Agent Native.

📍 Post 1 of 8 · Becoming Agent-Native

An 8-part series on going from delivery team to agent-native organization – lessons earned, not borrowed.
→ Genesis · Anxiety · Names Matter · Proof of Value · The Pivot · Co-Creation · The Garage · The Flywheel

We Started in Our Worst Quarter. On Purpose. Q4. Our busiest quarter. The one where we’re all running flat out and nobody has margin for anything extra.

That’s when we decided to pull a small group off delivery and dedicate them to building AI agents.

People thought we were crazy. The timing was bad. But here’s the thing about timing: there’s never a good quarter to change how you work. If you wait for a slow moment, you’re waiting forever.

Three decisions made Phase 1 work. They’re worth naming clearly, because we got all three right (and had gotten them all wrong in an earlier attempt).

Decision 1: Delivery resources. Not new headcount.

The instinct is to hire specialists. Build a separate AI team. Find people with “AI” in their title.

We did the opposite.

We took people already doing the work – people who knew exactly where the friction was, who understood what “a bad Monday looks like” in our workflow – and we gave them dedicated time. Not 10% time. Not a side project. A real squad with a real mandate.

The people who know the pain are the ones motivated to build the cure.

Decision 2: Low-code or nothing.

We made it a mandate: no code-based solutions. No Foundry builds. No MCP servers. No deep engineering.

Partly practical; code means maintenance, and we didn’t have a team to own that. But mostly strategic. The platforms were moving faster than we ever could. Our edge wasn’t engineering. It was application. Low-code kept us in our lane.

Decision 3: Start embarrassingly small.

We had tried the big project approach before. A large-scale agent initiative run as a hobby by people with other jobs to do.

It failed. Not because the vision was wrong. Because nobody owned it, nobody had real time for it, and the scope was too big to make rapid, visible progress.

This time: small agents. Single tasks. The thing you do six times a day that shouldn’t require a human.

Not a meta-agent. Not a platform. Just: let’s automate that one thing that people hate doing.

Out of Phase 1, we had a handful of agents doing small, specific, daily automations. Unimpressive on a slide. Genuinely useful in a workday.

That was enough to move to Phase 2.

The biggest barrier to starting isn’t technology or budget. It’s the belief that you need a huge, perfect project to justify the investment. You don’t.

Next: What happened when those agents met the broader delivery team, and why it didn’t go the way we expected.

Remember the TCP/IP Stack Wars?

Same Frenzy, New Plumbing

In the early 1990s, “networking” on a PC was a jigsaw puzzle. You didn’t have TCP/IP. You assembled TCP/IP:

The right stack
The right network card
The right driver
The right OS version
The right configuration (that you’d only discover was wrong at 2am)

If you’re too young to remember this, imagine that the new thing for your work PC was to connect others send messages…but email only worked inside your company, and was not connected to the internet. You carried a briefcase full of papers home if you needed to work on something after hours. You probably didn’t have a mobile phone, and if you did it was mounted in your car – and only made voice calls.

I worked in customer support at a company that lived at the intersection of hardware, software, and networking. Our application ran across multiple protocols, so we didn’t just watch customers struggle—we helped them fight the puzzle: stack + driver + NIC + OS + application. It wasn’t just technical complexity. It was market immaturity.

This is the LLM market today.

Act I: Monetize the Mess

Inside the company I worked for, leadership was on a path to buy a TCP/IP stack to provide a consistent foundation for our applications.

The absurd part: application teams had to make functionality decisions based on disparate network stacks. Test teams had to test them all. Users had to understand them to get them to work. Then someone kicked the cable out of the adapter under their desk and the whole network went down.

Have you tried Hummingbird and Chameleon on both EtherLink and NE2000? What about when the network has both BNC and 10-BaseT connectors?

Networking vendors made tons of money in the confusion…and the switching costs…and the new versions. I believed the stack (and maybe the network cards) were heading toward commodity status. Essential, but not differentiating. I argued against buying or building a stack.

Act II: Standardize It (the Part Everyone Forgets)

TCP/IP didn’t win because one vendor’s stack was the best. Ethernet was technically deficient to Token Ring. But they both won because they became the standard—and standards create gravity. Once the interfaces stabilized, the application didn’t care whose stack you bought.

That’s the key idea: the application shouldn’t care. The user shouldn’t care. Maybe the IT department cares for a while, but eventually just procurement cares.

Act III: Forget It’s There

Once TCP/IP became “default” and the interfaces stabilized, the market stopped paying premiums for stacks.

Networking wasn’t eliminated. Thinking about networking was eliminated. Who knows which network adapter is in their new laptop today? Can you imagine buying a laptop without connectivity? It’s unthinkable.

History continues to prove the pattern:

Monetize → Standardize → Forget

The LLM World Is in Its “Stack Wars” Era

Today’s LLM discourse sounds like the early 90s networking discourse—just with better fonts and worse certainty:

Which model for which task?
Which provider is “best”?
Should we build our own?
How do we avoid lock-in?

It’s the same jigsaw puzzle, modernized:

model + prompt style + tooling + memory + safety + cost + latency

And it produces the same executive temptation:

“Let’s build the stack so we control our destiny. Everyone is doing it; we don’t want to be left behind!”

The value today, in the complexity phase, is the model. The value in the future is the thing that uses those models.

Segmentation: Specialized Providers vs Specialized “Application-Layer Engines”

Yes—there are real segments emerging: coding, personal info management, health, and more.

But the more important question is where specialization will live:

Path A: Specialized model providers dominate each segment

“Best model for coding.” “Best model for health.” “Best model for XYZ.”

Path B: Commodity base models + specialized implementations on top

Fine-tunes, adapters, retrieval, tool-use, memory, evals—packaged as product capabilities. In applications.

Path B is the historical match.

The winning move is applications standardize how they connect to intelligence, and the model selection becomes invisible plumbing.

That’s the interoperability point—and it’s where network effects quietly return.

Network Effects: TCP/IP Interoperated with Networks. LLMs Interoperate with Tools.

TCP/IP’s network effect was obvious: the value came from being able to talk to other networks.

LLMs don’t inherently need to “talk” to other LLMs. They compete on capability.

So where’s the network effect?

It moves up one layer.

That’s why protocols like Model Context Protocol (MCP) matter: they standardize how AI systems connect so developers don’t rebuild bespoke, model specific integrations.

Once connectivity is ubiquitous, the LLMs start to disappear.

Phase 1: Value = Plumbing
         (TCP/IP stacks | LLM providers)

Phase 2: Value = Interfaces
         (Winsock | MCP)

Phase 3: Value = Outcomes
         (Connectivity | Apps & Agents)

The Pattern Is Undefeated

Every platform shift starts the same way:

We monetize the complexity.
Then we standardize the plumbing.
Then we forget it was ever hard.

TCP/IP stacks were once a market category. Now they’re invisible.

LLMs are in their stack-wars era.

The winners won’t be the companies with the prettiest model demo…or even the best model. They’ll be the ones who make magical apps and let you forget the model exists.

Perspective We Could Use; Missing My Dad

My dad passed away 14 years ago this week. About a decade before that, he suffered a stroke that slowed him physically but didn’t stop the way he drew insights (although it did remove some of his filters which produced some surprisingly humorous outcomes). His spark was still there. He had a way of pulling together threads no one else thought to connect, and even after the stroke, he could cut through complexity with a clarity that made you stop and rethink your positions.

He was one of the creators of the original concept of “crossing the chasm” — the framework Geoffrey Moore later popularized in his book (original product here and here – I continue to appreciate Warren Schirtzinger’s passion around maintaining that history). The idea that technology adoption moves differently, that those products that have the greatest early success tend to stall out before going mainstream, was theirs. That kind of insight wasn’t born from reading one market report or trend piece. It came from years of seeing patterns where others saw noise, of spotting trajectories when everyone else was focused on the present moment.

Image from 8/23/89 (36 years ago this week!)

That was my dad’s gift: perspective based on deep experience and wisdom from a history of seeing trends in the static where most just saw what was right in front of them. He could look across disciplines — business, politics, technology, science, human behavior — and connect dots that seemed unrelated until he put them together. It wasn’t just foresight; It was synthesis. He was the ultimate example of David’s Epstein’s Range which may explain why it is one of my favorite books.

Today, in the middle of the AI revolution, I can’t help but think how much the world could use his mind. We’re in a moment where the pace of change is faster than our ability to interpret it. Every week brings new breakthroughs, new promises, new anxieties. There’s an event horizon that most of us can’t see over that has shrunk from decades to years to quarters and now to months as the pace of change accelerates. Models can summarize, remix, and predict, but they can’t provide meaning. They can’t tell us what matters, what’s durable, what’s just noise.

That requires humans who have the scar tissue of experience, who have lived through cycles of hype and disillusionment, who can see not just the product demo in front of them but the trajectory it suggests. My dad was one of those people. And if he were here today, I know he would have a great perspective — and would provide it to me and anyone who would listen — to make sense of what AI means beyond the headlines.

So yes, I miss him because he was my dad. I miss his humor, his encouragement, his presence. But I also miss him for what the world could use right now: a steady voice of perspective in the middle of chaos, a connector of dots when the picture feels fragmented, someone who could see beyond the event horizon and help us chart a course.

In an age where AI is amplifying everything — both the noise and the signal — the ability to find clarity has never been more valuable. That was his superpower; I really miss him.

*“The process of change that drives market leadership”* – Lee James (March, 1989)

Agents: Names Matter

This post is part of an ongoing series about what we’ve learned from augmenting our team with agents. This series shares hard-won lessons from integrating agents into our team. It’s not theory—it’s transformation, in motion.

AI agents struggle to succeed when you treat them like tools. We know this because we’ve been down this path, and 82% of enterprise led agentic projects are shelved after 12 months. “FastTrack Tool #17” won’t spark enthusiasm and drive usage. But Reese and Casey? They changed the conversation.

Here’s what we’ve learned by doing the work:

1. Personas Build Teammates

When we first started talking about agents, the most common reaction wasn’t excitement—there was an undercurrent of fear and anxiety. People worried they’d be replaced.

But after we introduced Casey as a teammate, things shifted. The conversation became: “How can we help Casey succeed and do more for us?” That reframing worked because Casey felt like a person, a part of the team—not a bot.

2. Onboarding, Not Launching

We learned quickly that you don’t launch a teammate—you onboard them.

For us, that meant treating agents with the same discipline as new hires: communication plans, awareness sessions, training, and buddy systems. Adoption improves the moment you stop thinking “tool release” and start thinking “new colleague.”

We do regular reviews with our agents’ performance, just like we do with the rest of the team – more frequent right after onboarding (or iterations of new capabilities, just like promotions). Less frequent as our agents get more experience.

3. Scaling Agents Comes with Responsibility

At first, agents were treated casually—something that was just a test, that could be turned on or off at will. That didn’t work.

Now, our agents are roles on the org chart. Adding or retiring an agent requires a process, because their work has real dependencies. One person’s frustration shouldn’t lead to Winston being deleted on a Friday afternoon any more than it should lead to a human being walked out the door. We need to put the same thought, coaching, iteration, and decision process into all of our teammates; human or not.

4. Personas Force Clarity

Our early experiments taught us that tool development drifts—overlap, redundancy, and confusion creep in. But personifying agents forces sharper thinking.

When Mona “graduated” from a personal helper to a team-level role, we treated it like a promotion. We clarified scope, aligned expectations, set up an owner (manager), and eliminated overlap. Without that discipline, grassroots innovation can quickly tip into chaos (more on this in a future post).

The Big Lesson

This isn’t theory. This is earned wisdom that we’ve learned by doing.

Giving your agents personas isn’t just branding—it’s adoption strategy, trust-building, team culture, and organizational clarity.

Because when an agent stops being Tool #17 and starts being Theo, your team doesn’t ask “Do we need this?” anymore. They ask “How do we help them thrive?”