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?”

Despecialization is the new Superpower

Why AI Agents Reward Infielders, Not Aces

I was listening to Charles Lamanna on Soma’s Founded & Funded podcast on Monday , and his point about despecialization really hit home.

It’s rare that a single comment ties together your career, your instincts, and your favorite bookshelf reads. But this one did. Charles argued that AI is shifting value away from hyper-specialized skills and toward flexible, cross-disciplinary thinking. Generalists—what I’d call “infielders”—are suddenly in demand.

And I couldn’t nod hard enough.

A Bias, Confirmed

To be fair, this plays into every bias I have. I’m a self-identified “just enough depth to be dangerous” operator. My value hasn’t come from mastering a narrow domain, but from stitching together patterns across finance, tech, operations, change management, sales, and many others. Reading Range by David Epstein felt like someone had written my career into a defense memo. Lamanna just gave it a business case.

Why now?

Because AI agents are replacing the white-collar specialists.

Not the CMO, but the copywriter.
Not the VP of Data, but the analyst building dashboards.
Not the strategy consultant, but the associate churning slides.

Tasks that once demanded focused expertise can now be delegated to AI agents—faster, cheaper, and increasingly better. When that happens, the edge shifts to the person who can connect those outputs, not just create them. Can think of how to use them in new and different ways. Generalists who know enough to be dangerous across multiple domains are the new integrators.

Agents Accelerate the Shift

We’re not seeing a skills apocalypse. We’re seeing a redistribution. The person who was great at “one thing” now shares that lane with an agent. But the person who can see across lanes, adjust the play, and call the audible? That person is now running the whole offense.

This reframes the AI conversation. The goal isn’t to master every model or become an AI prompt savant. It’s to build systems of agents—and then figure out how to manage those systems with judgment, context, and creativity.

That’s not about coding. That’s about ownership.

A Nod to Jocko

It’s no coincidence that both Charles and Soma reference “extreme ownership” in their conversation. The phrase, coined by Jocko Willink, isn’t just leadership advice. It’s a survival skill in this AI-enabled world.

When the tools can do the work, the work becomes about accountability. You don’t get to say “that wasn’t my job”—because now your job is making the whole thing run. That’s leadership at every level.

Extreme Ownership was the book that made me a Jocko fan. This podcast just brought that mindset to AI.

The Generalist Advantage

So, where does that leave us?

Generalists are getting the opportunity to scale.
Leaders are taking ownership of complex, agent-powered systems.
Organizations are starting to value adaptability over mastery.
Specialists are looking up and out —or being replaced.

And people like me? We’re finally not apologizing for being “a little bit of everything.” We’re leaning in.

Because in the world of AI , it turns out the infielders are running the game.

#AIagents #FutureOfWork #GeneralistVsSpecialist #Despecialization #ArtificialIntelligence #WorkplaceTransformation #CareerStrategy #Productivity #LeadershipMindset #RobertHeinlein

The Compound Interest of Productivity

AI Agents Are the New Leverage

Productivity tools have long promised to make work easier.
Most deliver accumulation—you stack features, shortcuts, and automations, each adding a marginal gain.

Helpful? Sure.
Transformational? Not really.

But what if, instead of stacking, we could compound?

That’s what AI agents offer

From Tasks to Systems

Start with the small stuff:

An agent that filters email.
Another that summarizes meetings.
A third that drafts follow-ups.

Alone? Nice-to-haves.
Together? They form a system.

The output of one becomes the input of the next. A peloton, not a solo rider. And once that loop forms, you’ve crossed a threshold—from isolated tasks to an adaptive system that gets smarter with each pass. This isn’t just automation—it’s orchestration.

Agents don’t just execute. They learn. They adapt. They cooperate.

Each one adds leverage. Each one amplifies the others.

You’re not saving time. you’re building momentum.

The more agents you connect, the more capable the system becomes.
You go from incremental gains to exponential lift.

Accumulation vs Compounding

We started with a handful of lightweight agents. One evaluated incoming requests and scored them. One updated case notes and status. One checked case hygiene. Another populated task lists and project plans.

Individually? Fine. Connected? Something else entirely.

Work got faster. Work scaled to more customers. Work got smarter.

The system started making decisions:

with better quality
with greater outcomes
across more customers

We’re crossing a threshold in how we work. Productivity isn’t about brute force anymore—
It’s about systems that interoperate and learn.

Agents are the first technology that mirrors how nature builds: organically, iteratively, through networks that adapt and strengthen over time. Each agent you create doesn’t just add function—it adds force.

Future-proof your productivity, start building small agents today.
Connect them.
Let them learn.
Let them compound.

The AI Revolution is here – and it is the savior

I saw some of the fallout of the interview with Dario Amodie yesterday and one of his key attention grabbers was:

“unemployment will spike to 20% in the near future”

On my team, we’re driving hard and fast into onboarding agents (more on that soon), and in doing so, we’re building earned wisdom, not just hypothetical or philosophical views.

His statement made me pause—not because it’s dramatic, but because it’s directionally right and emotionally wrong. There’s a better thought exercise to pursue:

AI won’t just disrupt jobs – it will accelerate the creation of their replacement.

The uncertainty around AI is due to the rate of change—and how fast that rate is itself accelerating.

If you consider past paradigm shifts, they all disrupted the existing workforce massively, but slowly:

The Mainframe
The PC
The Internet
Mobile
The wheel, fire, electricity…

These changes all transformed industries. They put people out of work—but not forever. No one today is training to be a switchboard operator. People adapted.

The fear with AI, especially Agentic AI, is that those changes are happening in days or weeks instead of years or decades. But this isn’t like past tech waves where new roles emerged slowly.

The technology that is disrupting everything is part of everything.

This means that AI will help design, build, and onboard the future of work in real time. It will empower people to adapt faster, create faster, and solve problems from every angle—not just the top down.

This is the key difference that gives me great hope from working in real time with AI ; the disruptor is the savior all in one, and it brings the power to help those that are disrupted.

AI is driving disruption centrally within organizations, but we are adapting in a decentralized way – AI is enabling those that are getting on board to create systems, training, opportunities, and resiliency for the new future.