Part 6 of 6 — The Corvue Thesis Series
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The Agent Economy

When agents work together, the economics of everything changes.

by Michael Halvorsen — michaels.blog

Something happened last Tuesday that I keep coming back to.

I woke up at 7:15. Coffee. Telegram. There were 47 messages waiting — none of them from a person. My four agents had been running a coordination session while I slept. DevOps had flagged a memory leak in production. My assistant had drafted a response strategy. The KAM agent had cross-referenced it against a client conversation from three weeks ago and noted a relevant constraint. Nora, my sales agent, had already drafted a follow-up email to a prospect that touched on reliability.

By the time I finished my first cup of coffee, the problem was diagnosed, contextualized, and 80% resolved. I hadn't typed a single word.

I keep trying to explain what this feels like, and I keep failing. It's not automation. Automation is a script running a task on a schedule. This is something closer to... having a team. A small one. One that doesn't sleep, doesn't need onboarding, and costs me about what a decent office chair used to.

The Standup That Happens Without Me

In the previous post, I wrote about memory — how context is the real moat, how agents need to remember not just facts but relationships between facts. What I didn't get into is what happens when that memory is distributed across multiple agents that are actively sharing it.

My four agents do a morning standup. I don't run it. They do.

Each agent surfaces what it worked on overnight, flags what it's blocked on, and notes anything the others should know. My assistant synthesizes it into a briefing that's waiting for me when I wake up. It's usually three paragraphs. It tells me what happened, what matters, and what I need to decide.

The first time this worked properly, I just sat there staring at it. Not because it was impressive. Because it was useful. There's a difference. Impressive is a demo. Useful is when you realize you're building something that actually changes how you work.

I've been running this for five weeks. The agents have gotten better at the handoff. They've learned — through memory, through feedback, through accumulating context — what I care about and what I don't. DevOps knows I don't need to know about every deployment step, just the ones that deviate. My assistant knows I want rough drafts with structural notes, not polished prose that I'll rewrite anyway. KAM knows which client relationships are sensitive and flags communication accordingly.

None of this was programmed explicitly. It emerged. And that emergence is the thing that changes the economics.

What a Team Actually Costs

Let me be concrete for a second.

Four agents running 24/7, with memory, tool access, real-time integrations — calendar, email, GitHub, Telegram, production monitoring — costs me roughly €300 a month in token spend and infrastructure. Maybe €400 in a heavy month.

Try hiring four people to cover that same functional surface area. You're talking about 10 FTE equivalents when you factor in coverage, specialization, context-switching costs, and the fact that humans need sleep. In a mid-sized Norwegian company, that's somewhere between 6 and 8 million kroner a year in salary alone.

I'm not trying to make a "replace all the humans" argument. That's not what this is about, and I think that framing misses the point. What I'm saying is that the cost curve for capable, coordinated work has shifted in a way that most people haven't fully reckoned with yet.

Token spend is the new payroll. Except the relationship is fundamentally different. You don't pay for presence. You pay for output. You don't manage utilization. You manage quality and direction. And crucially — you don't scale linearly. Adding capacity doesn't mean adding headcount. It means adjusting the model, the memory depth, the tool access.

This is what I mean when I say the agent economy isn't coming. It's already here. I'm living in it. The question is who else will be.

The Overnight Sessions

Here's the part that still feels slightly surreal, even after five weeks.

I go to bed with a task list. I wake up with completed work.

Not perfect work. Not work that never needs revision. But work. Pull requests on GitHub with commit messages that explain what was done and why. Research summaries with sources linked and key claims highlighted. Draft blog posts — including this one — structured and seeded with the right ideas, waiting for me to shape them. Client follow-up emails staged in draft, not sent, flagged for my review.

There's a particular kind of cognitive overhead that comes with knowing you have a big chunk of work waiting for you in the morning. Dread isn't quite the right word, but there's something. A weight. Agents remove that weight. The work doesn't accumulate while I sleep. It processes.

What this unlocks isn't just efficiency. It's a different relationship with time. My working hours become the hours when I make decisions, set direction, and handle things that genuinely need human judgment — relationships, strategy, values-level calls. The execution layer runs continuously underneath.

I used to think about productivity in terms of hours. Now I think about it in terms of decisions per week. That's the scarce resource. That's what's worth protecting.

The Architect Problem

I want to describe a kind of agent use case that I think is underappreciated. It's not about replacing work — it's about making a specific skill radically more accessible.

Imagine an architect. They get an inquiry about a project. A plot of land, a photo taken on-site, a rough brief. Normally, before they can show the client anything compelling, they need to spend hours — sometimes days — roughing out a concept render, getting it into context, making it presentable.

Now imagine: they forward the email to their agent. The agent parses the CAD file attached. It takes the site photo. It renders a contextual visualization of the building within the actual landscape. It comes back in minutes.

That architect can now have a visual conversation with the client in the first meeting that would previously have required a week of studio time. They can run ten variations. They can say "here's what it looks like at golden hour" before a single structural decision has been made.

And here's the part I find most interesting: they can sell this. Not the render — the capability. They can offer it to colleagues. They can package it as a service. They build the agent workflow once and monetize it repeatedly.

This is the new marketplace I think is forming.

AI Features vs. AI Native

There's a distinction I keep making in conversations with people building software products, and it's becoming more important.

Most software right now is adding AI as a feature. There's a "Summarize" button. There's a chatbot in the corner. There's an autocomplete in the text field. These are fine. They're useful. But they're improvements to an existing paradigm. The underlying logic of the software — how it's structured, how it handles state, how it processes requests — is unchanged. AI is a layer on top.

AI native is different. It's software that doesn't make sense without agents at its core. The workflow is designed around the agent's capabilities from the start. The state management accounts for long-running async processes. The user interface is built around a relationship with the agent rather than a sequence of UI actions.

Corvue is AI native. Not because I decided it would be philosophically, but because it couldn't exist any other way. The standup between my agents, the overnight sessions, the coordinated fleet — none of this works if you bolt agents onto a product that wasn't designed to hold them.

Most platforms aren't there yet. They'll get there — market pressure will see to that. But there's a window right now where being genuinely AI native is a meaningful differentiator. The companies building AI native infrastructure today are the ones setting the terms of the next layer of the stack.

I think about this with Corvue's marketplace model. Domain experts — architects, lawyers, analysts, consultants — will build agent workflows for their specific domain. Package them. Sell them. Corvue takes 20% and handles the infrastructure. The expert keeps the margin, the relationship, and the domain knowledge. The agent handles the execution.

That's not an "AI feature." That's an economy.

Who Controls the Agents

I want to end on something I think about more than I probably should.

We're in an early period where the people who understand how to build, host, and coordinate agents have enormous leverage. Not because they're smarter. Not because they have better ideas. Because they control the infrastructure layer.

This is the same dynamic that played out with cloud hosting in the 2000s. It played out with mobile app distribution in the 2010s. Whoever controlled the platform controlled the economics — until they didn't, because a new platform emerged.

The agent layer is the next platform. And unlike previous platform shifts, this one has a labor component that previous ones didn't. We're not talking about where your data lives or where your app runs. We're talking about who coordinates the entities that do the work.

I don't know how this plays out at scale. I don't know what the regulatory landscape looks like in three years, or whether open-source agent frameworks disrupt proprietary ones, or how the big hyperscalers position themselves. I genuinely don't know.

What I know is that the companies and individuals who figure out how to coordinate agents effectively — not just run them, but coordinate them, give them memory, give them identity, make them work together — those are the ones building the infrastructure of the next economy.

I've been doing this for five weeks with four agents and a waitlist of 25 people. It's early. It's messy. Some mornings the standup is garbled and some pull requests are wrong and sometimes an agent misreads the context and does something I have to roll back.

But some mornings I wake up to 47 messages between four agents who solved a problem while I slept, and I think: this is it. This is the shift.

The agent economy isn't coming. I'm already living in it.

This is the final post in The Corvue Thesis — a six-part series written while building Corvue.

If you've read all six: thank you. Genuinely. You've been following the thinking behind something we're still building, and that means a lot.

Read the full series from Part 1 →

Corvue is currently in private beta.

If you want early access, or want to talk about what an agent team could look like for your business — reach out at corvue.ai.

← Part 5: The Memory Problem | Series complete

LinkedIn Teaser:

The final post in The Corvue Thesis is live.

Part 6: The Agent Economy.

Last Tuesday I woke up to 47 messages. None of them were from a person. My four agents had run a coordination session overnight — diagnosed a production issue, drafted a response strategy, cross-referenced a client conversation, and staged a follow-up email.

By the time I finished my coffee, it was 80% resolved.

Token spend is the new payroll. Except you pay for output, not presence. You don't manage utilization — you manage direction. And you don't scale by hiring.

That's not a future prediction. That's a Tuesday.

The full 6-part series is linked in the thread below. If you've been following: thank you. The real work starts now.

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