The Momentum Thesis: Why We Build AI Employees

As a founder, there are a few consistent truths: good help is hard to find, there are never enough hours in the day, and there are few things more valuable than momentum.

We built MMNTM to solve a problem we lived through: the work that must exist so your business can exist. Not the work that makes your product better or your customers happier—the work that simply has to happen. HR. Admin. Recruiting. Compliance. The operational substrate that nobody starts a company to do, but everyone has to do anyway.

This is how we think about it.

The Founder's Dilemma

Early-stage companies face a structural problem that's rarely named: humans are integer-denominated resources.

You either have zero HR people or one. There's no 0.3 FTE. At a startup, this typically means one of three outcomes:

1. The founder wears it — You context-switch between building product and handling employment paperwork
2. A generalist grabs it — Someone on the team takes it on because "someone has to," but it's not their job and it shows
3. A specialist goes unused — You hire a full HR person who's either bored or overwhelmed depending on the week

This isn't unique to HR. Finance, legal, IT, recruiting—every operational function has the same dynamic. The work exists. It must be done. It doesn't justify a full head.

The traditional answer is fractional resources: consultants, agencies, part-time hires. But fractional resources are expensive per hour, context-poor, and you lose institutional knowledge every time they rotate.

The founder's dilemma: you need help, but you don't need a full person.

Why Copilots Aren't Enough

The first wave of generative AI promised to solve this with copilots. Give everyone an AI assistant. Make existing employees more productive.

That thesis has a flaw: productivity gains tend to disappear into the ether.

This has been the story of the internet for twenty years. Every new tool promises productivity, and yet work expands to fill the time available. Meetings multiply. Email proliferates. The workweek stays the same.

Part of this is structural—Parkinson's Law. But part is incentive misalignment. Nobody wants their job to get smaller. If you give someone a copilot that doubles their output, they don't say "give me twice as much work." They enjoy the breathing room.

More fundamentally: copilots still require you to drive. You're still the bottleneck. The AI helps you go faster, but every task still demands your attention, your decision, your approval. You're moving data more efficiently, but you're still moving it.

For the operational work founders hate—the work that must exist but isn't why they started the company—faster isn't the goal. Gone is the goal.

The MMNTM Model

We don't build copilots. We build employees.

What "agentic" actually means: Not an assistant that helps you do work. An agent that does work and asks for guidance when needed.

The distinction matters. A copilot is a tool. An employee is an agent with defined scope, clear responsibilities, and autonomy to execute within that scope.

When you hire a human, you don't micromanage every keystroke. You define their role, give context, check in periodically, and let them figure out details. They ask questions when ambiguous. They escalate when risky. They run autonomously on the 90% that's routine.

That's what we're building.

Our agents live in your Slack, join your meetings, read your documents, and build context the way any new hire would. They handle operational work—benefits questions, compliance checks, scheduling—with human oversight at decision points. See Solutions to explore available agent profiles.

The difference is elasticity. A human employee is binary: you have one or you don't. An agent scales from 0.2 FTE to 5 FTE on demand. Lever up for a product launch, lever down during slow periods. No hiring, no firing, no onboarding, no severance.

How We Build Them

Every MMNTM agent runs through a three-step cycle that operates continuously:

1. Context

Our agents learn your business the way new hires do.

Internal context: They read your Slack history, documents, meeting transcripts. They study your closed deals and lost ones. They learn brand voice from marketing and customer sentiment from support tickets.

External context: They study your competitors. They track regulatory changes. They monitor market signals, customer demographics, technology shifts.

Institutional knowledge: The undocumented rules. "How we do things here." The preferences and patterns that live in people's heads but never made it to a wiki.

Context accumulation is continuous. Every interaction adds to the knowledge base. Unlike humans, agents don't forget—context only grows.

2. Tools

We integrate best-in-class partners continuously.

SaaS layer: Rippling, Vanta, Linear, Slack, HubSpot, Salesforce—your systems of record.

AI layer: ElevenLabs for voice, Gemini Deep Research for investigation, Perplexity for real-time information, hundreds of specialized models.

Your tools stay the same. The agent uses them the way a human does—gathering context from one system to make decisions in another. It's not moving data from Column A to Column B. It's thinking across your stack.

3. Evolution

This is where most AI products fail: they ship a prompt, run it in production, and hope. When performance degrades, they debug manually.

We follow scaling principles.

We work with world-class performers in each functional role—senior HR leaders, experienced recruiters, top-tier admins—to develop rigorous evals. What does excellent look like? How do you distinguish good judgment from bad? What edge cases separate experts from amateurs?

Then we run generations of agent variants through these evals. We measure performance against human baselines. We introduce mutations—prompt variations, tool configurations, context strategies—and keep what works. We kill non-performers and replicate winners.

This isn't one-time optimization. It runs continuously. As your business evolves, agents evolve with you. Visit our Evolutionary Workbench to see this process in action, or read State of Evals for technical depth.

The Compounding Effect

Here's the insight that changes everything:

The second agent shows up knowing everything the first one learned.

When you hire a human, they start from zero. They learn your industry, company, processes, preferences. Every new hire goes through the same onboarding curve.

Our agents share context.

Your first agent—say, an HR agent—learns your company. Benefits structure. Hiring policies. Management style. Internal politics.

Your second agent—say, a recruiting agent—already knows all that. It doesn't start from zero. It inherits institutional knowledge the HR agent accumulated. It only needs to learn its own function specifics.

The third agent knows more still. The fourth more.

This creates compounding advantage. The more agents you deploy, the faster each becomes productive. Your AI workforce gets smarter together in ways human teams can only approximate.

It also enables something new: agents introducing other agents.

Imagine your HR agent notices the team struggling with go-to-market. It says: "I noticed we're having trouble with outbound. My company has a sales development agent that's really effective. Want me to invite them?"

The new agent shows up already knowing how your company works. Not a cold start—a warm introduction with full context. Explore how this ecosystem grows at /ecosystem.

The Human Layer

Full autonomy sounds good in pitch decks. In practice, it's a liability.

We believe in assisted autonomy: agents that work independently on routine tasks but escalate appropriately on judgment calls.

Early in deployment, agents run in high-approval mode. Every significant action gets reviewed. You see the reasoning, the proposed action, you approve or redirect.

As trust builds, constraints relax. The agent handles more autonomously. But guardrails remain—you define risk tolerance, set escalation triggers, maintain control.

There's another human layer most AI companies don't discuss: expert oversight.

We work with world-class performers not just to build evals, but to refine strategies and handle edge cases. A senior HR professional might oversee agents across ten companies. They're not doing routine work—agents do that. They're handling complex situations, judgment calls, moments where experience matters.

You get expert-level guidance without paying expert salaries. Agents handle the 90% of routine tasks. Experts handle the 10% requiring real judgment. See HITL Firewall for how we structure human oversight.

What This Means

For twenty years, software sold productivity: "Our tool makes your employees 20% more efficient." The TAM was always a fraction of the labor budget—whatever percentage of someone's time your tool could save.

We're not selling productivity. We're selling labor.

Agents are priced like headcount, not software. Not $20/seat/month. Not per-token. Units of work output, comparable to what you'd pay a human doing the same job.

This changes what you can afford. Operational work you've neglected because it didn't justify a full hire? You can afford a 0.3 FTE agent. A project requiring temporarily doubling ops? Spin up capacity for two months and spin it back down.

Humans are integers. Agents are continuous.

For more on the economic case, see Agent Economics.

The Work That Lets You Work

We sell the two most valuable resources for founders: time and momentum.

Not time in the abstract—time spent on what matters. The work that makes beer taste better. The core product. Key relationships. Strategic decisions. The things only you can do.

The operational substrate—HR, admin, compliance, documentation—is the work that lets you do that work. It must exist. It must be done well. But it doesn't have to be done by you.

That's what we're building. Feature-complete AI employees that handle the work that must exist so your business can exist. They arrive onboarded. They share context. They improve continuously. And they give you back the thing you started your company to spend: your attention on the things that matter.

We protect your time. We preserve your momentum.

That's the thesis.

Ready to deploy? Start at /deploy.

What's Next

If you're interested in how vertical AI is winning over horizontal approaches, read Vertical Agents Are Eating Horizontal Agents. For deeper analysis of AI labor economics, see Agent Economics.