The Economics of Agency

Imagine you hire a plumber to fix a leaky sink. The job takes one hour.

Now, imagine that the plumber demands to be paid for the other 23 hours of the day. They want to stand in your kitchen, wrench in hand, staring at the sink, just in case it leaks again.

You would fire that plumber immediately. It’s a ridiculous economic model. You pay for labor when labor happens.

Yet, this is exactly how we have traditionally deployed software. We spin up a server (a "container") on AWS or DigitalOcean. We pay for it to run 24 hours a day, 7 days a week. We pay for CPU cycles that are doing nothing but waiting for a request.

For a high-traffic website like Google or Facebook, this makes sense. The requests are constant; the plumber is always working.

But for AI Agents, this model is broken.

The "Bursty" Nature of Agents

AI Agents do not work like websites. They work like humans. They are bursty.

• 9:00 AM: Your personal assistant agent wakes up. It checks your email, summarizes your Slack messages, and updates your calendar. This takes 45 seconds.
• 9:01 AM - 1:59 PM: The agent does nothing. It sleeps.
• 2:00 PM: You ask the agent to research a competitor. It browses the web, scrapes 5 sites, and writes a report. This takes 2 minutes.
• 2:02 PM - 8:59 AM: The agent sleeps.

If you deploy this agent on a traditional server (even a cheap $5/month droplet), you are paying for 23 hours, 57 minutes, and 15 seconds of idle time every single day. You are paying for waste.

The Serverless Revolution (Revisited)

This is why Serverless (or "Function-as-a-Service") is the native architecture of AI.

In the Serverless model—pioneered by AWS Lambda and perfected for AI by Dwizi—infrastructure is ephemeral. It does not exist until it is needed.

1. The Trigger: Your agent decides to call the get_stock_price tool.
2. The Spark: Dwizi spins up a micro-Virtual Machine in milliseconds.
3. The Work: The code runs. It fetches the price. It returns the JSON.
4. The Vanishing: The VM disappears. It is wiped from existence.

You pay for the 200 milliseconds of execution. You pay $0 for the idle time.

The "Cold Start" Problem

If Serverless is so great, why isn't everyone using it? Because of the Cold Start.

In traditional serverless (like Lambda), it can take 1-5 seconds to spin up a new function. For a website, a 5-second delay is annoying. For an AI agent—which might need to call 10 tools in a row—a 5-second delay per tool is catastrophic. It breaks the flow of conversation. It makes the agent feel stupid and slow.

This is why Dwizi is obsessed with runtime performance.

We don't use heavy Docker containers. We use V8 isolates and lightweight virtualization. Our "Cold Starts" are measured in milliseconds, not seconds. To the AI, the tool feels instant. It feels like reaching for a hammer that is already in your hand, not ordering one from Amazon.

The Cost of Autonomy

Why does this economic model matter? Because of scale.

We are moving toward a future where everyone will have not just one agent, but dozens. You will have a "Travel Agent," a "Coding Agent," a "Shopping Agent," a "Scheduling Agent."

If every single one of those agents required a dedicated server (even a small one), the cloud bills would be astronomical. The economics of AI would collapse.

By embracing ephemerality, we drive the marginal cost of "doing work" down to near zero.

• Running a tool once costs a fraction of a penny.
• Running a tool zero times costs zero.

This efficiency is what makes autonomy affordable. It is what allows us to unleash an army of digital interns to do our bidding, without bankrupting us.

The future of AI is not just about smarter models. It is about cheaper execution. And the cheapest execution is the one that only exists when you need it.