Experimenting with Autonomy: Our Learnings from Building a Self-Improving AI Agent

The promise of business automation has always been to free up human potential. Yet, for years, the reality has been a complex web of brittle scripts, rigid workflows, and tools that require more maintenance than the tasks they're supposed to automate. They follow instructions, but they don't understand intent. They can execute a predefined path, but they break the moment something unexpected happens.

We knew there had to be a better way. This conviction led us to create Ivy, an autonomous AI agent on the .do platform. Our goal wasn't just to build another automation tool, but to build a true digital worker—an agent capable of understanding complex goals, planning its own actions, and improving over time.

Building Ivy has been a journey of experimentation and discovery. Here are our four biggest learnings from creating a truly autonomous, self-improving AI agent.

Learning #1: True Autonomy is More Than a Chain of Prompts

Early AI automation often involved "prompt chaining"—linking several LLM calls together in a fixed sequence. If Step 1 succeeds, do Step 2, then Step 3. This is automation, but it isn't autonomy.

A truly autonomous AI agent operates differently. It doesn't just follow a script; it creates the plan. When you delegate a task to Ivy, you're not defining the how; you're defining the what. Ivy then builds and executes an agentic workflow:

1. Understand: It parses the goal, even if expressed in natural language.
2. Plan: It breaks the goal down into a series of executable steps. (e.g., "To qualify this lead, I must first find their company website, then locate their employee count on a data provider, then search for recent news about funding rounds.")
3. Execute: It uses a suite of tools (browsers, APIs, databases) to perform each step.
4. Self-Correct: If a step fails or a path is a dead end (e.g., the website is down), it re-evaluates and tries an alternative approach without human intervention.
5. Deliver: It synthesizes the results into a concise, structured answer.

This ability to plan and adapt is what allows Ivy to handle complex, multi-step processes like in-depth lead research or resolving dynamic customer support issues—tasks that would cripple a rigid, scripted bot.

Learning #2: Natural Language is the Most Powerful API

Traditional automation demands you fit your problem into the tool's rigid structure. You need to write complex configurations or code that maps perfectly to the system's predefined inputs.

We learned that to unlock mass adoption, the tool must adapt to the user. That's why we built Ivy to understand instructions in natural language. This philosophy is baked directly into our SDK.

Look at how a task is delegated:

import { ivy } from '@do/sdk';

// Delegate a lead qualification task to Ivy
async function qualifyNewLead() {
  const result = await ivy.do('qualify-lead', {
    prospect: {
      name: 'Jane Doe',
      company: 'Global Tech Inc.',
      website: 'globaltech.com'
    },
    qualificationCriteria: [
      'Company size must be over 1,000 employees.',
      'Must be in the software industry.',
      'Check for recent funding rounds.'
    ]
  });

  console.log('Qualification result:', result);
}

qualifyNewLead();

The qualificationCriteria isn't a complex object of booleans and enums. It's a simple array of strings, just as you'd give instructions to a human team member. Ivy interprets this intent and translates it into an actionable plan. This approach makes powerful automation dramatically more accessible and flexible.

Learning #3: A Digital Worker Must Deliver Services-as-Software

An agent that performs a complex task but returns a messy log file or an unstructured wall of text hasn't finished the job. The final—and most crucial—step of any workflow is delivering a clean, predictable, and usable result.

This is why we embraced a Services-as-Software model. You delegate a task to Ivy as if you were calling a microservice. The immense complexity of the agentic workflow is completely encapsulated. Ivy handles all the browsing, data scraping, and analysis on its own infrastructure, and what you get back is a clean JSON object.

The magic is in the await:

const result = await ivy.do(...)

One line of code triggers a potentially massive workflow, and your program simply waits for the final, structured result. It’s the ultimate abstraction. This turns your digital worker from a simple tool into a reliable service provider integrated directly into your applications and business logic.

Learning #4: Self-Improvement is the Engine of Long-Term Value

A human employee becomes more valuable with experience. They learn shortcuts, recognize patterns, and get better at their job over time. Why should an AI agent be any different?

A static AI is a depreciating asset. We built Ivy on a learning architecture from day one. With each task it performs, Ivy gathers data on which strategies were successful and which were not.

• Did a particular data source prove highly reliable for company information? Ivy will favor it in the future.
• Was a certain search query pattern more effective for finding funding news? That pattern is reinforced.
• Did it get stuck in a loop and have to self-correct? It learns to avoid that pitfall.

This means the Ivy you use a month from now is more efficient, more knowledgeable, and more effective than the Ivy you use today—without you lifting a finger. It's not just an agent; it's a member of your team that grows with your business.

AUTOMATE. EXECUTE. DELIVER.

Building Ivy has taught us that the future of automation isn't about better scripts; it's about better collaborators. By focusing on true autonomy, natural language interaction, service-oriented delivery, and continuous improvement, we've created a digital worker that can finally start to fulfill the long-held promise of automation.

Ready to delegate your first task? Meet Ivy at ivy.do and put your business on autopilot.