Pushing the Limits: Creative Experiments with an Autonomous Agent

The conversation around AI is shifting. We've moved past the novelty of simple chatbots and into a new era defined by action and autonomy. This is the age of the autonomous agent—a system that doesn't just respond, but reasons, plans, and executes complex tasks from start to finish.

Enter Lena, your autonomous digital worker on the .do platform. Lena is designed to understand high-level business objectives and independently manage the entire workflow to achieve them. Forget micromanaging rigid scripts; with Lena, you delegate the goal, and she handles the "how."

While Lena is incredibly powerful for standard business operations like market research and data analysis, we wanted to push the boundaries. What happens when you get truly creative with an autonomous agent? We ran a few experiments to find out.

The Power of a Simple assign

Before we dive into the experiments, let's look at what makes Lena different. Unlike traditional automation tools that follow a strict, predefined path, Lena operates on an agentic workflow. You give her a destination, and she charts her own course.

This is all done through a surprisingly simple API. You don't need to code every step of the process. You just need to define your goal.

import { Do } from '@do-sdk/core';

// Initialize the .do client
const dispatch = new Do();

// Get a client for the Lena agent
const lena = dispatch.agent('lena');

// Assign a high-level goal to Lena
const project = await lena.assign({
  goal: 'Analyze market trends for solar energy in Q3',
  deliverable: 'A comprehensive report with key findings and data visualizations.'
});

console.log(`Project assigned to Lena. ID: ${project.id}`);

This simplicity is the key. It allows you to transform complex business processes into scalable, on-demand services—a concept we call Business-as-Code.

Now, let's see what happens when we give Lena some more unconventional goals.

Experiment 1: The Competitive Moat Detector

Standard competitive analysis is useful, but it often only scratches the surface. We wanted to see if Lena could go deeper and identify the intangible, hard-to-replicate advantages that protect a business.

• The Challenge: Identify a company's true competitive moat.
• The Goal for Lena: "Analyze our company and our top 3 competitors. Identify our primary competitive moat by evaluating customer sentiment from online reviews, patent filings for unique technology, and corporate financial statements for margin defensibility. Deliver a presentation outlining the moat, its key threats, and three strategic recommendations to strengthen it."
• Lena's Autonomous Workflow:
  1. Plan: Lena first breaks the goal into sub-tasks: identify competitors, find data sources (review sites, patent offices, SEC filings), analyze each data type, synthesize findings, and build a presentation.
  2. Execute: She scrapes G2 and Capterra for review data, running sentiment analysis to find recurring praise ("great customer service," "irreplaceable feature"). She then queries patent databases for unique intellectual property and pulls quarterly reports to compare profit margins over time.
  3. Reason: Lena connects the dots. She notices that while a competitor has more features, our company's consistently higher margins and glowing reviews about "integration support" point to a service-based moat, not a technological one.
• The Outcome: A slide deck that doesn't just list strengths and weaknesses but delivers a high-level strategic insight: "Our competitive moat is our white-glove onboarding and support. To strengthen it, we should invest in certifying implementation partners." This is actionable intelligence, not just data.

Experiment 2: The Emergent Hype Tracker

Staying ahead of the curve is critical. But how do you spot a trend before it's a trend? We tasked Lena with becoming an automated "cool hunter."

• The Challenge: Find the next big thing in a specific niche before it hits the mainstream.
• The Goal for Lena: "Continuously monitor arXiv, niche tech subreddits (like r/singularity), and Y Combinator funding announcements for emerging concepts in 'agentic AI'. Identify the top 3 most promising, under-the-radar developments each week. Compile a Monday morning briefing with sources, a summary of a why-it-matters, and an 'expert' I can follow on X for each trend."
• Lena's Autonomous Workflow:
  1. Plan: Lena recognizes this is a recurring task. She sets up a schedule to run her queries every Sunday. She establishes criteria for "promising," such as a concept appearing across multiple source types (e.g., a paper on arXiv is cited in a popular subreddit discussion).
  2. Execute: She uses APIs and web crawlers to gather new data daily, filtering for keywords. She then analyzes the text to identify novel terms and track their frequency over time.
  3. Adapt: Over a few weeks, Lena learns to filter out the noise. She notices that papers co-authored by certain researchers gain more traction and begins to weight them more heavily in her analysis.
• The Outcome: An automated intelligence briefing that lands in your inbox every Monday. It provides a massive head-start, giving your team the ability to explore new technologies, talent, or investment opportunities weeks or even months before your competitors.

Experiment 3: The Full-Stack Product Launch Coordinator

Complex projects require coordinating multiple steps, tools, and deliverables. We tested whether Lena could orchestrate an entire business process from a single command.

• The Challenge: Automate the busywork of a new feature launch.
• The Goal for Lena: "Coordinate the launch of our new feature, 'Services-as-Software'. Your deliverable is a completed launch checklist. Tasks include: drafting a 500-word blog post announcement, creating a social media campaign with 5 posts for X, finding 20 tech journalists who cover business automation, and drafting a personalized outreach email for the top 5."
• Lena's Autonomous Workflow:
  1. Plan: Lena creates a dependency graph: first, understand the feature from internal docs (hypothetically accessible via an internal API). Then, draft the blog post, as its content will inform the social media posts and emails. Finally, research journalists and draft the outreach.
  2. Execute: She writes a compelling blog post draft. She then atomizes its key messages into five distinct tweets. Next, she performs a targeted web search to identify journalists, ranking them by relevance and recent activity.
  3. Synthesize: For the top 5 journalists, Lena crafts personalized emails. She references a recent article each one wrote and connects it to the new feature, demonstrating a level of personalization that a simple mail merge could never achieve.
• The Outcome: A centralized folder containing a drafted blog post, a social media schedule, a target journalist list, and personalized pitch emails. All that's left is a quick human review and the click of a "send" button. Lena has acted as a true force multiplier, saving the marketing team dozens of hours.

From Experiment to Enterprise

These creative experiments highlight a fundamental shift. Lena isn't just another automation tool; she is a programmable, scalable member of your team. By delegating high-level objectives, you're not just offloading tasks—you're codifying your business's core processes into intelligent, on-demand services.

This is the future of knowledge work. It's a future where your human team is freed from complex, time-consuming execution to focus on what they do best: strategy, creativity, and building relationships. Lena gives them the leverage to do more, faster, and better than ever before.

What complex challenge would you delegate?

Ready to build your autonomous teammate? Get started with Lena and the .do platform today.