AGI-Experiments: How to A/B Test Entire Business Strategies with Autonomous Agents

Traditional A/B testing is a cornerstone of digital optimization. We test headlines, button colors, and user flows to eke out incremental gains. But what about the big questions? How do you A/B test an entire go-to-market strategy? Or compare a product-led growth model against an enterprise sales push?

These high-stakes decisions are typically made based on historical data, gut feelings, and months of deliberation. They are slow, expensive, and you only get one shot. What if you could run these complex scenarios as experiments, comparing fully-formed strategic plans before committing a single dollar to execution?

This is no longer science fiction. With Autonomous General Intelligence (AGI) agents, you can codify, simulate, and compare complex business strategies in parallel. Welcome to the next generation of strategic decision-making, powered by agi.do.

From Simple Automation to Intelligent Orchestration

Before we dive into the experiment, it's crucial to understand what makes an agi.do agent different. Unlike AI models that primarily generate content or answer questions (like GPT), AGI agents are designed to act.

An AGI agent on the .do platform is a sophisticated AI that can:

• Reason: Understand complex, ambiguous, high-level objectives.
• Plan: Break down a goal into a sequence of logical steps.
• Act: Autonomously use tools—like your CRM, analytics platforms, and internal APIs—to execute that plan.

This is the leap from automation to orchestration. You don't tell the agent how to do something; you tell it what to achieve, and it orchestrates the necessary resources to get it done.

The AGI Experiment: A/B Testing Your Next Big Move

Let's put this into practice. Imagine your company has a single, overarching goal for the next half: Increase enterprise customer acquisition by 20%.

The leadership team is divided on the best strategy. Do you double down on a high-touch, outbound sales motion or invest in a long-term, content-driven inbound engine? Instead of endless debate, let's run an AGI-Experiment.

Step 1: Define Your Strategic Hypotheses

We'll create two AGI agents, each representing one of our strategic hypotheses.

• Agent A: The Outbound Orchestrator. This agent will formulate a plan focused on aggressive outbound sales, targeted advertising, and direct engagement.
• Agent B: The Inbound Architect. This agent will build a strategy around content marketing, SEO, and attracting customers organically.

Step 2: Equip Your Agents for the Job

Using the agi.do SDK, we initialize both agents. We grant them access to the same set of business tools, ensuring a level playing field. The only difference is their core strategic directive.

import { AGIAgent } from '@do/agi';

// Define the shared tools both agents can use to reason and act
const sharedTools = [
  'SalesforceReader', 
  'GoogleAnalytics', 
  'MarketReportGenerator', 
  'BudgetPlannerAPI'
];

// --- STRATEGY A: AGGRESSIVE OUTBOUND ---
const outboundAgent = new AGIAgent({
  name: 'OutboundStrategyAI',
  description: 'An AGI that formulates a growth strategy based on aggressive, high-touch outbound sales and targeted advertising.',
  tools: sharedTools
});

const objectiveA = `
  Analyze our current enterprise sales pipeline in Salesforce and website traffic.
  Formulate a growth plan to increase enterprise acquisition by 20% in 6 months, focusing
  primarily on outbound sales tactics and a supporting paid ad campaign.
  Provide a detailed plan, budget allocation, and projected KPIs.
`;

// --- STRATEGY B: CONTENT-DRIVEN INBOUND ---
const inboundAgent = new AGIAgent({
  name: 'InboundStrategyAI',
  description: 'An AGI that formulates a growth strategy based on content marketing, SEO, and product-led growth principles.',
  tools: sharedTools
});

const objectiveB = `
  Analyze our current organic traffic from Google Analytics and content performance.
  Formulate a growth plan to increase enterprise acquisition by 20% in 6 months, focusing
  primarily on a long-term inbound content strategy and SEO improvements.
  Provide a detailed plan, content calendar, and projected KPIs.
`;

// Run both experiments to generate competing strategic plans
console.log("Running Outbound Strategy Experiment...");
const outboundStrategicPlan = await outboundAgent.run({ objective: objectiveA });

console.log("Running Inbound Strategy Experiment...");
const inboundStrategicPlan = await inboundAgent.run({ objective: objectiveB });

Step 3: Analyze the Orchestrated Plans

This is where the magic happens. In a matter of minutes, not months, you receive two complete, data-driven strategic plans. The outboundStrategicPlan and inboundStrategicPlan variables don't just contain text; they hold structured, actionable intelligence.

You can now compare, side-by-side:

• Projected Costs: How does the budget allocation differ for ad spend vs. content creation?
• Resource Planning: What team members or roles are required for each plan?
• Timelines & Milestones: What are the key initiatives and their expected completion dates?
• Projected KPIs: How does each agent project its impact on lead velocity, cost of acquisition, and ultimately, revenue?
• Identified Risks: What potential roadblocks did each agent uncover during its analysis?

You have effectively transformed a complex, high-risk business decision into a quantifiable comparison, a concept we call Business-as-Code.

The Future is Agentic Workflows

This experiment is just one example of a powerful new paradigm: Services-as-Software. By using agi.do, you're not just automating isolated tasks. You are codifying entire business functions—like strategic planning, market research, or supply chain optimization—and deploying them as autonomous agents via a simple API.

This approach allows you to de-risk major decisions, accelerate your innovation cycle, and build a more intelligent, adaptable organization. The Agentic Workflow moves beyond static scripts and allows your systems to reason, learn, and act on their own to achieve business goals.

Ready to go beyond automation and achieve true orchestration? Explore Autonomous General Intelligence on agi.do and start running your own AGI-Experiments today.