Case Study: Automating Quarterly Strategy with the MarketStrategyAI Agent

Quarterly strategy planning. It's a critical process that sets the direction for the entire business, but it's also a high-friction, time-intensive endeavor. It involves pulling data from disconnected silos, wrangling spreadsheets to find correlations, and coordinating insights across multiple teams—all before a single strategic decision is even made.

What if you could distill this entire complex workflow into a single command? What if you could deploy an autonomous agent to handle the data analysis, correlation, and report generation, delivering a complete strategic plan on demand?

This isn't science fiction. This is the power of Autonomous General Intelligence (AGI) agents on the .do platform. Let's walk through a real-world case study: building and deploying a MarketStrategyAI agent to automate the quarterly business strategy process from end to end.

The Challenge: The Manual Grind of Strategy

For most companies, the process of analyzing Q4 performance to plan for Q1 looks something like this:

1. Data Extraction: A data analyst is tasked with exporting raw sales performance data from Salesforce.
2. Marketing Correlation: A marketing manager pulls website traffic and campaign data from Google Analytics.
3. Manual Synthesis: These disparate datasets, often in CSV format, are imported into a massive spreadsheet. A team spends hours, or even days, trying to align timelines, identify trends, and correlate marketing efforts with sales outcomes.
4. Reporting: The findings are manually compiled into a slide deck, which is then presented to leadership. The process is slow, prone to human error, and opportunities are often missed in the sheer volume of data.

Traditional automation can't solve this. Scripts can fetch data, but they can't understand it. They can't reason about the relationship between a spike in website traffic and a rise in closed deals. This is where agentic workflows come in.

The agi.do Solution: An Autonomous Strategist as an API

Instead of a series of rigid scripts, we can deploy a single AGI agent. On the .do platform, an agent is not just a language model; it's an autonomous business unit delivered via API, capable of reasoning, planning, and executing tasks using a set of tools you provide.

Here’s how we build our MarketStrategyAI agent.

Step 1: Initialize the Agent with Business Tools

First, we define our agent and grant it access to the necessary business systems. It doesn't need to know the low-level APIs for these tools; it just needs to know what they are and what they can do. The .do SDK handles the complex integration behind the scenes.

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

// Initialize the AGI with access to relevant business tools
const strategyAgent = new AGIAgent({
  name: 'MarketStrategyAI',
  description: 'An AGI to analyze market data and formulate growth strategies.',
  tools: ['SalesforceReader', 'GoogleAnalytics', 'MarketReportGenerator']
});

Here, we've created a MarketStrategyAI agent and equipped it with three crucial tools:

• SalesforceReader: To access sales performance data.
• GoogleAnalytics: To analyze website traffic and user behavior.
• MarketReportGenerator: To structure its findings into a professional document.

Step 2: Define a High-Level Business Objective

This is where the magic happens. We don't give the agent a series of small, explicit instructions. We give it a high-level, complex objective—the same kind of goal you would delegate to a human strategist.

// Define a complex, high-level objective
const objective = `
  Analyze our Q4 sales performance using Salesforce data and
  correlate findings with website traffic from Google Analytics.
  Generate a comprehensive strategic growth plan for the next quarter,
  including three key initiatives with projected KPIs.
`;

This objective is far too ambiguous for a traditional program but is perfectly understood by an AGI agent. It understands concepts like "correlate findings," "comprehensive strategic growth plan," and "projected KPIs."

Step 3: Run the Agent and Achieve the Objective

With the agent defined and the objective set, all that's left is to run it.

// Run the agent to achieve the objective
const strategicPlan = await strategyAgent.run({ objective });

console.log(strategicPlan);

Behind this single .run() command, an entire agentic workflow is executed autonomously:

1. Reasoning & Planning: The agent analyzes the objective. It understands it needs to fetch data from two sources, find relationships between them, and then generate a report. It formulates a multi-step plan: "First, I'll use SalesforceReader to get Q4 sales. Then, I'll use GoogleAnalytics for traffic patterns. I will then analyze both sets of data to identify correlations. Finally, I will use my findings to formulate three initiatives and format them using MarketReportGenerator."
2. Tool Orchestration: The agent executes its plan, calling the necessary tools in the correct sequence. It might query Salesforce for deals closed in Q4 and then query Google Analytics for traffic sources that led to conversions during the same period.
3. Synthesis & Action: The agent doesn't just present raw data. It synthesizes the information, identifying that a recent blog post series led to a 15% increase in high-quality leads that closed 30% faster. Based on this, it formulates a strategic initiative: "Double down on content marketing for the new product line, with a projected KPI of a 25% increase in MQLs."

The final output, strategicPlan, is a complete, data-backed document that fulfills the complex objective—all from a single API call.

Beyond Automation, Towards Orchestration

This case study demonstrates a fundamental shift from simple automation to intelligent orchestration. While a script automates a known, static process, an AGI agent orchestrates multiple tools and services to achieve a dynamic, high-level goal.

This is the core philosophy of Business-as-Code. You can now codify complex business operations like market research, supply chain optimization, or ad budget management and execute them with the reliability and scalability of software.

The MarketStrategyAI agent frees your human team from the drudgery of data collection and synthesis, empowering them to focus on what they do best: final decision-making, creative thinking, and customer relationships.

Ready to turn your most complex workflows into a single API call?

Explore the agi.do Documentation and start building today.

Frequently Asked Questions (FAQs)

Q: How does this differ from using a model like GPT-4?
A: While models like GPT-4 are excellent at generating text or answering questions based on provided context, an agi.do agent can act. It autonomously uses tools, interacts with external systems (like your CRM), and executes multi-step plans to achieve a goal in the real world. It goes from providing information to producing outcomes.

Q: Is it difficult to integrate my own tools?
A: Not at all. The .do platform is designed to abstract away complexity. You can easily create wrappers for your internal APIs or databases, making them available as "tools" for your AGI agents with a simple and elegant SDK.

Q: What other kinds of tasks can AGI agents handle?
A: Any complex business process is a candidate. Imagine agents that can: 'Monitor supply chain disruptions and automatically re-route shipments,' 'Manage and optimize a multi-channel digital advertising budget in real-time,' or 'Conduct comprehensive market research and draft an investment thesis.' If you can define it as an objective, an agent can work to achieve it.