Generative AI is transforming industries, but many businesses are hitting a wall. While large language models (LLMs) are incredibly powerful at processing public information, they operate in a vacuum. They don't know your customers, your inventory, your internal processes, or your unique business logic. It's like hiring a genius-level consultant who has read every book in the world but is forbidden from entering your office or looking at your files.
This is the context gap. To move from simple text generation to true cognitive automation, your AI needs to be granted access to your world.
Enter the next evolution: Agentic AI. Platforms like thinking.do are not just about responding to prompts; they are cognitive automation engines designed to achieve complex goals. These autonomous AI agents can reason, create a plan, and execute it using a variety of tools. The real magic happens when you provide them with your tools and your data.
A standard LLM can write a generic email or summarize a famous historical event. But it can't answer questions like:
Answering these questions requires access to proprietary information locked away in your CRM, ERP systems, and internal wikis. This is where thinking.do shines. It's not an LLM replacement; it's an agentic workflow platform that uses an LLM as its reasoning core. It's designed to securely bridge the gap between AI's general intelligence and your specific business context.
Unlocking true AI Reasoning within your organization involves giving your agent a secure connection to two key elements: your data and your unique business functions.
An AI agent's effectiveness is directly proportional to the quality of the information it can access. By providing read-only access to your databases, APIs, and data warehouses, you empower your agent to perform sophisticated analysis based on real-time, proprietary information.
Business Example: Automated Stakeholder Reporting
Imagine you need a weekly report summarizing sales performance and identifying at-risk accounts.
The Goal: Generate a weekly summary for the sales leadership team, highlighting the top 5 deals closed, identifying 3 enterprise accounts with recent high-priority support tickets, and listing their upcoming renewal dates.
How it Works with thinking.do:
An agent is deployed with a set of secure tools:
The agent reasons about the goal, plans its steps (e.g., "First, get top deals. Second, find at-risk accounts. Third, compile a report."), executes each step using the right tool, and synthesizes the findings into a coherent, actionable summary.
import { Agent } from '@do/sdk';
const financeReporter = new Agent('thinking.do');
async function createQuarterlySummary() {
const result = await financeReporter.run({
goal: 'Analyze Q3 sales data from our internal postgresDB, cross-reference it with marketing spend from the analytics API, and write a two-paragraph summary on campaign ROI.',
tools: ['database.postgres.query', 'api.internal.get']
});
console.log(result.output);
// {
// "summary": "Q3 saw a 15% increase in sales, strongly correlated
// with the 'Summer Sprint' digital ad campaign. The campaign
// yielded an estimated ROI of 4.5:1...",
// "data_points": [ ... ]
// }
}
createQuarterlySummary();
Every business has its own unique processes, calculations, and logic. Agentic AI allows you to encapsulate this logic into custom "tools" that the agent can use. This is Business-as-Code in practice: turning your proprietary methods into executable functions that an AI can strategically deploy.
Business Example: Dynamic Logistics Optimization
A logistics company needs to find the most cost-effective way to fulfill an order.
The Goal: For order #12345, find the optimal fulfillment plan considering warehouse inventory, real-time shipping rates from our private carrier API, and customer delivery tier.
How it Works with thinking.do:
The agent is given custom tools that represent your business logic:
The agent doesn't just follow a script. It uses these tools to gather information, weighs the trade-offs (e.g., "Warehouse A is closer, but Warehouse B has a lower picking fee."), and reasons its way to the best solution, executing a complex decision-making process in seconds.
Handing over tasks to an AI can be daunting. That's why transparency is non-negotiable. With a true cognitive automation engine, the agent's work is never a mystery.
One of the most common questions we get is, "Is the agent's reasoning process a 'black box'?" The answer is a definitive no. thinking.do provides full observability into the agent's thought process. You can review the step-by-step plan it created, the exact tools it used, the data it saw, and the reasoning behind its decisions. This allows for complete oversight, easy debugging, and the trust needed to deploy AI for mission-critical tasks.
Integrating AI with your private data and custom tools is the defining step in moving from novelty to necessity. It's how you graduate from generic prompts to achieving complex, business-specific goals. By creating a sandbox where an AI agent can safely access your context, you don't just automate tasks—you build a resilient, intelligent system that can reason, adapt, and solve problems at scale.
Ready to build an AI that understands your business? Visit thinking.do to learn more about our Problem Solving API and deploy your first cognitive agent.