We're at a fascinating inflection point in artificial intelligence. For years, we've marveled at the capabilities of Large Language Models (LLMs). We’ve prompted them to write poetry, translate languages, and debug code. But as powerful as they are, we've also begun to feel their limitations. An LLM is like a brilliant encyclopedia; it can give you an answer, but it can't run an errand.
This is where the paradigm shifts. The future of applied AI isn't just about crafting the perfect prompt; it's about defining a goal and empowering an engine to achieve it. We're moving from prompt-and-response to goal-and-execution. This is the dawn of Cognitive Automation, powered by a new kind of tool: the Problem-Solving API.
A standard LLM API is fundamentally a text-completion engine. You provide an input (a prompt), and it generates a statistically likely output (a response). This is incredibly useful for a range of tasks, but it breaks down when a goal requires multiple steps, access to live data, or interaction with other systems.
Consider this goal: "Analyze recent tech news, identify the top 3 emerging AI trends, and write a one-paragraph summary for each."
If you give this to a standard LLM, it will hallucinate an answer based on its training data, which might be months or even years out of date. It can't browse the web, read multiple articles, synthesize findings, and then structure the output. It can only pretend to. To accomplish the goal, a human has to intervene: perform the Google searches, read the articles, feed the summaries back to the LLM, and assemble the final report. The LLM is a tool in the workflow, not the executor of the workflow.
Agentic AI represents the next logical step. An AI Agent is an autonomous system that uses an LLM not just for text generation, but as a core AI Reasoning engine. Instead of just responding, an agent can:
This is the difference between having an encyclopedia and having an expert research assistant. You don't tell the assistant how to do the research; you just give it the objective and the tools it needs.
This new agentic layer requires a sophisticated architecture that's difficult for individual developers and companies to build and maintain. That's why we built thinking.do—a powerful Cognitive Automation Engine delivered through a simple Problem-Solving API.
We handle the complexity of agentic workflows so you can focus on the goals. With thinking.do, you can deploy autonomous AI agents that reason, plan, and execute complex tasks with a single API call.
Look how simple it is to articulate a complex goal:
import { Agent } from '@do/sdk';
const thinker = new Agent('thinking.do');
async function analyzeMarketTrends() {
const result = await thinker.run({
goal: 'Analyze recent tech news, identify the top 3 emerging AI trends, and write a one-paragraph summary for each.',
tools: ['web.search', 'document.summarize']
});
console.log(result.output);
// {
// "trends": [
// { "trend": "...", "summary": "..." },
// { "trend": "...", "summary": "..." },
// { "trend": "...", "summary": "..." }
// ]
// }
}
analyzeMarketTrends();
In the background, the thinking.do agent is creating a dynamic plan. It might first use web.search to find relevant news articles, then pass the contents of the top results to document.summarize, and finally synthesize these summaries to identify the trends and generate the final, structured JSON output. It's completing a task, not just generating text.
The true power of this approach comes from applying it to real-world business problems. By providing agents with secure access to your own internal APIs, databases, and custom functions, you can automate workflows that were previously impossible.
This is the essence of Business-as-Code: encoding complex, dynamic business processes into autonomous agents that can execute them reliably and scalably.
A common fear with advanced AI is that it becomes a "black box." How can you trust a decision if you don't understand how it was made?
We believe transparency is non-negotiable. With thinking.do, the agent's reasoning process is fully auditable. You can review the step-by-step plan it created, see exactly which tools it used with what inputs, and analyze the intermediate results that led to its final output. This observability is crucial for debugging, refinement, and building the trust required for mission-critical applications.
The era of merely prompting AI is evolving into the era of directing it. Cognitive Automation and Agentic AI are enabling us to build applications that don't just talk, but do. They understand goals, create plans, and intelligently execute tasks to achieve results.
If you're ready to go beyond prompts and start achieving goals, it's time to explore the power of a Problem-Solving API.
Ready to build your first autonomous agent? Explore the thinking.do API and start turning your most complex goals into reality.