We've all been amazed by the power of large language models (LLMs). They can write poetry, draft emails, and translate languages in the blink of an eye. But when you move from simple instructions to complex, multi-step problems, you often hit a wall. The answers can become generic, lose context, or lack the depth required for real-world decision-making.
What if an AI could do more than just respond? What if it could reason? What if it could receive a complex objective, break it down into a strategic plan, execute the steps, and synthesize the findings into a coherent, expert-level result?
This isn't science fiction. This is the power of agentic workflows, and it represents the next major leap in artificial intelligence.
A standard LLM operates on a simple request-response model. You give it a prompt, it gives you an answer.
An agentic workflow, on the other hand, elevates the AI from a simple respondent to an autonomous agent. Instead of just generating text, an AI agent can:
Think of it as the difference between asking an assistant for a single statistic versus delegating a full market analysis report. The agentic system acts like a senior strategist, managing the entire project from start to finish. This is the core of Cognitive Automation.
Let's consider a complex problem, like the one in our code example below:
"Analyze current market trends in renewable energy and formulate a three-point strategic plan for a new startup focused on residential solar panel installation."
A traditional LLM might provide a decent but generic overview. An agentic system, however, understands this isn't a single question but a multi-layered project. It might internally create a plan like:
This structured approach is what separates a shallow answer from a deep, strategic analysis.
This is precisely why we built thinking.do. We saw the need for an AI that could handle ambiguity and complexity with genuine reasoning. Our Cognitive Automation Engine provides this agentic capability through a simple API call.
You don't need to engineer complex prompt chains or manage state. You simply define the problem, and our agent handles the rest.
Here's how easily you can leverage our problem-solving API to generate an expert-level strategic plan:
import { Do } from '@do-sdk';
const doClient = new Do({ apiKey: process.env.DO_API_KEY });
async function getStrategicPlan() {
const problem = 'Analyze current market trends in renewable energy and formulate a three-point strategic plan for a new startup focused on residential solar panel installation.';
const { result } = await doClient.agent('thinking').run({
prompt: problem,
complexity: 'expert',
output_format: 'markdown_list'
});
console.log(result);
}
getStrategicPlan();
With just a few lines of code, you delegate the entire cognitive workload—the research, analysis, and strategic formulation—to an AI agent designed for the task.
Integrating a reasoning engine like thinking.do into your applications unlocks several powerful benefits:
Because the agent breaks the problem down, it produces solutions that are far more detailed, nuanced, and reliable than a single-shot generation. It builds its conclusion from a foundation of structured sub-tasks, reducing the chance of hallucination or superficiality.
How can you trust an AI's conclusion if you can't see its work? Our API offers a 'chain of thought' option that provides a step-by-step log of the agent's reasoning process. This transparency is critical for verification, debugging, and building trust in the output.
From creating business plans and performing market analysis to refactoring complex codebases and reviewing legal documents, thinking.do is designed for a vast range of open-ended problems that require analysis, synthesis, and strategic reasoning.
You remain in the driver's seat. Use parameters like complexity, depth, and creativity to guide the agent's approach. For fact-based tasks, you can even instruct it to cite its sources, ensuring the final output is both accurate and verifiable.
The conversation around AI is shifting. We are moving beyond the novelty of text generation and into the era of true cognitive partnership. Agentic workflows are transforming AI from a clever tool into a powerful collaborator capable of tackling real-world, complex challenges.
By automating reasoning itself, we can build smarter applications, make better decisions, and unlock new levels of productivity.
Ready to move beyond the prompt? Integrate our advanced cognitive agent into your application today. Explore the thinking.do API and experience the power of Reasoning as a Service.