In today's fast-paced business environment, staying ahead of market trends isn't just an advantage—it's a necessity. But traditional market research is a bottleneck. It's a manual, time-consuming process of sifting through countless articles, reports, and data streams. It’s expensive, slow, and the results are often outdated by the time they’re compiled.
What if you could delegate this entire complex task to an autonomous AI agent? What if you could simply state your goal and receive a structured, data-backed analysis in minutes, not days?
This isn't science fiction. This is the power of an AI reasoning engine. In this case study, we'll walk through how thinking.do transforms a high-level business objective into actionable intelligence, demonstrating the future of cognitive automation.
Imagine you're a strategy lead at a tech company. Your task is to identify the next big wave in artificial intelligence to guide your product roadmap. Your current process looks something like this:
This workflow is fundamentally broken. It’s not scalable, it’s prone to error, and it keeps your brightest minds bogged down in low-level data gathering instead of high-level strategic thinking.
To showcase the power of agentic AI, we gave a thinking.do agent a clear and complex goal—the very one our strategy lead was tasked with:
"Analyze recent tech news, identify the top 3 emerging AI trends, and write a one-paragraph summary for each."
This is not a simple prompt for a Large Language Model (LLM). It requires planning, tool use, analysis, and synthesis. It’s a goal, not a question.
A standard LLM might provide a generic or even hallucinated answer based on its training data. The thinking.do cognitive automation engine works differently. It acts as an autonomous agent, using an LLM as its reasoning core to create and execute a multi-step plan.
Here’s a look under the hood at its thought process:
First, the agent receives the goal. Instead of immediately trying to answer, it deconstructs the request into a logical sequence of steps.
This transparent planning process is crucial. It moves from a vague goal to a concrete, executable strategy.
With a plan in place, the agent selects the necessary tools from its available toolkit to execute each step. This is Business-as-Code in action.
For this task, the agent identifies that it needs two primary tools:
The agent then runs the plan, calling these tools as needed, feeding the output of one step into the input of the next. It finds articles, reads them, identifies patterns, and drafts summaries, all without human intervention.
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);
}
analyzeMarketTrends();
The final, and perhaps most critical, step is synthesis. The agent doesn't just return a wall of text. It assembles the summarized information into the precise, machine-readable format requested by the user.
{
"trends": [
{
"trend": "Agentic AI Workflows",
"summary": "Emerging AI systems are moving beyond single-prompt responses to become autonomous agents that can plan, use tools, and execute complex, multi-step tasks. These 'agentic workflows' are being leveraged for applications like automated software development, complex research, and business process automation, representing a shift from conversational AI to 'task completion AI'."
},
{
"trend": "On-Device and Edge AI",
"summary": "There is a significant industry push to run smaller, more efficient AI models directly on user devices like smartphones and laptops. This approach enhances privacy, reduces latency, and lowers dependency on cloud infrastructure. Major tech companies are releasing new hardware and software frameworks optimized for on-device inference, enabling powerful real-time AI features without a constant internet connection."
},
{
"trend": "Multimodal AI",
"summary": "The latest generation of AI models can natively understand and process information across multiple modalities, including text, images, audio, and video. This allows for more sophisticated and context-aware applications, from generating images from detailed descriptions to analyzing video content for specific events. Multimodality is breaking down the barriers between different data types, leading to more intuitive and powerful human-computer interaction."
}
]
}
By using an AI reasoning engine, our strategy lead achieved their goal with unparalleled efficiency and reliability.
This case study is just one example. The power of a cognitive automation engine like thinking.do can be applied to any complex, multi-step problem:
By moving from simple prompts to goal-oriented execution, you can build a new class of intelligent applications. You can automate the mundane, accelerate the complex, and empower your team to focus on what matters most: making great decisions.
Ready to go beyond prompts and achieve goals? Integrate the thinking.do cognitive engine into your applications with a simple API call.