The way we search for and summarize information has changed dramatically since the spring of 2025. In this article, we unpack how Deep Research agents work and where they add the most value.
Today, nearly every AI provider offers some form of Deep Research solution. Typically, it’s a separate function within the user interface, allowing the user to indicate that the given query requires more in-depth consideration — where quality and comprehensiveness matter more than speed.
A Deep Research response is usually a multi-page essay, carefully sourced and referenced. It can take several minutes, sometimes up to half an hour, to complete. The underlying data is gathered through a series of targeted internet searches that the agent selects and executes independently
Where Do Research Agents Excel?
Deep Research shines in situations where there’s a wealth of information online that would take too long to sift through manually. Ideal use cases include market research, trend analysis, and synthesizing complex news topics.
Here are a few recent examples from my own work:
- Demand and use cases for digital twins in manufacturing, highlighting successes and failures
- Reactions to the US AI Diffusion regulatory framework in the EU
- Maturity and adoption rate of the Model Context Protocol standard
- Microsoft Fabric offerings among Finnish IT service providers
- How our client’s digital strategy stands out from competitors
Research agents have radically altered my workday. These days, I often start my morning by launching a few Deep Research queries that will be ready later in the day. I prioritize these tasks based on the themes I need to dive into. Between meetings, I try to read through the completed briefs so I can participate fully prepared.
How Do Research Agents Work?
Deep Research is perhaps the clearest example of an AI agent solution today. The term agent means that the AI solution works persistently and partially independently to solve a task set by the user. Technically, it’s not a new “agent model” but rather an application that leverages language models and other data sources to handle complex queries. The architecture relies on four key innovations:
Firstly, the agent leverages the reasoning capabilities of language models. Modern models can now break down complex tasks and reason through the next steps — for instance, “What data should I look for next?”, “How do I verify this source?”, and “When have I gathered enough information to meet the user’s needs?”
Secondly, the agent relies on specific tools. For Deep Research, the most important tool is internet search and the ability to read web pages. As agents evolve, they will increasingly use specialized tools for mathematical problem-solving and data visualization. Currently, the outputs are mostly text, but the handling of numerical data and visualizations is improving rapidly.
Third, Memory is essential for effective operation. As many have discovered when experimenting with language models, prompts that are too long don’t necessarily improve output quality. Breaking the task into segments requires temporary storage: the agent extracts key data points from each source, stores them, assesses the next steps based on the findings, and finally compiles the collected data into a report.
Lastly, the risk of hallucinations and misinterpretations decreases when the agent revisits the data multiple times. Typically, research agents implement some form of a maker/checker process, where the AI acts in two roles — first as the data gatherer, and then as the verifier. Even if both roles are performed by the same model, the impact on data accuracy is significant.
Research Agents Are Becoming a Part of Daily Work
The need for research agents is undeniable. The workplace is full of situations where large, potentially conflicting datasets must be compared and analyzed. Currently, tools like ChatGPT’s Deep Research are a significant asset - as long as the data is publicly available online.
The biggest challenge arises when the data lies within the organization. For example, a query like “Compare last year’s product development plans with this year’s releases and list the discrepancies” would be a perfect task for a research agent, but as of now, it’s impossible. Current research agents can’t access internal data.
However, the landscape is changing rapidly. Microsoft 365’s newly announced Researcher agent aims to bridge this gap, allowing access to internal data sources.
Regardless of the tool, AI-powered research is set to become a standard part of work routines. Our job is to identify the tasks and situations where we can prepare better by having the data compiled in advance. Hopefully, one day, that task will be handled by a calendar-integrated agent that greets me every morning with a briefing of the day’s tasks and the background data I need to be fully prepared.
