What is Deep Research, the AI agent who is looking for you?

Artificial intelligence is taking another major step forward with the emergence of Deep Research on the web, a technology that will radically transform our approach to research and documentary analysis. This innovation is much more than just an improvement to today's AI assistants: it marks the advent of truly autonomous agents that can conduct in-depth research and produce detailed data analyses.

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A technological breakthrough in documentary analysis

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Deep Research is distinguished by its ability to operate independently while maintaining relevant user interaction. This technology combines three major innovations:

First, the agent develops and executes their own research plan, adapting their strategy based on the discoveries made. This autonomy in planning represents a significant advance compared to traditional assistants who follow predefined instructions.

Second, the agent has an extensive multi-modal analysis capability, allowing him to explore the web and analyze various document formats - texts, PDFs, images, and videos. This versatility allows for a richer and more nuanced understanding of the topics and data covered.

Finally, user interaction is redesigned to be more natural and productive. The agent can seek clarification or guidance while maintaining autonomy, thus creating an optimal balance between initiative and control.

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The pillars of performance

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Deep Research's performance is based on three key factors that determine its ability to produce relevant and in-depth analyses:

Connector infrastructure is the first pillar. It allows the agent to effectively access and analyze various sources of information. The quality of these connectors directly influences the agent's ability to explore and understand different content formats.
The “supra LLM” capabilities form the second pillar. They include advanced short-term memory and sophisticated RAG (Retrieval-Augmented Generation) mechanisms. These technologies allow the agent to maintain consistency in its analysis and to synthesize large amounts of information effectively.
The power of the underlying language model is the third pillar. It determines the quality of the reasoning and the fineness of the syntheses produced. The latest advances in this area allow for more nuanced analyses and more relevant conclusions.

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Fast adoption by market leaders

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The Deep Research market is evolving rapidly and structurally. Google led the way in December 2024 with Gemini Deep Research, setting a new standard in AI-assisted search. OpenAI quickly followed in February 2025, by integrating this technology into its Pro offer, with prospects for expansion to its other service levels.

Perplexity, already positioned on AI-enhanced research, has naturally enriched its offer with Deep Research capabilities, confirming the relevance of this evolution. At the same time, HuggingFace's open source initiative paves the way for the democratization of this technology, promising to accelerate innovation in this field.

December 2024: Introduction by Google with Gemini Deep Research (link)
February 2025: Deployment by OpenAI (restricted to Pro but access via Plus or even Free coming soon) (link)
February 2025: Perplexity: Addition of Deep Research (natural extension of their basic feature) (link)
February 2025: HuggingFace: Open Source Initiative (link)

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Economic and experiential challenges

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Adopting Deep Research raises critical questions about resource management and user experience. A Deep Research request can involve the analysis of several dozen or even hundreds of contents, and therefore the consumption of tokens. The operational cost, directly linked to this time and scope of treatment, requires a balanced approach between performance and efficiency.

The resolution of this challenge is based on the ability of the LLM to manage its resources intelligently, like a human assistant capable of arbitrating between different priorities and requesting clarifications when necessary.

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Future perspectives

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Deep Research represents a natural and promising evolution of current AI interfaces. By integrating existing functionalities into a more sophisticated and autonomous workflow, this technology paves the way for a new generation of research and analysis tools.

The implications for organizations are significant: improved research productivity, democratization of in-depth analysis, and the possibility of processing volumes of information that were previously difficult to access. Initial feedback suggests that Deep Research could become an indispensable tool for research, analysis and business intelligence professionals.

The rapid evolution of the market and the commitment of the main players in AI demonstrate the transformative potential of this technology. As language models and infrastructures continue to advance, we can anticipate ever more sophisticated and impactful use cases.