What if there was a tool that didn’t just assist you but completely redefined how you approach complex tasks? Imagine a system that could seamlessly browse the web for critical data, write detailed reports, and even build custom tools on the fly, all while collaborating with specialized agents designed to tackle specific challenges. Enter the Deep Research Agent, a new innovation by Skywork AI. This isn’t just another AI framework; it’s a multi-agent powerhouse that combines innovative models, dynamic tool creation, and unparalleled adaptability to handle tasks with precision and efficiency. Whether you’re a researcher, developer, or strategist, this system promises to transform how you work.

Prompt Engineering explain the intricate architecture behind the Deep Research Agent, including its Agent Orchestra framework, which enables seamless collaboration between specialized agents. You’ll discover how this open source tool doesn’t just solve problems but evolves to meet unique challenges by creating and managing tools in real-time. From automating web browsing to generating actionable insights, the possibilities are vast, and the implications for industries ranging from tech to media are profound. By the end, you might just find yourself rethinking what’s possible in task automation.

Deep Research Agent Overview

The Agent Orchestra Framework: A Collaborative Core

At the heart of the Deep Research Agent lies the “Agent Orchestra,” a hierarchical framework that orchestrates the collaboration of specialized agents. Each agent is carefully designed to excel in specific tasks, working in unison to tackle complex challenges. The framework’s adaptability stems from its ability to dynamically create and manage tools, making sure it can address unique requirements, even when existing tools are insufficient. This dynamic approach allows the system to evolve continuously, offering tailored solutions to meet the demands of various industries.

Specialized Agents: Precision in Action

The Deep Research Agent employs a suite of specialized agents, each functioning as an expert in its domain. These agents work collaboratively to deliver precise and efficient results:

• Deep Analyzer Agent: Performs in-depth analysis to extract actionable insights from diverse data types, allowing informed decision-making.
• Deep Researcher Agent: Synthesizes information from extensive research, producing detailed reports, summaries, and comprehensive insights.
• Browser Use Agent: Automates web browsing to streamline data collection, making sure efficient and accurate information extraction.
• MCP Manager Agent: Oversees tool discovery, registration, and execution using the MCP protocol, making sure seamless tool integration and management.

Skywork AI’s Multi-Agent System : Browses, Writes and Builds Tools

Here is a selection of other guides from our extensive library of content you may find of interest on multi-agent framework.

Dynamic Tool Creation: Tailored Solutions

A standout feature of the Deep Research Agent is its ability to dynamically create tools. When existing tools fail to meet specific requirements, the system synthesizes new ones, validates their functionality, and registers them for future use. This capability ensures the framework remains adaptable and responsive to evolving needs, providing customized solutions for even the most intricate challenges. By continuously expanding its toolset, the system enables users to tackle tasks with unparalleled efficiency and precision.

Applications Across Industries

The versatility of the Deep Research Agent makes it an invaluable tool across a wide range of industries and tasks. Its applications include:

• Document creation, including the generation of Word documents, PDFs, and presentations tailored to specific needs.
• Data analysis, such as trend visualization, market insights, and real-time updates to Excel spreadsheets.
• Web development and comprehensive market research to support strategic decision-making.
• API integration for custom workflows, allowing seamless automation and enhanced productivity.

Technological Features: Innovation at Its Core

The Deep Research Agent incorporates advanced technologies to deliver exceptional performance and flexibility. Key features include:

• Integration of multiple AI models: Combines the strengths of OpenAI, Google, and open-weight models to achieve superior results.
• Support for local and remote tools: Offers maximum adaptability by seamlessly integrating tools across different environments.
• Open source availability: Accessible on GitHub, allowing users to customize and experiment with the framework to suit their specific needs.

Skywork AI’s Broader Vision

Skywork AI’s innovations extend beyond the Deep Research Agent, showcasing a commitment to advancing AI capabilities across various domains. The company’s other new projects include:

• 3D world generation from single images, transforming virtual environments and simulations.
• Open source multimodal reasoning models designed for complex problem-solving and decision-making.
• Infinite-length film generative models, pushing the boundaries of creative AI applications in media and entertainment.
• Image generation, understanding, and editing tools for diverse creative and analytical purposes.

Performance and Accessibility: Designed for Users

The Deep Research Agent has demonstrated exceptional performance, achieving high scores on GAIA and humanity benchmark tests. Its ability to deliver state-of-the-art results across various applications underscores its reliability and efficiency. For users, the framework offers API access for tasks such as document creation and data analysis. To encourage adoption, free credits are provided for initial testing, with tiered packages available for extended use. This accessibility ensures that organizations and individuals can use the system’s capabilities without significant barriers.

Setting a New Standard in Task Automation

The Deep Research Agent represents a fantastic advancement in multi-agent frameworks, combining precision, adaptability, and scalability. By integrating advanced AI models, dynamic tool creation, and open source accessibility, it establishes a new benchmark for task-solving systems. Whether automating workflows, conducting in-depth research, or exploring creative applications, this framework offers a robust and versatile solution tailored to meet the demands of modern industries.

Media Credit: Prompt Engineering

Filed Under: AI, Top News

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AI rivals OpenAI and Anthropic shed new light on the AI revolution this week, releasing the first major data-driven studies on how their technology is used.

The reports analyze millions of interactions to reveal a surprising truth: most people use AI for personal tasks, not their jobs, with 70% of ChatGPT conversations being non-work-related.

The findings also expose a growing global “AI divide,” as adoption is highest in wealthy nations and businesses lean heavily on automation.

This research provides the first concrete look at AI’s true economic and social footprint, clarifying its role in our daily lives and why its impact is so uneven.

The Great Divide: AI for Work vs. Play

The most striking revelation from the new research is that generative AI, long touted as a revolutionary workplace tool, is overwhelmingly a consumer phenomenon.

According to a landmark study from OpenAI’s economic research team, published as a National Bureau of Economic Research (NBER) working paper, a staggering 70% of all consumer ChatGPT conversations as of July 2025 were not related to work.

This finding, derived from the largest-ever study of AI usage based on 1.5 million conversations, fundamentally reframes the narrative around artificial intelligence, suggesting its primary impact is currently centered on personal life, not professional productivity.

This trend is not static; it’s accelerating. The NBER paper details how non-work-related messages have grown significantly faster than professional ones, swelling from just 53% of all usage in June 2024 to over 70% a year later.

The rapid shift indicates that as AI becomes more mainstream, its role is increasingly defined by personal utility—assisting with everyday tasks, providing practical guidance, and serving as a source of information outside the office.

Data shows that while “Writing” is the most common work-related task, it is dwarfed by the volume of queries related to personal guidance and information seeking, which collectively account for nearly 80% of all conversations.

This explosion in personal use is happening on a massive scale, as ChatGPT’s overall user base has surged to 700 million weekly active users. The 70% figure represents the activity of a vast and broadening global audience.

The study, which analyzed a representative sample of conversations using a privacy-preserving automated pipeline, also provides clear evidence that the platform is moving beyond its initial tech-centric, male-dominated user base.

The research documents a dramatic closing of the gender gap among users, signaling a true democratization of the technology.

Specifically, the analysis of user first names shows that in January 2024, only 37% of users had typically feminine names. By July 2025, that figure had climbed to 52%, indicating that the user base now reflects a more balanced demographic that is more in line with the general population.

This demographic shift underscores the broadening appeal of AI as a tool for everyone, not just early adopters in the tech industry, and helps explain the rapid growth in non-professional use cases that now define the platform’s activity.

Ultimately, the data suggests that while much of the economic discourse has focused on AI’s potential to augment or automate jobs, its most significant immediate value may lie in what the researchers call “home production” and personal decision-making.

This conclusion is supported by external economic analyses cited in the paper, which estimate that the consumer surplus—the economic value people receive above what they pay—from generative AI is at least $97 billion annually in the U.S. alone.

The massive figure quantifies the tangible, albeit often overlooked, benefit that millions derive from using AI to navigate their daily lives.

The Emerging AI Economy: Automation, Augmentation, and Geography

While consumers are increasingly using AI as a personal advisor, a separate, parallel trend is unfolding in the business world: the aggressive deployment of AI for pure automation.

A detailed report from Anthropic reveals that a commanding 77% of enterprise use of its Claude model via API is “automation-dominant.” This means businesses are programmatically delegating complete tasks to the AI, allowing its output to flow directly into downstream systems with minimal human interaction.

This approach, focused on direct task completion in areas like coding and administrative support, stands in stark contrast to consumer patterns, where usage is split almost evenly between automation and “augmentation”—a more collaborative, iterative process of learning and refinement between the user and the AI.

For the first time, Anthropic’s research also maps the geography of AI adoption, uncovering a stark global divide that mirrors historical patterns of technological diffusion.

To measure this, the report introduces the Anthropic AI Usage Index (AUI), which compares a country’s share of Claude usage to its share of the global working-age population.

The results show that AI use is heavily concentrated in high-income, technologically advanced countries. Singapore and Canada, for example, exhibit usage rates 4.6 and 2.9 times higher than expected based on their populations, respectively.

The report finds a strong positive correlation between a country’s AUI and its GDP per capita, suggesting that factors like robust digital infrastructure and a higher concentration of knowledge workers create fertile ground for adoption.

Conversely, many emerging economies, including India (0.27x), Indonesia (0.36x), and Nigeria (0.2x), show significantly lower-than-expected adoption rates.

This pattern suggests that, like transformative technologies of the past, the economic benefits of AI may initially concentrate in already-rich regions, raising concerns about the potential to widen global inequality.

The report notes that as adoption matures in a country, usage diversifies beyond coding into a broader range of applications in education, science, and business. Strikingly, mature markets also tend to use AI more collaboratively, while emerging markets are more likely to delegate complete tasks to it, even after controlling for the types of tasks being performed.

Within the United States, the landscape is more nuanced, with local economic conditions heavily influencing usage patterns. While California leads in total usage, it is surprisingly outpaced in per-capita adoption by Washington D.C. (3.82x its population share) and Utah (3.78x).

The data reveals that regional AI use often reflects the local economy’s unique character. For instance, California shows disproportionately high use for IT-related requests, Florida for financial services and business advice, and Washington D.C. for tasks related to document editing, information provision, and career assistance.

This granular view demonstrates that AI is not a monolithic tool but a flexible technology being adapted to solve specific, localized economic challenges.

A Tale of Two Taxonomies: How Users Interact with AI

To move beyond simple usage statistics, both studies introduced novel frameworks to classify user intent, revealing a crucial distinction in how AI creates economic value. I

n its NBER paper, OpenAI categorizes all interactions into three fundamental modes: Asking, where a user seeks information or advice to support a decision; Doing, where a user requests a tangible output like an email, code, or summary; and Expressing, which covers personal reflection and play. The research found that for the general consumer, AI’s primary role is that of an advisor.

Nearly half of all messages (49%) fall into the Asking category, a figure that has grown faster than any other. This highlights AI’s emerging function as a “co-pilot” for human problem-solving, a tool that enhances judgment rather than merely completing tasks.

However, this balance shifts dramatically in a professional context. For work-related queries, the dominant mode becomes Doing, which constitutes 56% of all messages.

According to the OpenAI study, “Writing” is by far the most common work-related activity, accounting for 40% of all professional messages.

This indicates that in a business setting, AI’s most valued feature is its unique ability to generate digital outputs, distinguishing it from traditional information technologies like search engines.

The data shows that most of these writing tasks involve modifying existing text—editing, critiquing, or translating—rather than creating new content from scratch, pointing to a highly collaborative workflow.

Anthropic’s analysis of its enterprise API traffic strongly reinforces this finding, showing that business use is overwhelmingly concentrated in task-oriented applications.

Coding and office/administrative tasks are the most frequent, reflecting their suitability for programmatic automation. This enterprise focus on Doing over Asking aligns with the report’s broader conclusion that businesses are primarily leveraging AI to delegate and automate specific, high-value workflows. T

his systematic deployment is a key channel through which AI is expected to deliver broad productivity gains across the economy.

Interestingly, Anthropic’s report uncovers a counter-intuitive economic dynamic: enterprise customers appear to be largely insensitive to cost. The analysis found a positive correlation between the cost of a task (determined by the amount of input and output tokens) and its usage frequency.

Businesses are prioritizing model capability and the economic value generated by automating a task far more than the marginal cost of the API call itself.

This suggests that for early adopters, the return on investment from deploying AI in critical areas like software development is high enough to make the operational costs a secondary concern.

The report does note, however, that a key bottleneck for more sophisticated AI deployment is access to contextual information, as complex tasks require firms to provide lengthy, well-organized data inputs.

Taken together, both reports paint a cohesive picture of a technology with a dual identity. For the individual consumer, AI’s greatest value lies in its capacity for Asking—providing personalized guidance and decision support that enriches daily life.

For the enterprise, its power is in Doing—automating complex tasks and generating tangible outputs that drive efficiency. This bifurcation is fundamentally reshaping how knowledge work is performed, creating distinct patterns of value creation across both personal and professional spheres.