Super Agent: An Agent That Builds Its Own Tools

Key Concepts:

• Deep Research Agent: An open-source agent capable of web search, browser control, and using MCP tools for document creation and more.
• Agent Orchestra: A hierarchical multi-agent framework for general-purpose task solving, the foundation of the Deep Research Agent.
• Skywork AI: The company behind the Deep Research Agent and other open-source AI projects, also sponsors of the video.
• MCP (Multi-Capability Protocol) Manager Agent: Manages MCP tools, enabling dynamic tool discovery, registration, and execution.
• Deep Analyzer Agent: Performs in-depth analysis of input information, extracts key insights, and supports various data types.
• Deep Researcher: Conducts thorough research, retrieves and synthesizes information, and generates reports.
• Browser Use Agent: Automates web browsing, information extraction, and data collection.
• Skywork Super Agent: A platform for document creation, data analysis, website development, and more, built on the Deep Agent architecture.
• GAIA Benchmark: A benchmark used to evaluate the performance of the agent.

1. Introduction to Deep Research Agent

• The video introduces the Deep Research Agent, an open-source project that can perform web searches, control a browser, and utilize MCP tools to create documents and PDF files.
• The agent is based on the "Agent Orchestra" paper, a hierarchical multi-agent framework for general-purpose task solving.
• Skywork AI, the company behind the project and the video's sponsor, is highlighted for its open-source contributions, including a 3D world generated from a single image and an open-source world model for a matrix game.

2. Skywork AI's Super Agent Platform

• Skywork AI is developing a platform called Skywork Super Agent, which allows users to create documents, slides, analyze Excel sheets, develop web pages, and create podcasts.
• This platform leverages the capabilities of the Deep Research Agent and other Skywork AI technologies.

3. MCP Manager Agent and Dynamic Tool Creation

• The video emphasizes the importance of the MCP Manager Agent in the Deep Research Agent framework.
• The system aims for general-purpose task solving, requiring the ability to create new tools on demand.
• The agent analyzes user intent, assesses available tools, and synthesizes new tools if needed.
• A multi-stage evaluation protocol validates the tool's functionality before registering it in the MCP server for future use.
• The concept of building and discarding tools on demand is presented as a key aspect of advanced agent systems.

4. System Architecture and Agent Roles

• The core of the system is the planning agent, which coordinates several specialized agents.
• Deep Analyzer Agent: Performs in-depth analysis of input information, extracts key insights, and supports various data types. It also has a Python code interpreter.
• Deep Researcher: Conducts thorough research, retrieves and synthesizes high-quality information, and generates research reports. Similar to research capabilities in ChatGPT, Gemini, or Claude.
• Browser Use Agent: Automates web browsing, information extraction, and data collection. It can be used with the Deep Researcher for up-to-date information.
• MCP Manager Agent: Manages MCP tools and services, enabling dynamic tool discovery, registration, and execution through the MCP protocol. Supports both local and remote MCP tool integration.

5. Operational Flow and Error Handling

• The system interprets user objectives using the Deep Analyzer Agent and decomposes them into subtasks.
• Specialized sub-agents are selected for each subtask, utilizing available tools or creating new ones.
• The system collects errors during operation and updates its plan and objectives accordingly.
• This multi-agent system can perform web searches, synthesize reports, and take actions using available tools and agents.

6. Model Flexibility and Open-Source Nature

• The Deep Research Agent utilizes multiple models from different providers, including Google, BAAI, open-weight models like Qwen, and OpenAI models.
• This flexibility allows the system to leverage the strengths of different models.
• The code is available on GitHub, allowing users to run the agent in their local environment.
• The agent orchestrator achieves top performance on the GAIA benchmark and near state-of-the-art results on the HellaSwag exam benchmark.

7. Performance and Benchmarking

• The video highlights the agent's performance on the GAIA benchmark, noting that dynamic tool creation through MCP improves performance.
• The agent also achieves competitive results on the HellaSwag exam benchmark.

8. Skywork Super Agent Capabilities

• Skywork Super Agent is presented as a competitor to platforms like Manace or Genpark.
• Document Creation: The agent can collect user input, perform web searches, and create high-quality documents, using MCP tools for web search and browsing.
• Data Analysis: The agent can analyze data from the web or user-provided Excel sheets, create plots, and perform sales analysis. It uses a Python code interpreter for analysis.
• Website Development: The agent can build websites based on user requirements, collecting data from different websites and rendering them into functional web pages.
• Video Analysis: The agent can analyze YouTube videos and create tutorials or mind maps based on the content.

9. API and Community Engagement

• Skywork AI has opened up its API, allowing users to access the agent's capabilities programmatically.
• The API offers document creation and other functionalities.
• Skywork AI provides free credits for testing the API and offers different packages based on user needs.
• The video praises Skywork AI's approach of building on open-source work and sharing research with the community.

10. Conclusion

• The Deep Research Agent and Skywork Super Agent represent a significant advancement in AI-powered task automation.
• The ability to dynamically create tools, leverage multiple models, and integrate various functionalities makes this system a powerful tool for research, document creation, data analysis, and more.
• Skywork AI's commitment to open-source development and community engagement is highlighted as a positive aspect of the project.