Emergent Capabilities in General Purpose and Coding Agents

Including 10,000+ Words Output Generation, Web Search and Online Actions and No-Code Multi-Agent Development

AI Agents are exciting, as they combine multiple AI models with software tools to take over more complex tasks and increase our productivity and software development abilities. They are more than wrappers around single LLMs that need to complete all of the tasks required for the application. They contain various specialized models that are trained for advanced reasoning and given access to software tools, such as web browsers, search engines and calculators.

In the past month there have been numerous papers published on Agents and Multi-Agents that expand their capabilities in a variety of directions and use cases. This is a collection of six papers on General Purpose and Coding Agents that describe new frameworks, methods and tools of building compound AI systems with advanced capabilities.

The key optimization across these advances is the breakdown of the main task into multiple sub-tasks, which is enabled through advanced reasoning by a model responsible for overall planning, and the delegation of each of them to different specialized models that exist within the agentic system.

They AI Agent emergent capabilities in this collection refer to:

• AgentWrite - Overcoming the common 2,000+ word LLM output limit to 10,000+ words

• The AI Scientist - A framework to run science experiments, generate research papers and simulate their evaluation

• Agent Q - A method to guide agents to perform web searches and act on websites

• AutoGen Studio - A No-Code Multi-Agent development tool

• PersonaGym - A dynamic evaluation framework for assessing LLM systems based on their assisgned role

• Advances of Agent-based coding assistants compared to LLM-based ones in the areas of:

  • Requirements engineering

  • Code generation

  • System design and evaluation

  • Test generation

  • Software safety and maintenance