Council: A Framework for Developing Generative AI Applications
Summary
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Daniel Kur provides an overview of the open source project called Council, a framework for developing generative AI applications using collaborative agents. He discusses the key concepts of control flow and evaluation, as well as the importance of constraints and integration with existing tools. Kur also addresses audience questions regarding parallel execution, hallucination, and the role of the controller. Overall, Council offers advanced control and predictability for creating reliable and accurate generative AI applications.
Topics:
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Control Flow and Evaluation
Control flow determines how user messages are routed to different parts of the software
Evaluation component assesses the success and relevance of agent responses
Control flow and evaluation enable the creation of sophisticated agents
Constraints and Integration
Council prioritizes constraints such as budget and time limitations
Supports multi-threading and parallel execution for improved efficiency
Integrates with existing tools and ecosystems
Components of Council
Controller handles control flow and selects chains of skills to execute
Chains are sequences of skills that perform specific tasks
Evaluator assesses the results of executed chains and provides the final response
State management facilitates development by providing access to conversational history and previous chain iterations
Parallel Execution and Agent-to-Agent Work Allocation
Currently, chains are executed sequentially but skills within chains can be executed in parallel
Future work aims to enable parallel execution of chains and agent-to-agent work allocation
Addressing Hallucination
Council provides mechanisms to address hallucination through the evaluator
Fact tracking and real-time information retrieval skills help avoid providing inaccurate responses