Building Agents - Copilot Streaming an Agentic Workflow w/ Fast Inference (Llama 3, Groq, LangGraph)
AI Summary
- Agent Prototype Demo Summary
- New Agent Features:
- Streams data from a complex workflow in real-time.
- Enhances user experience beyond the chat interface for collaboration.
- Explores the capabilities of agent workflows with fast inference speeds using Llama 3 and Grok.
- Purpose of the Agent:
- Designed for product teams without a PM.
- Aids in brainstorming and refining product vision and ideation.
- Workflow Process:
- Extracts core ideas from user input (business objectives, customer problems, etc.).
- Passes information to a vision writing agent that drafts, edits, and refines a product vision statement.
- Generates follow-up questions to improve the vision statement.
- Technological Backbone:
- Utilizes LangGraph for coordinating workflow steps.
- Built entirely on Llama 3, a 70 billion parameter model from Meta.
- Employs Grok for fast inference, essential for real-time collaboration.
- Advantages of Current Setup:
- Fast enough to support interactive, iterative conversations.
- Cost-effective compared to alternatives like GPT-4.
- Quality of output is competitive with top-tier models.
- Future of AI Tools:
- Anticipates a trend towards faster, cheaper inference.
- Believes in the continued need for specialized AI tools for specific tasks.
- Predicts that open-source models will rival proprietary models like GPT-3.5 or Claude Hau.
- Cost and Speed Considerations:
- Grok offers the cheapest and fastest service currently.
- Costs are expected to decrease while speeds increase.
- Open-source models are becoming sufficiently advanced to replace more expensive options.
- Conclusion:
- The agent demonstrates the feasibility of real-time, collaborative AI workflows.
- The future looks promising for AI tools with rapid advancements in speed, cost, and quality.
- Specialized AI tools will remain relevant for guiding users through specific processes.