Cut AI Agent Dev Time by 100X
AI Summary
Summary: Wordware Presentation by CEO Phillip
- Introduction
- Phillip, CEO of Wordware
- Experience: 7 years in LLMs (Language Learning Models)
- Current LLM uses: chatbots, summarization, content generation, knowledge retrieval
- Definition of Agents
- Non-agentic workflows: No planning, tool use, reflection, or collaboration
- Agentic workflows: Involves planning, reflection, tool use, multi-agent collaboration
- Building Agents
- Stages of agent development:
- Handcrafted agents with specific coded steps
- Specialized agents for particular tasks
- General agents like autog GPT or react (not yet in production)
- Challenges:
- Slow iteration speed
- Non-coders cannot participate in iteration cycles
- Wordware’s Approach
- Prompts should not be in the codebase
- Created an IDE focused on AI agent building
- New coding language based on English
- Demonstration
- Showcased IDE capabilities with examples:
- Hello World
- Research and storytelling
- Personal website creation
- Stock price checking
- React agent logic
- Emphasized the importance of iteration and observability
- Deployed applications accessible via API or internally
- Community sharing of LLM “buck” (building blocks)
- Use Cases
- Enterprise customers for knowledge retrieval
- Fun applications like creating a Lego figure from a photo
- Wordware’s Tools and Models
- Offers various tools and models for different tasks
- Recommends Sonar Large by Perplexity for research
- Suggests using Vision models for PDF transcription
- Editor and Evaluation
- WYSIWYG editor for immediate feedback
- Believes in iteration and traceability over ground truth for quality
- Promotion
- Exclusive promo code for 50% discount on Wordware
- Free credits for users, supported by investors like Y Combinator
- Closing
- Wordware aims to be the main IDE for LLM development and application