About AI agents
It seems like you’ve provided a transcript of a video about AI agents, specifically discussing their planning capabilities, user experience, memory, and flow engineering. Here are the three main areas you mentioned:
- Planning: The speaker talks about how language models aren’t yet good enough to plan multiple steps implicitly when running in a for loop. To overcome this, developers use external prompting strategies like planning steps explicitly upfront or reflection steps at the end. They mention that future models might have these systems built-in, eliminating the need for explicit prompting.
- User Experience: The speaker discusses how user interfaces will become nicer and more intuitive, referencing Devon, a proposed Alpha Codium flow, which is a new workflow to improve coding results. This new agentic workflow delivers better results and has a different feel to previous workflows.
- Memory: The speaker highlights the importance of memory in AI agents, allowing them to retain information and build upon it. They mention that future models will have this built-in, enabling more efficient and effective planning.
Additionally, you mentioned:
- The idea of flow engineering, which is crucial for designing efficient AI workflows.
- The Alpha Codium paper achieving state-of-the-art coding performance through better flow engineering rather than just better models or prompting strategies.
- The concept of offloading the planning of what to do to human engineers and relying on them as a crutch.
If you’d like me to clarify any specific points or provide more information, please let me know!