Real AI Agents Need Planning, Not Just Prompting - Yuval Belfer

Introduction to AI Models 00:00

• OpenAI's instruct GPT, released in 2022, aimed to follow instructions effectively but still struggles with instruction adherence by 2025.
• The evolution of prompts has led to increasingly complex queries, showcasing limitations in simple instruction-following tasks.

The Role of AI Agents 01:19

• AI agents go beyond prompting; they require planning to solve complex tasks effectively.
• Definitions of agents vary, but functionality is the primary focus, whether termed as agents, workflows, or something else.

Planning in AI 03:16

• Planning involves determining the steps required to achieve a goal and is essential for complex tasks needing parallelization and explainability.
• Different types of planners exist, including forms-based planners and dynamic planners, which allow for replanning and adaptability.

Execution Engines 04:17

• Execution engines enhance efficiency by analyzing dependencies between steps, enabling parallel execution, and balancing speed with cost.
• Smart execution is critical for optimizing the planning process.

AI21 Mastro System Overview 04:43

• AI21 Mastro incorporates both planning and execution engines to streamline instruction following.
• The system separates context, tasks, and requirements for easier validation and employs execution trees to optimize candidate selection.

Results and Effectiveness 06:29

• AI21 Mastro shows improved results compared to traditional LLM calls by using a combination of planning and smart execution.
• Higher quality outputs are achieved despite increased runtime and costs, demonstrating the advantages of this approach.

Conclusion and Recommendations 07:30

• LLMs alone are insufficient for complex tasks; starting with simpler models or tools is advisable before progressing to planning and execution engines for more challenging requirements.
• Users are encouraged to explore AI21 Mastro and join the waitlist for further insights.