Lecture on Agentic AI and Causal Reasoning

Introduction

• Topic: The evolving AI marketplace and the role of causal reasoning in agentic AI systems.
• Speaker: Scott Hebner, Principal Analyst.
• Objective: Understanding developments shaping the future of AI and preparation strategies.

AI Marketplace Dynamics

• Rapid evolution with frequent new developments.
• Importance of preparing for future advancements to avoid lagging.
• The AI revolution is a collaborative effort; requires integrating multiple components.

AI Systems: From Assistants to Agents

• AI Assistants/Chatbots:
  • Task-based.
  • Automate simple tasks via explicit prompts.
  • Widely used (70% of global businesses).
• Agentic AI:
  • Goal-based, handling dynamic conditions.
  • Involves multiple agents collaborating with individual goals and data sets.
  • Requires new mathematical and algorithmic models to handle complexity.

Challenges in Transition

• Current gap between chatbots and AI agents involves adapting to dynamic and complex environments.
• Requires decision-making in dynamic settings, not just pattern recognition.
• Introduction of causal reasoning is essential.

Causal Reasoning in AI

• Importance:
  • Enables decision-making and problem-solving in dynamic environments.
  • Necessary for AI agents to progress beyond task-oriented actions.
• Current Adoption:
  • About 10% of large enterprises currently use AI agents; expected growth to 82% in three years.

Toolkits and Methods

• Causal AI involves multiple steps and degrees, gradually building complexity.
• Causal Reasoning:
  • Critical for understanding cause-and-effect relationships.
  • Helps in decision intelligence, allowing AI to suggest how and why actions should be taken.

Practical Application

• Importance of integrating causal reasoning for real-world business applications.
• Causal AI mimics human reasoning by integrating skills, know-how, and problem-solving.

Future Directions

• Incremental integration of causal reasoning tools with LLMs.
• More organizations infusing causal reasoning into AI systems, including industry leaders (IBM, Meta, Google).
• Expected growth and improved intelligence in AI models.

Conclusion

• Ongoing research and collaboration on agentic AI and causal reasoning.
• Series of papers to cover use cases, technology deep dives, and system architecture.
• Anticipation of agentic AI systems embodying dynamic and integrated ecosystem approaches.