Understanding Agentic AI and Its Impact

Aug 13, 2024

AI Insights and Innovation Podcast: Agentic AI

Host: David Linthicum

  • Author, speaker, AI systems architect, and analyst

Overview

  • Discussion about Agentic AI and its evolution from traditional AI systems.
  • Focus on defining Agentic AI, its applications, target audience, enterprise leverage, and business benefits.

What is Agentic AI?

  • Agentic: Operates autonomously and independently.
  • Similar to past AI systems using architectural patterns like intelligence at the edge and AI-enabled devices.
  • Built on traditional AI agents but with new properties and attributes.

Difference from Traditional AI

  • Traditional LLMs (e.g., ChatGPT) respond based on training data and are static.
  • Agentic AI is more dynamic, capable of tool-calling, gathering up-to-date information, and making adaptive decisions.

Attributes of Agentic AI

  • Built on smaller language models for targeted tasks.
  • Able to integrate with external tools, APIs, and databases.
  • Functions like a mini human, reasoning, refining thinking, and consulting with other agents.

Applications of Agentic AI

  • Autonomous Vehicles: Self-driving cars, drones, and transport systems.
  • Healthcare: Personalized medicine, robotic surgery, patient monitoring.
  • Industrial Automation: Smart manufacturing, predictive maintenance, supply chain optimization.
  • Personal Assistants: Siri, Google Assistant, etc.

Components of Agentic AI

  • Perception and Sensing: Gathering environmental data via sensors and cameras.
  • Information Processing: Algorithms for data analysis and decision-making.
  • Action Execution: Mechanisms for executing actions (robotic actuators, software commands).
  • Learning and Adaptability: Learning from feedback, adapting behavior based on experiences.

Challenges

  • Scalability: Architectural considerations for scaling.
  • Data Integration: Middleware for data source communication.
  • Computational Resources: Needs for real-time decision-making with limited resources.
  • Robustness and Reliability: Complexity and potential fragility.

Who is it for?

  • Product companies building smart devices like thermostats, cars, drones.
  • Enterprises needing tactical AI for supply chain and manufacturing.

Future of Agentic AI

  • Expected growth and excitement in the business world.
  • Seen as a more affordable and useful alternative to creating large LLMs.

Conclusion

  • Agentic AI offers a tactical use of AI through an adaptable architectural pattern.
  • Offers new capabilities for enterprises beyond traditional large LLMs.