What is agentic AI?

Imagine a banking AI agent that doesn't just answer your questions but actively helps you manage your finances. It could check your account balance, suggest the best way to pay a bill, and, with your approval, complete the transaction—all within a single conversation.

Agentic AI follows four main steps:

• Gather information: The AI collects data from various sources to understand the situation.
• Think and plan: A language model acts as the brain, figuring out what to do and creating a step-by-step plan.
• Take action: The AI connects to other software through APIs to complete tasks, with safety limits in place.
• Learn and Improve: The system gets better over time by learning from each interaction.

Each type of AI serves different purposes:

Generative AI creates content (text, images, audio) based on patterns learned from training data. By mistake, ChatGPT is sometimes called agentic AI, but it's actually a generative AI tool that produces content rather than taking independent actions.

Agentic AI goes beyond content generation, deploying AI agents that plan, decide, and execute actions across digital systems to achieve specific goals. These AI systems operate autonomously, adapting strategies and making independent decisions.

Physical AI embodies intelligence in robots and devices that interact with the material world through physical sensors and actuators. This includes self-driving cars, robotic assistants, and AI-powered industrial automation.

Think of agentic AI as a new colleague joining your team—one who doesn't just wait for instructions but takes initiative, anticipates needs, and pursues goals independently. This evolution from passive tools to proactive partners brings both exciting possibilities and serious concerns:

• True digital assistants: Anticipates needs rather than just responding
• Tackles complexity: Breaks down difficult problems into manageable steps
• Adapts in real-time: Evolves strategies as circumstances change
• Multiplies capabilities: Handles multiple tasks simultaneously
• 24/7 availability: Maintains peak performance without fatigue
• Cross-domain expertise: Develops knowledge across multiple fields
• Enhanced efficiency: Automates complex tasks, streamlines workflows
• Superior data processing: Analyzes vast amounts of data faster than humans
• Precise decision-making: Identifies patterns and predicts outcomes accurately
• Scalability: Expands capabilities without proportional increase in resources
• Error reduction: Minimizes human mistakes in critical operations
• Cost reduction: Decreases expenses on salaries, training, and operations
• Personalized experiences: Tailors interactions based on user behavior
• Task automation: Handles repetitive work, freeing humans for creative roles
• Competitive advantage: Enables quick, data-driven business decisions
• Accelerated innovation: Speeds up product development and testing

• Job displacement: May make certain roles redundant
• Bias risks: Inherits and can amplify biases from training data
• High costs: Substantial investment in infrastructure and integration
• Dependency issues: Organizations may lose human expertise
• Emotional limitations: Lacks genuine empathy for sensitive interactions
• Transparency problems: "Black box" decision-making raises accountability concerns
• Security vulnerabilities: Attractive targets for cyberattacks
• Unintended consequences: May overlook ethical considerations
• Integration challenges: Often incompatible with existing systems
• Regulatory complexity: Faces evolving legal and compliance requirements

Here are prominent examples of agentic AI and their capabilities:

• Copilot for Microsoft 365 — Proactively drafts emails, creates documents, and summarizes meetings without explicit prompting.
• Harvey (Legal AI) — Independently researches case law, drafts legal documents, and suggests argumentation strategies.
• Autonomous vehicles (Tesla FSD) — Makes real-time driving decisions, navigates complex environments, and adapts to changing conditions.
• AI research assistants (Elicit) — Formulates research questions, gathers relevant papers, and synthesizes findings automatically.
• Trading algorithms (Renaissance Technologies) — Analyzes market conditions, identifies patterns, and executes trades without human intervention.
• Healthcare diagnostic systems (Google DeepMind) — Monitors patient data, suggests diagnoses, and recommends treatment plans proactively.