Agentic AI
AI systems that can take autonomous actions to complete tasks, not just respond to prompts.
Agentic AI goes beyond simple question-and-answer interactions. These systems can plan, execute multi-step tasks, and take autonomous actions to achieve goals.
Reactive vs Agentic AI: - Reactive: You ask a question, AI responds, done - Agentic: You set a goal, AI plans steps, executes them, adapts based on results
Characteristics of agentic AI: 1. Goal-oriented: Works toward an objective, not just responding 2. Multi-step: Plans and executes sequences of actions 3. Tool-using: Can interact with external systems (file system, APIs, browsers) 4. Adaptive: Adjusts approach based on results
In coding tools: Agentic AI in tools like Cursor or Windsurf can: - Edit multiple files to implement a feature - Run tests and fix failures - Refactor code across a codebase - Debug by examining logs and adjusting code
The Cascade feature in Windsurf is an example—it can autonomously complete complex coding tasks by taking multiple steps.
Considerations: - Agentic AI needs careful oversight - Actions should be reviewable before execution - More powerful = more potential for mistakes
Examples
- •Windsurf Cascade implementing a feature across multiple files
- •An AI agent that can browse the web to research solutions
- •Automated code review that opens PRs with fixes