AI that doesn't just think — it acts. Agentic AI plans multi-step tasks, uses tools, and delivers real-world results without being told exactly how.
Agentic AI means AI systems that can plan, decide, use tools, and take action autonomously. Regular AI answers your questions; agentic AI completes your projects. It's the difference between a calculator and an accountant.
Input → Output
Prompt → Content
Goal → Result
Step through the process to see agentic AI in action.
"Build me a landing page for my consulting business with a contact form, testimonials section, and SEO metadata."
The LLM (Claude, GPT-4) that provides reasoning and language understanding. The "brain" of the agent.
APIs, file systems, browsers, terminals — the agent's "hands" for interacting with the world.
Short-term (conversation context) and long-term (persistent knowledge) storage that maintains state across tasks.
The ability to break complex goals into steps, sequence them, and adapt when things go wrong.
Observe results, evaluate success, and iterate. Agents that can self-correct are dramatically more effective.
Boundaries, permissions, and oversight mechanisms that keep the agent safe and aligned with human intent.
Don't experiment in a vacuum. Pick a real project — a website, a script, an analysis — and use an agentic tool to build it.
The quality of agent output depends on the quality of your instructions. Be specific about what you want, not how to build it.
Type checking, tests, and builds should run automatically. This is your safety net — agents must pass before shipping.
Don't blindly trust agent output. Read the code, test the product, verify the logic. Your judgment is the final quality gate.
Agentic means the AI can act with agency — making decisions, planning steps, and taking actions autonomously rather than just responding to prompts. It's the difference between answering a question and completing a project.
Generative AI creates content (text, images, code) in response to prompts. Agentic AI wraps generative models with planning, tool use, and autonomy — so they can execute multi-step tasks independently.
No. AGI (Artificial General Intelligence) is a theoretical AI that matches human intelligence across all domains. Agentic AI is practical, available today, and focused on executing specific tasks autonomously within defined boundaries.
Examples include AI coding agents (Claude Code, Devin), research agents that gather and synthesise information, customer service agents that resolve tickets end-to-end, and multi-agent teams that build software.
With proper design — human oversight, sandboxed execution, clear boundaries, and quality gates — agentic AI is safe and already in production use. The key principle is keeping humans in the loop for critical decisions.
Ship your next project in a 2-week agentic development sprint. Real results, not experiments.