What is Agentic AI?
Agentic AI is the shift from language models that reply to systems that act. Instead of one prompt in, one answer out, an agent plans, calls tools, observes the result, and loops until a goal is reached.
From chatbot to agent
- Chatbot: input → model → output.
- Workflow with LLM: predefined steps, model fills in blanks.
- Agentic system: the model chooses the next step from a set of tools, using memory of what it has already done.
Anthropic's "Building effective agents" draws the same line: use workflows when the path is known, agents when the model needs to decide.
Why the term matters now
Search interest in agentic AI has surged past general AI agent queries. It captures three practical changes builders care about:
- Tool use is native. Frameworks like the OpenAI Agents SDK and LangGraph treat tools as first-class citizens.
- Multi-step reasoning is expected. Long-horizon tasks (research, coding, ops) require planning, not one-shot answers.
- Evaluation is different. Traditional NLP metrics don't measure whether an agent finished the job.
Core ingredients
- A model with tool calling (Claude, GPT-5, Gemini, open-weights variants).
- Tools: web search, code execution, retrieval, or your own API.
- Memory: short-term scratchpad plus long-term store (Mem0).
- A loop: think → act → observe → think, with a stopping condition.
- Guardrails: input/output validation and rate/scope limits.
When to reach for it
Agentic workflows shine when:
- The task has multiple steps that aren't fully knowable up front.
- Tools exist that can shortcut expensive reasoning.
- Success is measurable (a file compiles, a ticket is resolved, a report is written).
For linear, deterministic work, a plain workflow with a model call is usually cheaper and more reliable.
Where to go next
- Learn the pieces: Tool use primer, Memory in agents, Evaluating agents.
- See it in practice: browse the Playbooks for end-to-end examples.
- Pick a stack: compare Tools like CrewAI, LangGraph, and the OpenAI Agents SDK.