Definition
Agentic AI is AI that works toward a goal over several steps, deciding what to do next and using tools such as search, databases and apps.
Agentic AI explained
A standard chatbot answers one message at a time. An agentic system is given a goal, such as “research these ten prospects and draft a tailored email for each”, and works through it: planning steps, calling tools, checking results and adjusting until the task is complete or it needs a person's input.
Business uses that suit agentic AI well tend to be:
- Repetitive but varied, so rigid automation breaks and full manual work is slow.
- Built on information spread across several systems, such as a CRM, inbox, documents and the web.
- Easy to check, so a person can review the output quickly before it goes out.
Examples include lead research and enrichment, first-draft proposals, support triage, content briefs from search data, and reporting that pulls numbers from several tools. Good agentic tools are narrow and well-tested, with clear limits on what they can do without approval, logs of every action, and a person in the loop for anything customer-facing or irreversible.
Example
Every Monday your sales team spends hours researching new inbound leads. An agentic tool reads each new CRM lead, researches the company's website and recent news, scores fit against your criteria and drafts a first reply for a rep to approve. That is agentic AI in practice.
Why it matters
Agentic AI can take hours of multi-step busywork off a team, as long as it is scoped tightly and supervised.
Related service
Agentic AI Tools
Custom AI agents and automations that connect to your data and tools, with a human in the loop.
Custom agentic AI toolsPublished by Vidern, founded and led by Malhar Shah. Updated .
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Related terms
- AI agentAn AI agent is software that uses a language model to choose and take actions, such as searching, reading files or updating systems, to finish a task.
- Tool callingTool calling lets a language model ask software to run a function, such as a search, calculation or database update, and then use the result.
- Human in the loopHuman in the loop is designing an AI system so a person reviews, approves or corrects its work at key points, instead of letting it act fully on its own.
- MCP (Model Context Protocol)MCP (Model Context Protocol) is an open standard for connecting AI applications to external data sources, tools and workflows through a common interface.
- RAG (Retrieval-Augmented Generation)RAG (retrieval-augmented generation) is when an AI system first retrieves relevant documents, then has a language model write its answer from them.