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Agentic AI

AI systems that autonomously pursue goals using tool calls and multi-step reasoning, distinct from generative AI (which produces content) or predictive AI.

Quick answer

What is Agentic AI?

Agentic AI describes systems that pursue a goal by taking actions rather than only producing text. An agent plans a sequence of steps, calls tools or APIs, checks the result and adapts. In Salesforce that means AI which updates records and completes a task, not AI which only drafts a suggestion.

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Agentic AI is the umbrella term for AI that does rather than just answers. While generative AI produces text, images, or code in response to a prompt, agentic AI takes a goal and executes a sequence of actions to achieve it, calling APIs, querying databases, making decisions, and updating systems.

The move from generative to agentic systems is the main direction of travel in enterprise AI. Salesforce Agentforce, Microsoft Copilot agents and open frameworks such as LangGraph all sit in this category. The technical building blocks are consistent: a language model for reasoning, tool-calling or function-calling APIs for action, and an orchestration layer that plans and sequences steps.

For Salesforce teams: agentic AI is what makes "the AI that closes the ticket" possible, vs. "the AI that drafts a reply for a human to send." The trade-off is governance, autonomous action requires stricter guardrails, audit trails, and PII masking than generative use cases.

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Frequently asked

Agentic AI, common questions

  • What is the difference between agentic AI and generative AI?
    Generative AI produces content in response to a prompt: a summary, an email draft, a block of code. Agentic AI takes a goal and works towards it, calling tools, reading results and deciding the next step. The practical distinction is that generative AI hands you a draft, while an agent completes the task and changes system state.
  • What is an AI agent made of?
    Three parts in most designs. A language model does the reasoning and decides what to do next. A set of tools, usually APIs or functions, gives it the ability to act. An orchestration layer holds the goal, tracks what has been done, feeds results back to the model and decides when the task is finished or has failed.
  • How does agentic AI work in Salesforce?
    An agent is given a goal such as resolving a case or preparing an account brief, plus a defined set of actions it may take on specific objects. It reads the records the running user can see, calls the actions it needs, and writes results back. GPTfy scopes each action explicitly and records every prompt and response.
  • What are the risks of giving AI autonomy?
    An agent that can write to records can write the wrong thing at scale, and it reads untrusted content such as inbound email and web-to-lead fields, which creates exposure to prompt injection. The usual answer is least privilege: narrow the objects and operations each agent can touch, mask sensitive fields, require confirmation for irreversible actions, and keep an audit record.
  • Do you need Agentforce to run agents in Salesforce?
    No. Agentforce is one route. An overlay platform such as GPTfy runs agents inside the same org with no Data Cloud requirement, using the model you license and the permissions your users already have. Which route fits depends on whether you want model choice and a fixed per-user cost or a single-vendor stack.

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