Quick answer: Generative AI creates content such as text, code, images or audio from an input. Agentic AI uses models inside a goal-directed loop that can plan, call tools, observe results and take actions. Many AI agents use generative models, but adding tools and autonomy changes the architecture, security boundary and evaluation requirements.
The terms describe different layers. “Generative” describes a model capability. “Agentic” describes how an application organizes models, tools, state and control to complete work. A system can be both: an agent may generate an email, inspect a CRM, request approval and then send it.
Comparison at a glance
| Dimension | Generative AI application | Agentic AI system |
|---|---|---|
| Primary job | Produce content or a response | Pursue a goal and complete steps |
| Typical flow | Input → generation → output | Goal → decision → tool → observation → repeat |
| External actions | None or application-controlled | Often selects and requests actions dynamically |
| State | Prompt and conversation context | Task state, tool results, memory and checkpoints |
| Stopping rule | One response completes the call | Goal, limit, approval, failure or policy boundary |
| Main risk | Incorrect or harmful content | Incorrect content plus incorrect real-world action |
| Evaluation | Output quality and safety | Outcome, trajectory, tools, cost, safety and recovery |
Google Cloud describes agentic AI as AI focused on autonomous decision-making and action, while NIST describes generative AI as creating new content based on prompts and learned patterns. These are compatible, not mutually exclusive definitions.
What generative AI does
Generative models estimate and produce content based on input and context. Common applications include:
- drafting and rewriting;
- summarization;
- translation;
- code generation;
- image, audio and video creation;
- structured extraction;
- question answering.
The application may still use retrieval or deterministic code. A document-answering system can retrieve passages and generate an answer without becoming an agent if it follows one fixed path.
What makes a system agentic
An agentic system normally has three properties:
- A goal rather than only a prompt. The system is asked to achieve an outcome.
- A choice among actions. The model can decide which tool or step is appropriate.
- A feedback loop. Tool results affect later decisions until the task stops.
Read what is an AI agent and how AI agents work for the full loop.
Example: support email
Generative AI version
A support representative pastes a ticket into a model. The model drafts a response. The representative checks and sends it.
Agentic AI version
The system reads the ticket, retrieves approved documentation, checks account status, identifies a billing issue, proposes a credit, pauses for approval, applies the credit through a tool and sends a verified response.
The second system can deliver more work. It also needs identity, authorization, audit, idempotency, escalation and rollback. The architecture—not the prose quality—is the main change.
When generative AI is enough
Prefer a direct generative workflow when:
- the desired output is content;
- one or a few deterministic steps are known;
- a human will review the result;
- external actions are unnecessary;
- latency and cost should remain predictable;
- the task can be validated at the output boundary.
Examples include summarizing an interview, extracting invoice fields into a schema or generating headline alternatives.
When agentic AI is justified
Consider an agent when:
- the correct sequence changes by case;
- multiple systems must be consulted;
- intermediate results determine the next action;
- users can define goals that do not map to one workflow;
- success can be observed and bounded;
- human review can cover consequential decisions.
Products such as Pushable AI and 99helpers represent different agent-oriented product directions listed on IndieTools. Their presence does not mean every workflow needs autonomy; evaluate the actual permissions and operating model.
Architecture differences
A simple generative application may contain a prompt template, model call and output parser. An agentic application commonly adds:
- tool registry and schemas;
- execution runtime;
- state and memory;
- approval policy;
- guardrails;
- retry and idempotency logic;
- trace storage;
- cost and time budgets;
- evaluation datasets;
- human escalation.
Model Context Protocol can standardize connections between AI applications and tools or data. It does not automatically turn a generative application into an agent. Read what is MCP and AI agents with MCP.
Security changes when AI can act
A generated error can mislead a reader. An agentic error can change a record, send data or trigger payment. Apply:
- least-privilege identity per user or workload;
- explicit approval for consequential operations;
- typed and validated tool arguments;
- separation between untrusted content and trusted instructions;
- network and data boundaries;
- complete audit of external actions;
- safe retry and compensation paths;
- hard budgets and stop controls.
OWASP's agentic guidance identifies distinct risks around goals, tools, memory, identity and inter-agent trust. Use the AI agent security checklist before enabling write tools.
Evaluation differences
For generative AI, teams often score correctness, relevance, style, safety and structured-output validity. For an agent, also score:
- whether the final goal was achieved;
- tool selection and argument accuracy;
- unnecessary steps and cost;
- policy and approval compliance;
- recovery after tool failure;
- state and memory correctness;
- side effects in the external system.
A polished final answer can hide a bad trajectory. The AI agent evaluation guide explains trace- and outcome-level testing.
A practical decision test
Ask these questions in order:
- Can one model call produce a reviewable result?
- Can deterministic application code handle the remaining steps?
- Is model-selected tool use necessary because the path varies?
- Can every action be authorized and observed?
- Is the improvement worth added latency, cost and risk?
Start with the simplest architecture that works. Anthropic's engineering guidance similarly recommends simple composable patterns before adding complex agent frameworks.
Frequently asked questions
Is every generative AI application an agent?
No. A one-turn generator, extractor or summarizer is not necessarily an agent. Agentic systems add goal-directed action and feedback loops.
Does agentic AI require an LLM?
No. Agentic systems predate modern LLMs. Current products often use language models because they can interpret natural-language goals and choose among tools.
Is agentic AI better than generative AI?
It is more capable for some variable, multi-step tasks and unnecessarily complex for many fixed workflows. “Better” depends on the job and risk.
Can generative AI use RAG without being agentic?
Yes. A fixed retrieve-then-generate pipeline is a generative application. It becomes more agentic when the system dynamically chooses searches, tools and subsequent actions.
Pushable AI
99helpers
Chatgrow
Lynara
OpenWeave