understanding generative AI and agentic AI

The shared backbone

Most of the time, both are the same core idea:

prompt / context —> LLM —> output

Same engine. Same “brain”. Same ability to summarize, explain, and write.
The difference is what you put around it.

Generative AI

Generative AI is the “answering machine”.

You ask something —> it generates text.
You give it content —> it transforms that content.

In security work that usually looks like:

  • “Is this email phishy?” —> it highlights urgency language and a sketchy URL
  • “Summarize these logs” —> it points out what spikes and what’s weird
  • “Draft a detection idea” —> it gives you a starting point

Useful, fast, and mostly contained. If it’s wrong, it’s usually just wrong text.

Agentic AI

Agentic AI is where the model stops being only a writer and starts behaving like a worker.

Goal —> plan —> do something —> check result —> adjust —> repeat

Same LLM backbone, but now it can:

  • decide the next step without waiting for you
  • call tools, APIs, or searches
  • keep state across steps
  • stop when a success condition is met

That “do something” is the line you feel in real life.

A quick security example

Generative AI assistant
You: “Triage this suspicious email.”
It replies with a summary and some suggested actions.

Agentic AI SOC helper
You: “Triage this end-to-end.”
It pulls headers, extracts URLs, checks reputation, searches for similar messages in your SIEM, drafts a ticket, then asks for approval to quarantine.

Same model. Different system.

Why this matters for security

Generative AI can be tricked into saying dumb things. That’s annoying.

Agentic AI can be tricked into doing dumb things. That’s expensive.

Once tools are involved, prompt injection isn’t just “haha I made it ignore the rules” — it’s:

  • data exfil via tool calls
  • unintended actions with real permissions
  • cross-tenant leakage if boundaries are weak
  • tickets, quarantine actions, workflow side effects

So agentic design needs the boring stuff done well: least privilege identities, allowlists, approval gates, logging of tool calls, and sane defaults.

Takeaways

Generative AI —> produces content and helps you think
Agentic AI —> uses the same LLM backbone, but adds a loop and tools to complete tasks
Agentic systems are powerful, but you have to secure them like automation with privileges

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