Side by side
Seven dimensions decide which one your project needs. The first two settle it for most teams. The rest decide what it costs when you guess wrong.
| Dimension | Generative AI | Agentic AI |
|---|---|---|
| What it produces | A draft: text, code, an answer | An outcome: work finished across several steps |
| Who picks the next step | You, after each output | The system, inside a boundary you set |
| What it asks of you | A clear prompt and a reader | A goal, tools, and an authority boundary |
| What a failure looks like | A bad answer you can see | An action you may not notice until it lands |
| What you review | The output | The record of each decision |
| The cost of being wrong | A revision | An incident |
| Where it pays off | Drafting, summarising, first passes | Work too multi-step or too frequent to babysit |
The test that settles it
The labels blur because vendors benefit from the blur. A copilot with three features bolted on gets marketed as an agent, and a workflow with a model inside gets sold as autonomy. Ignore the label and ask one question: when a step finishes, who picks the next one.
If a person does, you are looking at a generator with a queue. If the system does, inside limits you set, you are looking at an agent. The agent loop either lives with you or inside the system, and everything else on this page follows from where it sits.
A second question separates real agentic work from theatre: where does the record of a decision live. Generative work leaves a chat history. Agentic work needs a decision record, because an action you cannot trace is an action you cannot defend.
When generative AI is enough
Generative AI is the right buy more often than vendors admit. Four conditions make the case.
A person reads every output
If a reviewer was always going to check the draft, the approval loop adds nothing. The generator already has its gate: you.
The work runs in one step
Draft the email, summarise the ticket, write the doc's first pass. One prompt and one answer need no machinery around them.
A wrong answer costs little
When the worst case is a bad sentence you delete, paying for action-level controls is insurance on a pencil.
The daily volume stays low
Ten outputs a day, a person can read them all. The case for acting alone starts when the reading becomes the bottleneck.
When agentic earns the overhead
Agentic AI earns its cost when the work has three properties at once: it runs across several steps, it runs too often for a person to read every step, and the steps are reversible or bounded enough that acting without asking is acceptable. Document processing, triage, monitoring, code review queues. The profile is high frequency plus bounded blast radius.
The overhead is real, and pretending otherwise is how agentic projects fail. A system that acts needs an authority boundary, a record of each decision, and an escalation path for the cases it cannot settle, the human in the loop for everything outside the boundary. Skip those and you have built an incident with a schedule.
Our agentic AI work is scoped around exactly this. The boundary and the record are the build, and the model is the part vendors already sell you.
Where Flytebit sits
We build both. The generative work is drafting, retrieval, and features inside products. The agentic work is systems that act under governed controls, and the two are different builds with different price tags, which is why the wrong answer here is expensive in both directions.
The split shows up in our own products too. PASSR reviews each pull request inside a bounded scope and tracks what it found. TESTR writes tests for code it did not write, then runs them. DOCKR rebuilds documentation on every merge. All three are agentic by the test above: each picks the next step inside limits we set. The drafting and summarising features our generative work produces are the other kind, and a person reviews what comes out.
The feasibility study exists mostly to settle this question. It maps the workflow, counts the volume, and prices the boundary, so the answer comes from your system rather than a vendor's pitch. Where a regulated process is involved, the governance and risk work decides what the agent may touch before anyone builds it.
What to ask before you commit
Five questions that tell a real agent from a generative tool wearing the label. They work on any supplier, including us.
Who reads each output before anything happens with it?
A person approving every step means you are buying a generator with a queue, not an agent. Different price, different risk.
Where is the boundary written down?
In actions, not topics. "It can approve refunds under $500" is a boundary. "It can help with support" is not.
What does it do when it cannot decide?
Escalating to a person is a design choice. Guessing is a defect. Ask which one the system was built to do.
Can you reverse an action after it lands?
And what happens to the work already in flight. A stop that halts without unwinding is a pause, not a control.
What does the record show afterwards?
The verdict, the reason, the input it was reached on. A chat log is not a decision record.
Common questions
Is agentic AI just generative AI with extra steps? ▾
The steps are beside the point. A generative model inside a workflow still waits for you to pick the next action at every stage. An agentic system picks it inside a boundary you set. Extra steps with a human deciding each one is a process, not an agent.
Is ChatGPT an agent? ▾
Mostly not. An assistant waits for instruction and answers. An agent acts between instructions: it plans, calls tools, checks what came back, and continues. Assistants keep gaining tools, which blurs the line, and the test still applies. Who picked the action?
Which one is cheaper? ▾
Generative costs less to run and far less to get wrong. Agentic costs more up front, in design and in the controls around it, and removes a person from a loop that may run thousands of times a day. The maths turns on volume: low volume favours a reader, high volume favours a boundary.
Can a generative tool become agentic later? ▾
The model part can. Add orchestration and tools and it will take steps. The harder part is what surrounds the steps: the authority boundary, the decision record, the escalation path. Retrofitting those is most of the work.
How do we know which one our use case needs? ▾
Count what happens to the output. If a person can read every one and the wrong ones are cheap, stay generative. If the volume makes reading everything impossible, you are already running an agent, because a person who approves without reading is one. You just have not built it yet.
What does picking the wrong one cost? ▾
Buying agentic where generative would do pays for controls and risk you never needed. Staying generative where you needed agentic makes a person the bottleneck, or worse, the rubber stamp. The expensive version is discovering which mistake you made in production.