AI product planning
Agentic AI vs Generative AI: Which Does Your Product Need?
By Haris Ahmed · Code Huddle · Product engineering guides
Generative AI creates content such as text, images, or code. Agentic AI uses models and tools to work toward a goal across steps. The ideas overlap: an agent may use a generative model. The useful product question is whether users need an answer, a predictable workflow, or a system that chooses its next action.
What changes when AI can take actions?
A language model can draft a response without changing anything outside the conversation. Add tools and a decision loop, and the system may retrieve records, choose a next step, and request an update. That changes the product’s permission, approval, and recovery requirements.
Anthropic distinguishes predefined workflows from agents that dynamically direct their own tool use. “Agentic” terminology varies across vendors, so describe the actual behavior in your requirements rather than relying on the label.
| Product question | Generative feature | Agentic feature |
|---|---|---|
| What does it produce? | Content or a prediction presented to a user | A task outcome using selected tools and intermediate results |
| Who chooses the next step? | The user or predefined application logic | The model chooses within configured limits |
| What must you verify? | Output quality, sources, latency, and cost | Those checks plus tool actions, permissions, and recovery |
Example: answering a support request versus resolving it
Consider a customer asking to change a delivery address. A generative feature can draft a helpful reply from the support policy. A fixed workflow can check the order state and show an address-change form. An agent could inspect the request, select the order lookup tool, and prepare the appropriate update.
The agent still needs a clear boundary. Require the customer or support operator to confirm the address before a write, and handle orders that have already shipped. This example describes a possible design, not a delivered client result.
Choose the smallest useful implementation
Start with the user’s task. If an answer is sufficient, a retrieval-backed response may be enough. If the steps are known, application logic can coordinate them. Consider an agent when the next step depends on information discovered during execution and a bounded set of tools can complete the task.
- Write three realistic requests and their acceptable outcomes.
- List the systems and permissions each request needs.
- Separate read operations from actions that change something.
- Define when to ask a person, stop, retry, or escalate.
Evaluate the whole task, including unsuccessful runs
Measure whether the intended task was completed correctly, not just whether the final response sounded plausible. Include missing records, unavailable APIs, ambiguous instructions, and duplicate requests in the evaluation.
Track the cost and latency of a complete run, including retries and tool calls. Release first to an agreed audience with execution limits, reviewable traces, and a way to pause the workflow. The AI agent service page explains how we scope that work.