If you are a Product Manager working with AI, you will quickly encounter terms such as LLM, AI workflow, RAG, ReAct, tools, reasoning, and AI agents.
At first, they can sound highly technical. But the underlying concepts are easier to understand if you look at them from a product perspective.
A useful way to learn is to think about AI systems in three levels:
- Large Language Models (LLMs) — AI responds to an input.
- AI Workflows — AI follows a predefined process and uses external tools.
- AI Agents — AI receives a goal, reasons about how to achieve it, uses tools, observes results, and iterates.
For an AI Product Manager, understanding this progression is more important than memorizing technical terminology.
1. Level One: Large Language Models
A Large Language Model (LLM) is the foundation behind many modern AI applications.
Examples include the models used by products such as ChatGPT, Claude, and Gemini.
At the simplest level:
Human → Prompt → LLM → Response
For example:
“Write a professional email asking a customer for a meeting.”
The LLM receives your prompt and generates an email.
This is useful, but the model has two important limitations.
Limitation 1: It may not know private or real-time information
Suppose you ask:
“When is my next customer meeting?”
A standalone LLM cannot know your calendar unless the application gives it access to that information.
The model may know general facts from its training data, but your private calendar, internal CRM, order database, or company documents are different.
Limitation 2: An LLM is fundamentally reactive
An LLM generally waits for an input before producing an output.
It does not independently decide:
- What information should I retrieve?
- Which application should I use?
- What should I do next?
- Is the answer good enough?
- Should I try another approach?
Those capabilities require additional system design.
This distinction is important for Product Managers because an LLM is a model, while an AI product is a system built around one or more models.
2. Level Two: AI Workflows
Now imagine connecting an LLM to external tools.
Suppose you build a workflow that says:
- Receive a user’s question.
- Check whether it is about a personal event.
- Search Google Calendar.
- Retrieve the relevant event.
- Give the information to the LLM.
- Generate the response.
Now the system can answer:
“When is my next product review meeting?”
because the workflow explicitly tells it to retrieve calendar information.
This is an AI workflow.
The key characteristic: predefined control logic
An AI workflow follows a path designed by humans.
For example:
Input → Calendar → LLM → Output
You could make it more sophisticated:
Input → Calendar → Weather API → LLM → Text-to-Speech → Output
Even if the workflow contains dozens or thousands of steps, it can still be an AI workflow if humans determine the decision path.
The important question is:
Who decides what happens next?
If the answer is the predefined program or workflow logic, you are dealing with a workflow rather than a fully autonomous AI agent.
A Practical AI Workflow Example
Imagine a marketing workflow that automatically creates social media content from industry news.
The workflow could be:
Step 1: Collect article URLs from Google Sheets.
Step 2: Send the articles to an AI search/summarization tool.
Step 3: Send the summaries to an LLM.
Step 4: Generate LinkedIn and Instagram posts.
Step 5: Schedule the posts.
Step 6: Repeat every morning.
This can save a Product Manager or marketer significant manual effort.
But notice that the human has already defined the sequence:
Collect → Summarize → Write → Schedule.
If the LinkedIn post is not good enough, a human might need to change the prompt and run the workflow again.
That human-driven iteration is another clue that the system is a workflow.
3. Where Does RAG Fit?
You will frequently hear the term RAG, or Retrieval-Augmented Generation, when discussing AI products.
The basic idea is simple:
Retrieve relevant information before generating an answer.
For example, an internal HR assistant could:
- Receive a question.
- Search company policy documents.
- Retrieve relevant passages.
- Give those passages to an LLM.
- Generate an answer grounded in the retrieved information.
Conceptually:
User Question → Retrieve Information → LLM → Answer
RAG is therefore better understood as a pattern or technique for giving an AI system access to external knowledge.
It does not automatically make a system an AI agent.
A RAG system can be a straightforward workflow where the retrieval process is predetermined.
4. Level Three: AI Agents
Now we make one major change.
Instead of a human defining every step, the LLM becomes part of the decision-making loop.
Suppose the goal is:
“Create today’s LinkedIn post about the most important AI product news.”
A workflow might have a fixed sequence.
An AI agent could instead reason about the task.
It might determine:
- Which sources should I search?
- Which news stories are relevant?
- Which stories are credible?
- Do I need additional information?
- What format should the post use?
- How should I evaluate the draft?
- Is the output good enough?
- If not, what should I change?
The system is no longer simply following one fixed path.
It is operating toward a goal.
5. The Three Core Capabilities of an AI Agent
For Product Managers, a useful mental model is:
Reason → Act → Observe → Iterate
1. Reason
The agent determines what it needs to do.
For example:
“I need current information, so I should search the web rather than rely only on my existing knowledge.”
2. Act
The agent uses tools.
Tools could include:
- Search APIs
- Databases
- CRMs
- Calendars
- Payment systems
- Code execution
- Internal knowledge bases
- Browser automation
3. Observe
The agent examines what happened after taking an action.
For example:
“The search returned five articles, but two are outdated. I need more recent sources.”
4. Iterate
The agent changes its approach based on the result.
For example:
“The first draft does not satisfy the required tone. I should revise it.”
This creates a loop:
Goal → Reason → Act → Observe → Reason → Act → Final Output
That loop is one of the most important concepts for understanding AI agents.
6. What Is the ReAct Framework?
You may also encounter ReAct, short for Reason + Act.
The idea is that an AI system can alternate between reasoning about a task and taking actions through tools.
A simplified version looks like:
Reason → Action → Observation → Reason → Action → Observation
For example, an AI travel assistant receives:
“Plan a three-day business trip to Bengaluru.”
The agent could reason:
“I need the user’s dates and location.”
Then it might:
- Check flight information.
- Search hotels.
- Check availability.
- Compare options against the user’s constraints.
- Build an itinerary.
- Ask for clarification if critical information is missing.
The important product concept is not the acronym itself.
It is the closed-loop interaction between decision-making and tool usage.
7. Workflow vs AI Agent: The Product Manager’s View
| Dimension | AI Workflow | AI Agent |
|---|---|---|
| Primary objective | Execute a defined process | Achieve a goal |
| Decision-making | Mostly predefined | Partially delegated to the AI |
| Control logic | Human-designed | AI can select next actions |
| Tool usage | Predetermined | Dynamically selected |
| Iteration | Usually predefined | Can adapt based on results |
| Best suited for | Repeatable processes | Dynamic, multi-step problems |
| Predictability | Generally higher | Can be less predictable |
| Evaluation | Step/output validation | Goal completion + trajectory evaluation |
This distinction matters when deciding what to build.
Not every AI problem needs an agent.
8. When Should a Product Manager Use an AI Workflow?
A workflow can be appropriate when:
- The process is highly predictable.
- The sequence rarely changes.
- Compliance requires deterministic steps.
- Errors are expensive.
- The number of possible paths is limited.
- You need strong control over system behavior.
For example:
Customer feedback → Categorize → Store in database → Notify PM
There may be little reason to introduce autonomous decision-making.
9. When Does an AI Agent Make More Sense?
An agent becomes more useful when:
- The task has multiple possible paths.
- The correct next action depends on previous results.
- The environment changes dynamically.
- Multiple tools may be required.
- Iteration is important.
- The system needs to operate toward a goal rather than execute one fixed recipe.
For example, an AI Customer Support Agent might need to:
- Understand the customer’s problem.
- Search the knowledge base.
- Check the customer’s account.
- Identify the applicable policy.
- Decide whether it can resolve the issue.
- Take an action such as issuing a refund.
- Escalate when required.
- Confirm the resolution.
The decision path can change depending on the customer’s situation.
10. What Should an AI Product Manager Evaluate?
Building an agent is not simply about making the model more autonomous.
An AI Product Manager should ask:
Goal
What exactly is the agent trying to accomplish?
Tools
Which tools does it need access to?
Permissions
What actions is it allowed to take?
Guardrails
What actions require human approval?
Evaluation
How do we determine whether the agent succeeded?
Reliability
What happens when a tool fails or the model makes an incorrect decision?
Cost
How many model calls and tool calls are required?
Latency
How long does the agent take to complete a task?
Observability
Can the product team understand why an agent failed?
These questions move the discussion from “Can we build an agent?” to the more important question:
“Should this product use an agent, and how do we make it reliable?“
11. A Simple Mental Model
You can remember the entire concept with three levels.
Level 1 — LLM
Input → Model → Output
The model generates a response.
Level 2 — AI Workflow
Input → Predefined Steps → Tools/LLM → Output
Humans define the process.
Level 3 — AI Agent
Goal → Reason → Act → Observe → Iterate → Outcome
The AI participates in deciding how to achieve the goal.
Conclusion
AI agents become much easier to understand when you stop treating them as a mysterious new category of AI.
Start with the progression:
LLM → Workflow → Agent
An LLM generates responses.
An AI workflow connects models to predefined processes and tools.
An AI agent goes one step further by allowing the AI system to participate in deciding what actions to take, observe the results, and iterate toward a goal.
For an AI Product Manager, the most important skill is not memorizing terms such as RAG or ReAct. It is understanding where decision-making happens, how tools are used, how much autonomy is appropriate, and how success can be evaluated.
That is the foundation for designing reliable AI products.
Frequently Asked Questions
What is an AI agent?
An AI agent is a software system that uses an AI model to pursue a goal by reasoning about actions, using tools, observing results, and potentially iterating until the task is completed or a stopping condition is reached.
What is the difference between an AI workflow and an AI agent?
An AI workflow generally follows a predefined sequence designed by humans. An AI agent can dynamically determine its next actions based on the goal and observations from its environment.
Is RAG an AI agent?
No. RAG is a technique for retrieving relevant external information and providing it to an AI model before generation. RAG can be used inside an AI workflow or an AI agent.
What is ReAct in AI?
ReAct is a framework that combines reasoning and action, allowing an AI system to alternate between deciding what to do and interacting with tools or an environment.
Do all AI products need agents?
No. A deterministic workflow may be more appropriate when the process is predictable and requires strong control. Agents are more relevant when tasks require dynamic decision-making and multiple possible paths.