“We will Build AI Agents.” If every Python script that calls ChatGPT is an AI Agent, then our integration teams are suddenly become AI architects. Welcome to the latest corporate obsession: AI Agents. Chatbot? Agent. Workflow? Agent. Python script? Agent. REST API? Agent. Cron job? Apparently, that is Agentic AI too. Somehow, adding the word “Agent” to a PowerPoint seems to increase the excitement, the architecture diagrams, and occasionally the project budget.
But let's simplify this. An LLM is not an Agent. An LLM is the brain. It understands language, reasons about a problem and generates a response. An Agent is the system built around that intelligence to achieve a goal. Imagine telling your travel admin, “Book me a flight to Bangalore.” The Travel Admin does not simply respond with a paragraph about airlines. He needs to understand the dates, check your preferences, search available flights, compare options, make a decision, book the ticket and send you the confirmation.
He did not just answer a question. He completed a task. That is the difference. An LLM answers questions. An Agent works on goals. If I ask, “Explain Kubernetes,” an LLM is perfectly capable of answering. But if I ask, “Create a Kubernetes cluster, deploy my application, verify that it works, troubleshoot any failures and notify the team,” we are moving into Agent territory.
Because the system needs more than intelligence. It needs memory, tools, planning, action and feedback.
- The
LLM provides the brain.
- Memory
provides continuity.
- Tools allow
the system to interact with the real world: APIs, databases, Jira,
Salesforce, Kubernetes, email and everything else the Agent is authorized
to access.
- Planning
allows it to break a larger goal into smaller steps.
- Action
is where things actually happen.
- And reflection/feedback
allows the system to look at what it has done and decide whether it worked
or needs another attempt.
Put those together and you have something that starts behaving like a digital worker. And then, naturally, we decided that one Agent was not enough. There it comes: Multi-Agent AI. Now we have an Architect Agent, Developer Agent, Tester Agent, Security Agent and DevOps Agent. Sounds familiar? right. It should. We have basically recreated a software engineering team, except everyone is running on tokens. The important part is not having multiple LLMs. The important part is having specialized capabilities working together toward a common goal.
But here is the part that does not make it into most Agentic AI presentations. Not everything needs an Agent. If your process is simply: A → B → C → D
You probably don't need Agentic AI. You just need automation. And you know, the available deterministic software does a great job here. Deterministic software is not suddenly obsolete because someone discovered the word “agentic.” Agents become valuable when the goal is clear but the path is not.
- When
decisions change based on outcomes.
- When
multiple tools need to interact.
- When
the system needs to adapt while executing.
Otherwise, we are just putting an AI engine on a bicycle. It may look impressive. But, The bicycle still goes at the same speed. So the next time someone says: “Let's build an AI Agent.” Don't immediately ask which framework they are using. Ask a much more uncomfortable questions: like
- What
goal will this Agent achieve?
- What
decisions will it make?
- What
tools can it access?
- What
actions is it allowed to take?
- How
will those actions be validated? ( and Accuracy of the outcomes)
- When
does a human need to intervene?
And most importantly: What problem does this Agent solve that our existing software cannot and What measurable value will this Agent create?
These questions will build better systems. Framework names and buzzwords just build presentations.
The bigger lesson is that AI is not just one thing.
- An LLM
gives you intelligence.
- RAG
gives you context.
- Memory
gives you continuity.
- Tools
give you capability.
- Planning
gives you direction.
- Actions
create value.
- Reflection/Feedback
improves quality.
If the above pieces are not there, then your shiny new “Agent” may just be a chatbot wearing a fancy job title. So the next time someone proudly announces: “We are building an Agentic AI platform.”
Smile. Then ask: “What can this Agent do tomorrow that our current software cannot?”
If the answer is clear, then it is measurable and valuable and You might actually be building an Agent.
If the answer begins with: “Well, it is Agentic AI”
Congratulations. You have just witnessed another gold medal performance in the AI Buzzword Olympics.
Stay tuned for Topic #6 "We will add MCP"