From Search Pipelines to Agentic AI: Our Journey Building a Smarter Helpdesk Bot

As part of my ongoing GenAI journey, we have been building a smart helpdesk bot using Azure AI Search and Azure OpenAI. The goal is simple:

✅ Take a user question ✅ Search across 4 internal sources ✅ Show the most relevant answer first ✅ Let users scroll through the remaining three

At first, it looked like a classic RAG (Retrieval-Augmented Generation) pattern. But I started wondering...

❓ Are we Building Agentic AI?

This sparked the key question in my mind:

Is Agentic AI just a prompt-driven model?

The short answer is: No. While prompts are important, true Agentic AI is much more:

🔹 It plans actions

🔹 It uses tools dynamically

🔹 It can adapt based on feedback

🔹 It might even have memory

⚙️ Our Initial Bot Setup: Smart, but Manual

Here’s the current pipeline:

1. Accepts user input

2. Queries all 4 sources using Azure AI Search

3. Uses Azure OpenAI to evaluate and rank the results

4. Displays the top answer first, others via scroll

This works well. But it’s not autonomous. It’s a smart app — not yet an agent.

🧠 Evolving It into Agentic AI

To go agentic, we are adding components that mimic human-like behavior:

🔧 Step 1: Treat Each Step as a Tool

• → Queries Azure AI Search

• → Uses GPT to rank responses

• → Structures the UI output

🧭 Step 2: Planning with a Coordinator Agent

Using tools like LangGraph or CrewAI, we are defining workflows:

• Search → Evaluate → Retry if unclear → Respond

• If answers are poor, reformulate the query automatically

🔄 Step 3: Add Self-Evaluation

The agent decides:

• Are the answers good enough?

• Should we try a better prompt?

• Do we need to ask the user for more detail?

🧵 Step 4: Add Memory (Optional Phase)

Eventually, the agent will:

• Remember past questions

• Learn which source gives the best info

• Personalize responses

🎯 Why Shift to Agentic AI?

Here’s the difference in one table:

🧰 Stack I’m Using

• Azure AI Search – For semantic query across sources

• Azure OpenAI (GPT-4) – For ranking, summarizing, and query rephrasing

• LangGraph / AutoGen – For multi-step reasoning and autonomy

• React – For UI with scrollable answer cards

💡 Final Thoughts

This experience showed me that a well-crafted LLM app is powerful, but an autonomous, decision-making AI agent is transformative.

Agentic AI isn't just about fancy architecture. It's about giving AI systems the ability to reason, retry, and refine — just like we do.

If you're working on similar use cases, or are curious how to evolve LLM apps into agents, I’d love to connect and exchange ideas! 🤝