How I’m Building a Smarter AI Agent: Beyond Prompt Chains to Full Debugging & Deployment

“How I’m Making My AI Agent Smarter Than Prompt Chains — From Debugging to Deployment”

👋 Intro: From Prompt Follower to Real Thinker

Let me be honest — I’m still learning. I’m a 2nd-year BSc CS student trying to build production-level LLM tools.
But this past month, I realized something dangerous:

Most AI agents are just smart parrots.
They don’t remember, adapt, or think. And if you don’t design better logic, they never will.

That’s when I made a decision:

I won’t build just another chatbot.
I’ll build autonomous AI agents that actually get smarter with time.

Here’s how I’m upgrading my agents — even as I learn and debug daily.

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🔧 Problem: Agents Are Dumb (By Default)

At first, I built my agent using simple tools:

• LangChain chains

• Prompt templates

• Basic task routing

But every time I gave it a complex task — like evaluating a resume or suggesting a career path — the agent would:

• Forget what I said earlier

• Repeat generic answers

• Hallucinate facts

I realized: I wasn’t building an AI.
I was building a one-time-use prompt executor.

And users deserve more. Especially for real-world tools like CareerBuilder AI, where real careers are at stake.

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🚀 Solution: Intelligence = Memory + Feedback + Context

Here’s how I started upgrading:

1. 🧠 Contextual Memory (Vector Embedding)

I integrated a Chroma vector database to store:

• User questions

• Past responses

• Resume content

• Roadmap generation history

Now, before answering — the agent pulls relevant memory chunks using semantic search.
It’s no longer flying blind.

I use sentence-transformers for embedding and Chroma for querying — works smoothly with LangChain or raw Python.

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2. 📚 RAG (Retrieval Augmented Generation)

Instead of giving direct prompts, I feed the agent a chunk of curated content before each task:

• Blogs

• Resume templates

• Interview data

The agent reads like a student — then answers like an expert.

I call it the “agent-study mode.”
It’s the same logic I use when learning something new before coding it out.

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3. 🪞 Self-Reflection & Multi-Step Tasks

Using Chain-of-Thought (CoT) prompting, the agent now:

• Thinks step-by-step

• Reflects on output

• Reruns faulty responses with feedback

Even better: I use a second LLM call as a feedback agent, which critiques and improves the main agent’s response.

Think of it like:

Me coding → Then I ask myself, “Wait… is this logic right?” → Then I fix.

I made the agent do the same thing.

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4. 🧑‍💻 Debugging Like a Developer

At first, this was chaotic. Errors, loops, and dead-end calls.

But now, every day I:

• Read logs

• Log LLM outputs

• Set up retry handlers

• Limit hallucinations using custom validation rules

And yes — I still get it wrong.
But every bug I fix = one more step toward building a real AI assistant, not just a toy.

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🧪 Real-World Application: CareerBuilder AI

All of this isn’t just for learning.

I’m applying this upgrade strategy to my live project — CareerBuilder AI:

• Resume Evaluator Agent

• Roadmap Generator Agent

• Blog RAG for personalized upskilling

Soon, every user interaction will improve the agent’s knowledge.

It’ll “learn” from:

• Successes (when users like output)

• Failures (when users click ‘regenerate’ or leave)

And because I store everything in Supabase + Chroma, I can fine-tune this without a full retrain.

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🧩 Lessons I Learned (That You Won’t Find on YouTube)

• 👉 LLMs are not smart — you make them smart with logic

• 👉 Debugging AI agents is like teaching a kid — feedback, repeat, reward

• 👉 Orchestration tools like CrewAI only matter if your agent thinks clearly

• 👉 Most projects fail not because of LLM quality — but because of bad agent planning

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📢 Final Thoughts

I’m not an expert. I’m just a builder who ships daily.
But I can feel the shift — the more I learn, the better my agents become.

I’m not chasing AI trends.
I’m chasing autonomy, intelligence, and real-world use cases.

If you're also building agents — try memory, RAG, self-checking.
It’ll change how you think about “smart” tools forever.

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🔗 Try It Yourself

video link on yt complete working system :

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🚀 What’s Next?

Tomorrow, I’m experimenting with LLM agents that can teach themselves from failure logs.
I’ll share that in Day 31.
Stay tuned.