🚀 Everyone is building AI wrappers.

Very few developers are building AI systems. 🤔

There's a big difference.

A production-ready AI agent is much more than an LLM. 🤖

It requires:

✅ A decision loop 🔄
✅ Tool integration 🛠️
✅ Intent recognition 🎯
✅ Error handling and recovery 🛡️
✅ Context and state management 🧠
✅ Clear separation between reasoning and execution ⚖️
✅ An extensible architecture 🏗️

The LLM is just one component.

The real engineering lies in designing how the agent observes, reasons, decides, and acts. 🧩

Master these fundamentals, and you'll be able to build AI applications with any model or framework—from Ollama and OpenAI to LangChain and CrewAI. 🚀

To help developers understand the fundamentals, I explained an AI agent from scratch using pure Python and Ollama—without hiding the core concepts behind a framework. 💻

🎥 https://youtu.be/tkA6vCPihuE

💬 If you were building the next version of this agent, which capability would you add first?

✔️ Memory 🧠
✔️ Web Search 🔍
✔️ RAG 📚
✔️ MCP Support 🔌
✔️ Multi-Agent Collaboration 🤝
✔️ Computer Use 💻
✔️ Voice Interface 🎤

#AI #AIAgents #Python #Ollama #LLM #MachineLearning #AIEngineering #SoftwareEngineering #GenerativeAI #OpenSourceAI

⇒ 𝐌𝐨𝐬𝐭 𝐨𝐟 𝐮𝐬 𝐭𝐡𝐢𝐧𝐤 𝐀𝐈 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐢𝐬 𝐣𝐮𝐬𝐭 𝐚𝐧 𝐋𝐋𝐌 + 𝐚 𝐬𝐲𝐬𝐭𝐞𝐦 𝐩𝐫𝐨𝐦𝐩𝐭.

Actually, that approach may work for a demo, but production support needs much more.

When a customer asks to check an order, change a reservation, or request a refund, the system needs to manage 𝙨𝙩𝙖𝙩𝙚, 𝙩𝙤𝙤𝙡𝙨, 𝙥𝙚𝙧𝙢𝙞𝙨𝙨𝙞𝙤𝙣𝙨, 𝙫𝙖𝙡𝙞𝙙𝙖𝙩𝙞𝙤𝙣, 𝙖𝙣𝙙 𝙚𝙭𝙚𝙘𝙪𝙩𝙞𝙤𝙣.

A solid architecture looks like this:

✅ 𝙆𝙚𝙚𝙥 𝙨𝙩𝙖𝙩𝙚 𝙤𝙪𝙩𝙨𝙞𝙙𝙚 𝙩𝙝𝙚 𝙇𝙇𝙈: your application should manage session data, transactions, authentication, and tool results.

✅ 𝙐𝙨𝙚 𝙩𝙝𝙚 𝙇𝙇𝙈 𝙖𝙨 𝙖 𝙧𝙤𝙪𝙩𝙚𝙧: let the model understand intent, choose the right tool, and extract parameters.

For example:

𝚐𝚎𝚝_𝚘𝚛𝚍𝚎𝚛_𝚜𝚝𝚊𝚝𝚞𝚜(𝚘𝚛𝚍𝚎𝚛_𝚒𝚍)
𝚒𝚗𝚒𝚝𝚒𝚊𝚝𝚎_𝚛𝚎𝚏𝚞𝚗𝚍(𝚘𝚛𝚍𝚎𝚛_𝚒𝚍)

The backend should handle the actual database operations and business rules.

✅ 𝙆𝙚𝙚𝙥 𝙚𝙭𝙚𝙘𝙪𝙩𝙞𝙤𝙣 𝙙𝙚𝙩𝙚𝙧𝙢𝙞𝙣𝙞𝙨𝙩𝙞𝙘: tools should return structured results. Your runtime decides what happens next.

If validation fails, permissions are missing, or human intervention is required, your application should handle it with explicit rules.

The key separation is simple:

𝑳𝑳𝑴 → 𝒓𝒆𝒂𝒔𝒐𝒏𝒊𝒏𝒈 & 𝒓𝒐𝒖𝒕𝒊𝒏𝒈
𝑩𝒂𝒄𝒌𝒆𝒏𝒅 → 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒍𝒐𝒈𝒊𝒄 & 𝒆𝒙𝒆𝒄𝒖𝒕𝒊𝒐𝒏
𝑹𝒖𝒏𝒕𝒊𝒎𝒆 → 𝒔𝒕𝒂𝒕𝒆 & 𝒐𝒓𝒄𝒉𝒆𝒔𝒕𝒓𝒂𝒕𝒊𝒐𝒏

That separation is what makes an AI agent more predictable, auditable, and reliable in production.

An AI support agent isn't just a chatbot with a better prompt; 𝙄𝙩'𝙨 𝙖 𝙨𝙤𝙛𝙩𝙬𝙖𝙧𝙚 𝙨𝙮𝙨𝙩𝙚𝙢 𝙬𝙞𝙩𝙝 𝙖𝙣 𝙇𝙇𝙈 𝙞𝙣𝙨𝙞𝙙𝙚 𝙞𝙩.


▶️ I walk through how to build this kind of agent from scratch here:

https://www.youtube.com/watch?v=AgconCK-l4g

#AI #AIAgents #GenerativeAI #LLM #MachineLearning #Python #SoftwareEngineering #CustomerSupport #AIEngineering #Automation

𝐀𝐈 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐜𝐨𝐦𝐞𝐬 𝐰𝐢𝐭𝐡 𝐚𝐧 𝐮𝐧𝐜𝐨𝐦𝐟𝐨𝐫𝐭𝐚𝐛𝐥𝐞 𝐫𝐞𝐚𝐥𝐢𝐭𝐲: 𝐲𝐨𝐮𝐫 𝐜𝐨𝐝𝐞 𝐜𝐚𝐧 𝐛𝐫𝐞𝐚𝐤 𝐞𝐯𝐞𝐧 𝐰𝐡𝐞𝐧 𝐲𝐨𝐮𝐫 𝐥𝐨𝐠𝐢𝐜 𝐢𝐬 𝐜𝐨𝐫𝐫𝐞𝐜𝐭.

I have experienced this firsthand while building AI agents with Gemini and LangChain.

➜ A model endpoint changes.

➜ A parameter gets renamed.

➜ A framework updates its API.

A response that used to be a string becomes a structured object.

Suddenly, perfectly reasonable code starts throwing errors.

What I have learned from that:

✅ 𝑫𝒐𝒏’𝒕 𝒕𝒊𝒈𝒉𝒕𝒍𝒚 𝒄𝒐𝒖𝒑𝒍𝒆 𝒚𝒐𝒖𝒓 𝒂𝒑𝒑𝒍𝒊𝒄𝒂𝒕𝒊𝒐𝒏 𝒕𝒐 𝒇𝒓𝒂𝒎𝒆𝒘𝒐𝒓𝒌 𝒊𝒏𝒕𝒆𝒓𝒏𝒂𝒍𝒔: Keep your business logic separate from model and framework integrations.

✅ 𝑬𝒙𝒑𝒆𝒄𝒕 𝑨𝑷𝑰𝒔 𝒕𝒐 𝒆𝒗𝒐𝒍𝒗𝒆: Pin important dependencies, read changelogs, and test upgrades before pushing them into production.

✅ 𝑵𝒆𝒗𝒆𝒓 𝒂𝒔𝒔𝒖𝒎𝒆 𝒎𝒐𝒅𝒆𝒍 𝒐𝒖𝒕𝒑𝒖𝒕 𝒉𝒂𝒔 𝒐𝒏𝒆 𝒇𝒐𝒓𝒎𝒂𝒕: Structured responses, tool calls, metadata, and plain text can all require different parsing strategies.

✅ 𝑩𝒖𝒊𝒍𝒅 𝒔𝒎𝒂𝒍𝒍 𝒊𝒏𝒕𝒆𝒈𝒓𝒂𝒕𝒊𝒐𝒏 𝒍𝒂𝒚𝒆𝒓𝒔: If Gemini or LangChain changes, you should be able to update one part of your application instead of rewriting the entire agent.

The goal isn't to avoid change, but to make change cheap. While deploying my latest AI agent with Gemini and LangChain, I ran into several of these exact edge cases.

I documented the fixes and the complete setup here:

👉 https://www.youtube.com/watch?v=AgconCK-l4g

If you're building AI agents with Python, this is one lesson worth learning early.

#AIEngineering #AIAgents #Python #LangChain #Gemini #LLM #GenerativeAI #SoftwareEngineering #MachineLearning

When I build an AI agent, I do not start by asking, Which model should I use? I start by designing the system around the model.



The model provides reasoning and language capabilities. The surrounding architecture determines whether the agent is reliable, controllable, and production ready.



This is the approach I follow:

𝟏. 𝐌𝐨𝐝𝐞𝐥: I select the model based on reasoning capability, task complexity, latency, cost, and context requirements.

𝟐. 𝐓𝐨𝐨𝐥𝐬: I give the agent well-defined tools with strict schemas, validation, permissions, and predictable outputs.

𝟑. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭: I carefully control the information provided to the model through retrieval, memory, conversation state, and structured context.

𝟒. 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧: I define how the agent reasons, when it can call tools, when it should retry, when it should ask for clarification, and when it must stop.

𝟓. 𝐆𝐮𝐚𝐫𝐝𝐫𝐚𝐢𝐥𝐬: I validate inputs, tool calls, and outputs. For sensitive or high-impact operations, I add additional verification.

𝟔. 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲: I monitor tool calls, model responses, latency, failures, token usage, and agent execution paths.

𝟕. 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: I test the complete system against realistic scenarios, edge cases, adversarial inputs, and expected failure modes.

▶️ I walk through how to build this kind of agent from scratch here:


https://www.youtube.com/watch?v=AgconCK-l4g



#AIEngineering #AIAgents #GenerativeAI #LLM #MachineLearning #Python #LangChain #LangGraph #MLOps #SoftwareEngineering