Python for Network Automation with AgenticOps

Software isn't just math; it's business logic. Day 9/90 of my AI Product Engineer sprint: Python Conditionals (If/Else). This isn't just basic syntax. It is the exact mechanism that routes users in an application: If user is premium -> Route to Gemini Pro. Else -> Route to Flash. I built a raw terminal script today forcing user inputs through a basic decision tree. It’s unpolished, but the underlying logic is exactly how companies route data automatically. You cannot architect complex AI pipelines if you don't control the basic flow of information.

I just wrapped up Anthropic's course on the Model Context Protocol (MCP). If you’ve ever built integrations between AI models and external services, you know it usually means writing a lot of custom boilerplate code and manually handling JSON schemas. The most valuable takeaway from this course was seeing how MCP standardizes that entire process. Instead of building one-off connections, MCP shifts the integration burden to a consistent architecture based on three clean primitives: Tools (controlled by the model), Resources (controlled by the app), and Prompts (controlled by the user). Getting hands-on with the Python SDK to build an MCP server—and replacing manual schema writing with simple decorators—showed exactly how much this protocol reduces the friction of connecting AI to real-world data and APIs. A really practical look at how AI infrastructure is maturing and becoming easier to scale. #ArtificialIntelligence #ModelContextProtocol #Anthropic #Python #SoftwareEngineering

A supply chain attack hit a Python package with ~3 million daily downloads. Malicious code executed automatically on every Python process startup for roughly 40 minutes, enough time to harvest credentials and install a persistent backdoor. That package was LiteLLM, one of the most widely used AI gateway libraries in production environments. And the attack didn't even come through LiteLLM's own code; it came through a compromised GitHub Action in their CI/CD pipeline. The deeper lesson here isn't specific to LiteLLM. It's about how engineering teams think (or don't think) about AI gateways as infrastructure. A proxy that sees your LLM API keys, your prompts, and sits in the request path between your applications and your model providers isn't a dev tool. It's critical infrastructure. We wrote a breakdown of what happened, what the migration path looks like, and what questions to ask of any AI gateway you're evaluating. Link in comments.

Understanding data is one thing. Controlling it is where real power starts. ⚡ Learned how CRUD operations work on lists in Python today. 🧠💻 Create, Read, Update, Delete — simple concepts, but they form the backbone of how applications manage data. Practicing how to add, access, modify, and remove elements made me realize how dynamic data handling actually works. 🔄 This is exactly how real systems operate behind the scenes. Building step by step toward AI Product Engineering. 🚀

The next generation of data infrastructure won't be built for analysts. It'll be built for agents. And honestly, most data platforms aren't ready for that yet. They were designed for humans- people who slow down, double-check, and ask for sign-off before anything hits production. Agents work differently. They move fast, they iterate, and they need infrastructure that can keep up without breaking things. That means Python-native, isolated by default, atomic merges, instant rollbacks. Not manual guardrails. On April 21 at 9am PT, we're showing what that looks like in practice- live, in #Python, with our friends at dltHub. Looking forward to seeing you there :)) Register: https://lnkd.in/eqVQ5C5n

🚀 Excited to share Episode 2 of my MCP (Model Context Protocol) series on Praveen Tech Lab! In this video, I break down some critical concepts for building scalable AI systems: ✅ Sequential vs Concurrent Programming (with real-world restaurant analogy) ✅ Python Asyncio explained with hands-on examples ✅ Deep dive into MCP: Tools, Resources, and Prompts ✅ Fun “Movie Night” demo to simplify everything 🎬 💡 If you're working on AI systems, distributed architecture, or backend engineering, this will be highly useful. 🎥 Watch here: https://lnkd.in/gw6ZDx3K   Would love your feedback and thoughts! #MCP #ModelContextProtocol #PythonAsyncio #AIAgents #GenAI #AgenticAI #ConcurrentProgramming #AIEngineering #SystemDesign #PraveenTechLab

A few weeks ago, I was excited about what I built with AI— ✔️ Daily AI news summaries ✔️ LinkedIn outreach automation It felt like the future. Until reality kicked in. When multiple automations ran together, they started competing for token limits—and everything began to fail. That’s when the real lesson hit: It’s not about the tool—it’s about using the right tool for the right job. So I rebuilt everything: • Core automation in Python • Windows Scheduler for reliable execution • Groq API with LLaMA for fast, cost-effective generation Now the workflows run independently, cleanly, and without limits. It took late nights and a lot of debugging—but that’s the real takeaway from building with AI: 👉 Don’t just build—architect. #AIAutomation #Python #LinkedInAutomation #WorkflowAutomation #BuildInPublic #AITools #LessonsLearned

A few weeks ago, a friend of mine who's a Math PhD told me he was completely stuck with his research. He's a genius at math, but coding isn't his thing. He was trying to use AI chatbots to help him turn complex formulas from academic PDFs into Python code so he could test his ideas. The problem? They kept hallucinating or just missing the logic in the math notation entirely. He was spending days trying to fix broken code that was supposed to save him time. He said: "I just want to test these ideas without getting stuck in the code every time." That stuck with me. I'm a software engineer, so I built him something. I called it AlgoMath, a specialized agent skill that sits on top of Claude Code and OpenCode. Instead of a generic chatbot, it follows a proper autonomous workflow to make sure the math actually stays accurate: It reads the PDF and pulls out the raw mathematical logic. Breaks it into structured steps. Turns those into clean, executable Python code. Runs it in a sandbox to catch errors. Then explains the results and checks everything against the original paper. A task that used to kill his whole week now takes about 30 seconds. He just tells his terminal agent to use the AlgoMath skill, and he's back to doing actual research. I open-sourced it and kept the setup simple: npm install, a small wizard walks you through the rest, and you're running it in your terminal agent immediately. Check it out: NPM: https://lnkd.in/d2TMKpjj GitHub: https://lnkd.in/dwWACnnH #SoftwareEngineering #AIAgents #ClaudeCode #Python #Math #AlgoMath #OpenSource

I published a new article about something many developers will appreciate: how to recover your Claude Code chat history in VS Code without losing the early context that gets compacted away. The key idea is simple: - Claude Code stores history locally in ~/.claude/projects. - The logs are saved as JSONL files. - Context compaction affects the live session, not the stored history. - A Python CLI can help you retrieve and read the transcripts. If you’ve ever lost a good idea because you clicked “yes” too quickly or forgot to save your prompt trail, this workflow can save you time and frustration. Read the article here: https://lnkd.in/epUjEF6f #ClaudeCode #VSCode #Python #DataScience #AI #DeveloperProductivity #Automation #Substack

What if your code could think? That's LangChain. LangChain is a framework that lets you build apps powered by LLMs (like GPT or Claude) - with memory, tools, and logic. Here's how simple it is to build a chatbot with memory in Python: from langchain_openai import ChatOpenAI from langchain.memory import ConversationBufferMemory from langchain.chains import ConversationChain llm = ChatOpenAI(model="gpt-4") memory = ConversationBufferMemory() chain = ConversationChain(llm=llm, memory=memory) chain.predict(input="My name is Virat") chain.predict(input="What's my name?") # → "Your name is Virat." Without memory → every message is a fresh conversation. With memory → the model remembers context across turns. LangChain also lets you: 🔹 Connect LLMs to your own documents (RAG) 🔹 Give the model tools — search, calculator, APIs 🔹 Build multi-step AI agents that reason and act 🔹 Chain prompts together for complex workflows #LangChain #Python #LLM #MachineLearning #BackendDevelopment #LearningInPublic #Java #SpringBoot #AI