Enterprise AI: 7 LLM Skills for Powerful Agents

Recently found an interesting/useful tool - semantic differ: sem it shows what functions, structs, and modules changed instead of raw line diffs. A high-level overview of what actually happened, without reading through every changed line. Useful for agentic engineering: when an AI agent makes changes across multiple files, sem diff tells you what it touched at a glance. Rust, uses tree-sitter, supports 22 languages. Also ships with an MCP server for AI agent integration. To install: brew install sem-cli Full write-up with examples: https://lnkd.in/gPR47RNY https://lnkd.in/gbUzHxZ6 #DevTools #CLI #Git #AI

Reframing a common belief The common belief is that AI speeds up coding. A more useful question is whether it improves how we think about systems. When code is generated quickly, the bottleneck shifts to clarity of intent. If the specification is weak, the output reflects that. If the specification is precise, the system becomes easier to evolve. The focus moves from writing code to defining what should be built. At GIDS 2026, Simon Martinelli explores how spec-driven development changes the way teams work with AI and what it takes to keep requirements, code, and tests in sync. A thoughtful session for engineers and architects interested in shaping systems through clearer specifications. https://lnkd.in/gccdURjm GIDS 2026 | April 21–24 | Bengaluru, India | In-person

Most AI systems today don’t actually solve problems. They just retrieve information. That’s the core limitation of traditional RAG. It follows a simple pipeline: Query → Retrieve → Generate This works for lookup. But it breaks down for real-world scenarios involving: * Multi-step reasoning * Cross-system correlation * Stateful context * Production debugging We are now seeing a shift: From **retrieval pipelines** → to **decision-oriented systems** Agentic RAG introduces a control layer that: * Interprets intent * Plans execution (DAG-based, not linear) * Orchestrates tools (logs, metrics, APIs) * Maintains memory across steps * Applies reasoning after aggregation Example: Debugging a microservices latency issue is not a search problem. It requires correlating: * Metrics * Logs * Deployments * Traces Agentic systems actually perform this investigation. Key insight: The LLM is no longer the system. It is a component inside a larger control plane. This unlocks better problem-solving capability, but introduces trade-offs: * Higher cost * Increased orchestration complexity * Observability challenges Not every system needs this. But for complex, multi-system workflows, this is a fundamental shift. Full deep dive (architecture + case studies): 👉 https://lnkd.in/ddGfg63y #AI #AgenticAI #RAG

🚨 Everyone is talking about Claude leaks… But here’s something actually useful 👇 I found this GitHub repo: 👉 https://lnkd.in/g8GimQMz Instead of exploiting anything, this project does something smarter: It rebuilds the idea of an AI “agent system” in a clean, legal, and understandable way. So what’s the real value here? 💡 For engineers • A simple reference on how to structure AI agents • How tools, commands, and workflows connect together • Python-based → easy to read, modify, and extend 🧪 For AI builders & tinkerers • A playground to experiment with agent workflows • Learn how systems track tools, tasks, and behavior • Understand how to compare (audit) different AI systems ⚖️ For people thinking about ethics • Shows how to learn from closed systems without copying them • A great example of “build inspired, not duplicated” 🌍 For the AI community • Real-world example of an agent framework • Easy way to understand how modern AI systems are structured The biggest takeaway? 👉 AI is not just about models anymore 👉 It’s about how you design the system around them Most people will chase leaks. Smart builders will learn from them. #AI #Github #Claude #PromptEngineering #Developers #BuildInPublic

You don’t always need better prompts — sometimes you need a better thinking model This is a reflection on one of the projects I worked on last year. We started with a simple idea:  let’s create an AI Agent that uses an LLM to generate runnable outputs for legacy Java code that meet certain code conventions to improve developer's performance.  We invested significant effort into prompt engineering: refining structure, adding examples, and guiding the model step by step.  On the surface, the results looked solid… but in practice, they consistently failed to compile or execute correctly. The code conventions were not always met.  At first, it seemed like a prompt design problem. It wasn’t. The real limitation was the model. Once we switched to a reasoning-capable model like o4-mini, the behaviour changed significantly.  Errors dropped, outputs became more consistent, and the model started improving its own results through iteration.  That’s when the difference became clear: 🧩 Reasoning models follow multi-step logic instead of approximating answers  🔁 They handle feedback loops and self-correction more effectively  ⚙️ They operate closer to how a developer actually thinks: planning, adjusting, refining  For me, this was the moment where “prompt engineering” met “model capability.” The takeaway?  Sometimes the biggest improvement doesn’t come from better instructions. It comes from using a model that can actually reason. #GenAI #LLM #AIEngineering #Reasoning #SoftwareModernization #PromptEngineering #LearningByDoing 

AI is smart. I’m smart too. Together… we still need 3 prompts to align You explain what you’re building - you get good response You continue - still good You go one step deeper - slightly off And now you’re there adjusting things like: “okay wait, let me rephrase that…” For a while, I thought this meant: I need better prompts. But honestly, it felt more like a context problem than a prompting problem. Then I learnt something simple: 𝗸𝗲𝗲𝗽𝗶𝗻𝗴 𝗮 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 (claude code specific) file with the stuff I keep repeating anyway: project structure, decisions, constraints, little details. Nothing fancy changed overnight, but:  • Fewer back-and-forth clarifications  • Responses felt more aligned  • Less “this isn’t exactly what I meant” It made things feel less like - starting fresh every time and more like - continuing something that already has context. Wrote about what I tried and what I noticed here: https://lnkd.in/gt_wDdnC #AI #AIAgents #ClaudeCode #Productivity #Tech #LLMs

AI coding tools are making teams faster. That part is real. The problem is that security maturity is not keeping pace with the acceleration. More AI-generated code is shipping before review practices have adapted. Issue #9 from Gradient Push breaks down the four-part pipeline teams need if they want AI-assisted development without quietly increasing risk. https://lnkd.in/eHUbxCQK #AICoding #AppSec #DevSecOps #SoftwareEngineering

A simple way to understand how modern AI agents are built. Most people think it’s just prompts. But real agent systems follow a structure. Here’s a useful framework (inspired by Google’s ADK): 1️⃣ Agents The core unit. They reason, make decisions, and execute tasks. 2️⃣ Tools What gives agents power. APIs, databases, code execution, other agents. 3️⃣ Memory Short-term + long-term context. So the agent doesn’t start from zero every time. 4️⃣ Orchestration How everything connects. Sequential, parallel, or looping workflows between agents. 5️⃣ Evaluation Measure performance. Improve outputs. Ship better systems. Most people focus only on the model. But production AI is really about: how these pieces work together. Once you see this… AI agents stop looking like magic. And start looking like software systems. Curious: Which part do you think is hardest to get right? #AI #AIAgents #MachineLearning #AIEngineering https://lnkd.in/dmmD4Un4

AI can generate the code that compiles, passes basic checks, and looks plausible but is subtly wrong, bloated, or misaligned with what was actually needed. - Plausible but wrong: Correct syntax, wrong logic. It looks like it works until edge cases bite you. - Over-engineered: The AI builds an abstraction layer for a problem that needed 10 lines. - Convention-blind: Ignores your repo’s patterns, naming, or architecture. Code is generic good code, not the code that fits your system. - Confidently hallucinates calls to APIs that don’t exist, uses deprecated methods, or invents config options. These are sometimes caught, but not always, especially when wrapped in an otherwise legitimate structure. - Defensive programming: Too many “try-catch” blocks, absorbing errors silently, and adding too many logs. - Cargo-cult code: Copies patterns without understanding why, like retry logic where it’s not needed, error handling that swallows everything The common thread is that slop is not obvious. It passes the eye test; it looks like real code. That’s what makes it dangerous. Practical guardrails how to reduce AI code slop: https://lnkd.in/g_Na-49A

Using AI (Cursor IDE) for System Design & Development Most people use AI just for code. But the real value is in: *Designing systems *Creating HLD *Thinking through problems One thing that helped me a lot: ->Socratic Prompting (asking better questions) Instead of: “Build payment service” Ask: “Where can it fail?” “How will it scale?” “How to handle retries?” Better questions = Better output I wrote a short blog on: HLD → LLD → Code using AI How to guide Cursor properly Common mistakes to avoid Read here:  https://lnkd.in/gb8BkqYs #AI #SystemDesign #Backend #Cursor #SoftwareEngineering