Studying Langchain, RAG, and AI Agents for tech learning

🧠 Building LLM Agents is Fun — Until You Start Debugging Them 😅 Tools like LangGraph and LangChain make building AI agents incredibly powerful — but once you dive in, the real-world pain points hit hard: 💸 High API costs from repeated LLM calls 🕒 Slow iteration waiting for responses every run 🧩 Hard-to-debug multi-node graphs with unpredictable outputs 🔁 No way to replay or reproduce a previous successful run ❌ Tool-calling flows can’t easily be simulated offline These challenges make experimentation expensive and slow — especially when every tweak means another round of costly LLM calls. If you’ve been building agentic workflows lately — how are you handling this? Caching? Local simulation? Smaller mock models? Curious to hear how others are approaching this balance between power and practicality. #AI #LangGraph #LangChain #LLM #Agents #GenAI #SystemDesign ♻️ Repost to spark more dev discussions. 🔔 Follow me Arijit Sengupta for more like this. 📌 Subscribe to my free weekly newsletter.

Context is the new RAM, and your agentic context engine orchestrates it. Context engineering is the emerging discipline focused on building systems that deliver the right information at the right moment. An agentic context engine integrates outputs from multiple tools that perform search, retrieval, and reasoning to supply AI agents and humans with the most relevant data just in time. These tools act as JIT context compilers, assembling and optimizing knowledge on demand.

Building Agents with LangGraph and LangSmith The recent session led by Sabur B. was an insightful introduction to how AI agents can be designed and orchestrated using LangGraph and LangSmith. Sabur explained how these tools make it easier to visualize, debug, and coordinate multi-agent systems, offering a hands-on perspective on the growing ecosystem around LangChain. 💡 Key takeaways from the talk included: The fundamentals of building and structuring AI agents. How LangGraph enables agent orchestration through graph-based workflows. Using LangSmith to monitor, trace, and evaluate agent performance. It was a great opportunity to understand the practical side of building intelligent, modular systems and how these frameworks streamline experimentation and development in the LLM space. A huge thanks to Sabur Butt for sharing his expertise and to everyone who joined the session — it was an engaging and informative experience! #LangGraph #LangSmith #LangChain #AIagents #ArtificialIntelligence #LLMs #MachineLearning #TechTalk

Building general-purpose AI agents isn't just about throwing an LLM at a problem. You need planning modules that decompose goals. Awareness layers that track environmental state. Execution engines with proper error handling. Memory architectures that persist across sessions. And security layers that prevent things from going sideways. Here's the full architecture we use at BigCircle, 9 core modules that make an agent actually work in production. The key insight? The Control Loop. Plan → Awareness → Execute → Observe → Re-plan. This is where ReAct methodology meets real-world constraints. Details in the carousel. Built from shipping agent systems, not just research papers. #AIArchitecture #ArtificialIntelligence #AI #ReActMethodology #AIinProduction

ATLAS delivers up to 400% faster inference by learning in real-time. 🚀 From today's coverage on VentureBeat: "The shift from static to adaptive optimization represents a fundamental rethinking of how inference platforms should work." We couldn't agree more! "The software and algorithmic improvement is able to close the gap with really specialized hardware," says Tri Dao, Founder and Chief Scientist at Together AI. "We were seeing 500 tokens per second on these huge models that are even faster than some of the customized chips." Read more (link in comments 👇)

💥 We took “agentic AI” from hype to boring, reliable, fast. Most agent stacks start cute and turn chaotic: a mega-orchestrator prompting tools, spawning sub-agents, and leaking state everywhere. Great demo… until P95 blows up and on-call gets paged at 2am. 1. Old → New: We replaced prompt-spaghetti with graph-based orchestration -Composition, not inheritance: IAgentNode interface; planner, router, tool-runner, verifier, memory as separate nodes with explicit contracts. -Choreography when possible, orchestration when necessary: events for happy paths; a coordinator only for cross-tool sagas. -Deterministic state: every step persists {plan_id, step_id, input_hash, tool, args, result_ref} → retries are idempotent. 2. Hard constraints: -Run old + new pipelines in the same binary (no extra services). -Zero-downtime cutover with shadow eval. -Safety first: sandboxed, least-privilege tool calls. 3.Breakthroughs that mattered: -Feature-flagged dual paths behind one interface → live traffic mirrored to the new graph. -Saga pattern for multi-tool actions (with compensations). -Time-boxed steps + backpressure; per-tool queues to tame tail latencies. -Deep observability: step-level traces, prompt/version lineage, artifacts, eval queues (faithfulness/grounding/toxicity). -Memory split: short-term scratchpad vs. long-term vector/SQL with router rules (structured retrieval before embeddings). 3.Outcomes: predictable latency, cleaner rollbacks, lower tool spend—agents that ship value instead of surprises. Takeaway: Agent systems aren’t “magic prompts.” They’re distributed systems—design them like one. #AIAgents #AgenticAI #BackendEngineering #Orchestration #LangGraph #LLM #RAG #MLOps #SystemsDesign #Reliability #Observability #Scalability

If you’re building an AI application today, Retrieval-Augmented Generation (RAG) is where intelligence meets context. This visual breaks down the full stack, from ingestion and embeddings to orchestration, observability, and evaluation, showing how to move from static LLMs to dynamic, context-aware systems. What I like about this, is how it blends: • Hybrid retrieval (vector + BM25 fusion) • Strong evaluation loops (synthetic + human feedback) • Modern observability (Langfuse, Helicone, Phoenix) A solid reference for anyone scaling enterprise RAG pipelines - especially with OpenAI, Claude, or Gemini at the core. #GenerativeAI #RAG #VectorDatabases #AIEngineering #LLMs #LangChain #LlamaIndex #AIProducts #DataScience

𝐃𝐢𝐝 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐫𝐮𝐧 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐞𝐧𝐭𝐢𝐫𝐞𝐥𝐲 𝐨𝐧 𝐲𝐨𝐮𝐫 𝐨𝐰𝐧 𝐦𝐚𝐜𝐡𝐢𝐧𝐞? Yesterday I led a Lunch & Learn on local AI models: what they are, how they differ from cloud-based tools, and crucially, when they actually make sense to use. We explored both sides: the advantages (complete data privacy, no recurring API costs, platform independence) and the limitations (hardware requirements, model capabilities, maintenance overhead). I demonstrated tools like #Ollama, #n8n, and #LangChain, showing how to run models locally and build automated workflows, but emphasized that the technology choice should always follow the business case, not the other way around. Seeing people willing to use their lunch break to explore this topic was encouraging. Most joined online, but several attended in person. The questions showed genuine interest in practical applications, particularly around data security and when local deployment justifies the tradeoffs. Multiple people expressed interest in hands-on workshops where they could experiment with these tools directly. I sincerely hope we'll be able to expand the AI Learning Hub in that direction.

Anthropic has released Claude Sonnet 4.5, calling it their most advanced model so far. It’s being positioned as the best coding model in the world, hitting 82% accuracy on SWE-bench Verified, ahead of GPT-5 and Gemini 2.5 Pro. They’ve also rolled out a new Claude Agent SDK, which lets developers build more complex AI agents on top of the model. Big step forward for Anthropic, but also for how AI is moving from being a tool you prompt into something you can build into entire workflows. For me, I’m looking at how these models can plug directly into design, coding, and workflow automation, not just speeding up tasks but reshaping how those flows are built in the first place. https://lnkd.in/eVgEt5yw

🚨 76,000 lines of code produced. More than 6 hours of continuous AI execution. That's what Eric (@realfunnyeric) has done on Replit, and its CEO confirmed it sets a record! I saw this over the weekend, and it hasn’t left my mind. Think about what it takes to make an AI run that long: - Decoupling features so they don’t break each other - Sequencing execution so dependencies don’t collapse - Documenting the future so the agent knows what’s next The hard part is definitely the architecture of intent: Plan, Design, and Anticipate. Impressive ... but I don't know yet if the product is working ☺ Link in the first comment below. #SoftwareEngineering #VibeCoding #IndustrializedCoding