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LangGraph is becoming the default framework for teams building agentic AI workflows. That is both a good thing and a problem. The good part: it has real production pedigree, is actively maintained, and is used by teams doing serious work. The problem is that its growing reputation means a lot of tea
Sales teams spend hours every day on tasks that should never see a human. Research a prospect, score them against their fit, and put it all into a CRM. These are repeatable, rule based processes AI workflows driven by multi-agent systems can do all three, with speed and consistency that no human tea
Runtime governance for autonomous AI workflows is becoming framework-native. Over the last few weeks we’ve been exploring a problem that increasingly shows up once AI systems become more autonomous: runtime behavior becomes operationally unpredictable. Not because the models are “bad.” But because l
Your AI demo worked perfectly in development. You opened a local notebook, wrote a clean prompt wrapper, and watched the model respond beautifully to your test queries. It felt like magic. Then production traffic hit. User sessions started losing memory. API latency exploded under concurrent request
Hi everyone! Has anyone found a modern and up-to-date course or short course for learning advanced RAG and agent architectures using technologies like: LCEL Runnables LangGraph Modern LangChain patterns Agentic RAG Memory systems Multi-agent workflows I already know the basics of LangChain, but most
So far, we’ve covered how to use agent tools, skills, and sub-agents. Now, it’s time to put everything into practice. I’m going to walk you through a small proof of concept (POC) project to show how these pieces actually work together. In this small POC project, I built a simple DEV blog post writer
Postmortem: How a LangGraph 0.1 Multi-Agent Bug Broke Our 2026 Customer Support Bot Executive Summary On October 12, 2026, our production customer support bot experienced a 4-hour partial outage caused by an unpatched edge case in LangGraph 0.1’s multi-agent orchestration layer. The bug triggered in
Multi-agent systems need three layers of visibility. System health, run quality, and workflow state. We run a stack of Sentry, Langfuse, and LangGraph for that. Three tools, three clearly separated jobs, none can solve the problem of the others. Here is how it plays together at our place and why exa
At some point in building an agentic system, you will hit the same wall. An agent workflow needs to pause, wait for a human decision, and resume. The implementation seems straightforward but then you realise: what happens to the state during the pause? Where does it live? Who owns it? How does the r
LangGraph State Machine Patterns — How to Stop AI Agents from Going Off-Script The "Agent Does Something Unexpected" Problem LLM-powered agents that run outside their intended scope — infinite loops, unauthorized operations, non-interruptible processes. This happens when agent state management is im
88% of Agent Systems Got Hacked — Your LangGraph Auth Layer Is the Problem 88% of teams running AI agents reported security incidents. Not hypothetical risk — actual incidents. And the root cause isn't your LLM. It's the 4 auth gaps every LangGraph developer ships to production without noticing. Her
Cybersecurity researchers have disclosed three security vulnerabilities impacting LangChain and LangGraph that, if successfully exploited, could expose filesystem data, environment secrets, and conversation history. Both LangChain and LangGraph are open-source frameworks that are used to build appli
There’s always a moment that comes up in almost every serious AI project. To be honest, at first everything feels simple. Like you wire up a model, add a tool or two, maybe introduce a planner. Then the system grows! You add another agent. And suddenly, what started as a clean “agent flow” turns int
