LangChain
LangChain on Beyond Market Intelligence: a running collection of 5 stories we have gathered and hand-picked because they are worth your time. Every post here touches on langchain in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around langchain, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
One in five enterprises can't stop a runaway AI agent's spending in real time
Enterprise adoption of AI agents is revealing a critical shift: organizations are increasingly deploying multiple orchestration platforms—averaging three—to mitigate vendor risk and retain control. This trend, driven by concerns around security, permissions, and visibility, sees Microsoft AI Foundry/Copilot Studio leading usage, with Anthropic's Claude Platform gaining significant consideration. Notably, one in five enterprises still lacks real-time control over agent spending, highlighting the need for robust oversight as AI deployments evolve. Learn more about this emerging landscape with VentureBeat's coverage of Serval’s AI agent, Catalyst.
How to Add Skills in Agents using LangChain
Ever questioned how chat interfaces like ChatGPT and Gemini effortlessly generate diverse outputs—PDFs, presentations, and more—despite relying on a core LLM? The secret lies in "skills," modular instructions loaded only when needed, not a fundamentally smarter model. This post explores how to implement skills within LangChain agents, unlocking a powerful approach to agentic workflows. Discover how this technique simplifies complex tasks and expands agent capabilities. For deeper insight into agent scaling challenges, see "Three Generations of Autoscaling."
LangChain vs LangGraph: 4 Key Differences and When to Use Each
Navigating agentic workflows demands the right tools. LangChain and LangGraph are both vital for building AI systems, but understanding their differences is key to optimal performance. This guide delivers a practical comparison, outlining 4 key distinctions to empower your decision-making. Discover when to leverage LangChain’s versatility versus LangGraph’s focused approach to graph-based agent design. For deeper insights into knowledge exchange within LLMs, explore "How to Utilize OKF Efficiently."
Agentic AI vs AI Automation: What’s the Real Difference?
Across engineering teams, the distinction between AI automation and Agentic AI is becoming increasingly critical. While looping LangChain calls might initially appear to create an "AI agent," production environments often reveal vulnerabilities. Agentic AI represents a more robust architecture, designed for adaptability and resilience. Explore the real differences – and why understanding them is vital for reliable AI deployments. For deeper insights into the broader AI landscape, consider "AI and the rise of the universal entertainment app."
A single AI agent conversation can look perfect and still be broken, leaders from LangChain, Conviva and CoreWeave said at VB Transform 2026
Evaluating AI agents requires a shift from scrutinizing individual conversations to analyzing user cohorts against a baseline, according to leaders from LangChain, Conviva, and CoreWeave at VB Transform 2026. The disconnect between seemingly flawless agent interactions and underlying product issues is driving this change. Teams are moving toward treating evaluation criteria as a living product specification—akin to a product requirements document—rather than a static test suite. This approach, alongside cheaper, narrower judge models, promises a more reliable path to robust AI agent performance.