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You don’t lose sleep over dashboards. You lose sleep over decisions. You’re standing on the factory floor, surrounded by the hum of million-dollar assets, and you know something is off. An alert flashes on a screen: “Anomaly Detected.” But what now? In that split second, the gap between a minor […] The post Your factory […]
Your factory floor is asking for better decisions. Are you listening? Read More »
Most AI agents today have a fundamental amnesia problem. Deploy one to browse the web, resolve GitHub issues, or navigate a shopping platform, and it approaches every single task as if it has never seen anything like it before. No matter how many times it has stumbled on the same type of problem, it repeats
Agentic AI in insurance has potential to transform insurance. Read four use cases. The post 4 core insurance functions primed to benefit from agentic AI appeared first on SAS Blogs.
4 core insurance functions primed to benefit from agentic AI Read More »
How to make multiple agents work together like an elite team — autonomously dividing tasks, communicating efficiently, and collaborating seamlessly? The openJiuwen community released the latest version of JiuwenClaw, which adds support for AgentTeam — a multi-agent collaborative capability. It proposes that the next leap beyond Harness Engineering is Coordination Engineering. In in-depth tests, this team
Next Leap to Harness Engineering: JiuwenClaw Pioneers ‘Coordination Engineering’ Read More »
Debugging an AI agent that runs for dozens of steps: reading files, calling APIs, writing code, and revising its own output, is not like debugging a regular function. There is no single stack trace to read. Instead, developers are left staring at hundreds of lines of raw JSON, trying to reconstruct what the model was
Moonshot AI, the Chinese AI lab behind the Kimi assistant, today open-sourced Kimi K2.6 — a native multimodal agentic model that pushes the boundaries of what an AI system can do when left to run autonomously on hard software engineering problems. The release targets practical deployment scenarios: long-running coding agents, front-end generation from natural language,
Error tracking has evolved far beyond catching stack traces after something breaks. In modern software teams, the best error tracking tools for developers help identify crashes in real time, group similar issues intelligently, surface rich debugging context, connect failures to code changes, and reduce the time between detection and resolution. That matters even more now
In this tutorial, we build a universal long-term memory layer for AI agents using Mem0, OpenAI models, and ChromaDB. We design a system that can extract structured memories from natural conversations, store them semantically, retrieve them intelligently, and integrate them directly into personalized agent responses. We move beyond simple chat history and implement persistent, user-scoped
How to Build a Universal Long-Term Memory Layer for AI Agents Using Mem0 and OpenAI Read More »
In this tutorial, we build an advanced, production-ready agentic system using SmolAgents and demonstrate how modern, lightweight AI agents can reason, execute code, dynamically manage tools, and collaborate across multiple agents. We start by installing dependencies and configuring a powerful yet efficient LLM backend, and then progressively design custom tools, including mathematical utilities, memory storage,
Learn how an AI voice agent built with SAS Viya and LLMs can transform fraud detection into real-time, automated fraud resolution through customer interaction and governed decisioning. The post From fraud detection to fraud resolution: Building an AI voice agent with SAS Viya and LLMs appeared first on SAS Blogs.
