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Google Chrome and Apple Safari once defined how the internet felt—fast tabs, synced bookmarks, and clean design. But by 2025, that formula is starting to show its age. The web is no longer just something we browse or read. It’s something we ask, direct, and collaborate with. In response, a new generation of AI-first browsers […]
Top 5 AI Browsers to Use in 2026 Read More »
In this tutorial, we walk through the process of creating a fully autonomous fleet-analysis agent using SmolAgents and a local Qwen model. We generate telemetry data, load it through a custom tool, and let our agent reason, analyze, and visualize maintenance risks without any external API calls. At each step of implementation, we see how
Google has open sourced A2UI, an Agent to User Interface specification and set of libraries that lets agents describe rich native interfaces in a declarative JSON format while client applications render them with their own components. The project targets a clear problem, how to let remote agents present secure, interactive interfaces across trust boundaries without
ESG reporting or Environmental, Social, and Governance reporting, often feels overwhelming because the data comes from so many places and takes ages to pull together. Teams spend most of their time collecting numbers instead of interpreting what they mean. Agentic AI changes that dynamic. Instead of one chatbot answering questions, you get a coordinated group
Building an Agentic AI Pipeline for ESG Reporting Read More »
NVIDIA has released the Nemotron 3 family of open models as part of a full stack for agentic AI, including model weights, datasets and reinforcement learning tools. The family has three sizes, Nano, Super and Ultra, and targets multi agent systems that need long context reasoning with tight control over inference cost. Nano has about
We explore how businesses can build readiness across teams, governance, and infrastructure to scale AI agents responsibly in 2026. The post Preparing for the Next Phase of Enterprise AI Agents in 2026 appeared first on Fusemachines.
Preparing for the Next Phase of Enterprise AI Agents in 2026 Read More »
AI agents are reshaping how we build intelligent systems. AgentOps is quickly becoming a core discipline in AI engineering. With the market expected to grow from $5B in 2024 to $50B by 2030, the demand for production-ready agentic systems is only accelerating. Unlike simple chatbots, agents can sense their environment, reason through complex tasks, plan
AgentOps Learning Path 2026 Read More »
In this tutorial, we implement how we build a small but powerful two-agent CrewAI system that collaborates using the Gemini Flash model. We set up our environment, authenticate securely, define specialized agents, and orchestrate tasks that flow from research to structured writing. As we run the crew, we observe how each component works together in
By executive order last month, Donald Trump launched his so-called “Genesis Mission.” Described as a “historic national effort” to “invest in AI-enabled science to accelerate scientific advancement,” Trump claimed his mission would address key challenges to American energy dominance, innovation, and national security. This mission, Trump boasted, would be a game-changer to science akin to
“A Band-Aid on a giant gash”: Trump’s attacks on science may ruin his AI moonshot Read More »
Let’s be honest! Building AI agents is exciting but debugging them, not so much. As we are pushing the boundaries of agentic AI the complexity of our system is skyrocketing. We have all been there staring at a trace with hundreds of steps, trying to figure out why agent hallucinated or chose the wrong tool.
Meet LangSmith Assistant – Polly [An Agent for Agents] Read More »
