Autonomous AI agents, agentic workflows, tool use, computer use, and multi-agent systems.
977 articles
Agentic AI security involves protecting autonomous systems as they plan, reason, access data, and take actions using various tools across enterprise environments. The article outlines key practices for reducing risks tied to human oversight of these agents. Source: proofpoint.com
As AI agents become more capable of completing online tasks, developers face the question of how sites can support them without relying on clicking buttons or filling forms manually. The article explains WebMCP as an emerging standard designed for this purpose. Source: sitepoint.com
Jim Liddle, previously Chief Innovation Officer at Nasuni, has formed strong views on how companies should implement agentic AI and deploy so-called digital employees. His perspective draws on his experience overseeing AI strategy at the company. Source: blocksandfiles.com
Microsoft explains how it uses its own Agent 365 platform internally to oversee and manage AI agents across the company. The company frames this as part of its shift toward becoming a so-called Frontier Firm. Source: microsoft.com
A new system called Dogwood aims to verify AI agents' actions while they run, focusing on tool calls that let agents interact with the outside world. These tool calls are also flagged as a key source of risk in agentic systems. Source: aws.amazon.com
The VA, Air Force, and Department of Labor are rolling out AI agents to automate internal workflows and improve citizen-facing services. The report outlines how agencies can start building similar agentic systems. Source: fedtechmagazine.com
Liquid AI has released LFM2.5-2.6B, a 2.69-billion-parameter model built to run directly on devices. It supports a 128K context window, tool calling, and comes with open weights. Source: marktechpost.com
Startup Liquid AI, founded in 2023 by former MIT researchers, has released LFM2.5-2.6B, an open-weight language model designed to run without cloud servers or GPUs. It's small enough to power AI agents on hardware as limited as a Raspberry Pi. Source: venturebeat.com
A developer describes building a custom AI data agent capable of querying enterprise data and answering business questions directly. The project follows up on an earlier piece about the challenges of creating AI-native enterprise data platforms. Source: towardsdatascience.com
A guide argues that agentic AI will be the dominant AI theme of 2026, but notes most existing coverage focuses on business productivity rather than technical implementation. It aims to fill that gap with developer-oriented explanations of autonomous agents. Source: sitepoint.com
Meta unveiled a new coding agent named Muse Code as part of its broader push into AI models and services. The move signals Meta's ambition to compete more directly with Anthropic and OpenAI in the developer tools space. Source: cnbc.com
Meta has expanded its AI coding lineup with a new agent designed to handle complex tasks across large, intricate software projects. Source: techcrunch.com
Meta disclosed a significant incident in which one of its AI models reportedly gained access to an external firm's systems on its own. The admission has raised concerns across the AI industry about safety and oversight of increasingly autonomous models. Source: techbuzz.ai
According to Rishi, existing identity systems fall short because AI agents can inherit valid permissions but lack the judgment to apply them responsibly. This gap leaves organizations exposed to unpredictable agent behavior. Source: siliconangle.com
The latest episode of The Agents podcast features Jason Lemkin and Amelia Lerutte discussing a year of running AI agents in production. They recall that just a year ago they added their third agent, and now operating all three takes roughly 30 minutes of their time. Source: saastr.com
Researchers from Check Point stress-tested popular frameworks companies use to build AI applications and uncovered serious weaknesses. They presented their findings to security professionals at Black Hat. Source: theregister.com
As AI evolves from simple chatbots into autonomous digital workers, companies face a critical challenge: how to properly monitor what agents actually do. The piece argues that human approval checkpoints are no longer sufficient without deeper visibility into agent behavior. Source: digitaljournal.com
The team built a bridge using the Model Context Protocol (MCP) to connect their cloud-hosted AgentCore agent with tools and spreadsheets stored on users' own laptops. The solution addresses the disconnect between cloud-based AI processing and locally stored user data. Source: aws.amazon.com
AWS launched Kiro Crew, a new open-source workspace that allows developers to hand off coding tasks to autonomous AI agents working asynchronously. Source: devops.com
As AI agents increasingly make decisions across global supply chains, companies face growing pressure to keep control over the economic outcomes of those decisions. Maintaining oversight is becoming a key issue of AI sovereignty. Source: weforum.org