The agentic AI space faces challenges around secure, governed connectivity between agents, tools, large language models, and microservices. To address this, Solo.io developed two open-source projects: Kagent and Agentgateway. While Kagent, donated to the Cloud Native Computing Foundation, helps scale AI agents, it lacks a secure way to mediate communication between agents and tools. Enter Agentgateway, donated to the Linux Foundation, which provides governance, observability, and security for agent-to-agent and agent-to-tool traffic. Written in Rust, it supports protocols like MCP and A2A and integrates with Kubernetes Gateway API and inference gateways.

David Cramer, founder and chief product officer of Sentry, remains skeptical about generative AI's current ability to replace human engineers, particularly in software production. While he acknowledges AI tools aren't yet reliable enough for full autonomy—especially in tasks like patch generation—he sees value in using large language models (LLMs) to enhance productivity. Sentry's AI-powered tool, Seer, uses GenAI to help developers debug more efficiently by identifying root causes and summarizing complex system data, mimicking some functions of senior engineers. However, Cramer emphasizes that human oversight remains essential, describing the current stage as "human in the loop" AI, useful for speeding up code reviews and identifying overlooked bugs.

Cursor, the AI code editor, recently integrated with Linear, a project management tool, enabling developers to assign tasks directly to Cursor's background coding agent within Linear. The collaboration felt natural, as Cursor already used Linear internally. Linear's new agent-specific API played a key role in enabling this integration, providing agents like Cursor with context-aware sessions to interact efficiently with the platform.

In this episode of The New Stack Agents, ServiceNow CTO and co-founder Pat Casey discusses why the company runs 90% of its workloads—including AI infrastructure—on its own physical servers rather than the public cloud. ServiceNow maintains GPU hubs across global data centers, enabling efficient, low-latency AI operations. Casey downplays the complexity of running AI models on-prem, noting their team’s strong Kubernetes and Triton expertise.

The European Union’s upcoming Cyber Resilience Act (CRA) goes into effect in October 2026, with the remainder of the requirements going into effect in December 2027, and introduces significant cybersecurity compliance requirements for software vendors, including those who rely heavily on open source components. At the Open Source Summit Europe, Christopher "CRob" Robinson of the Open Source Security Foundation highlighted concerns about how these regulations could impact open source maintainers. Many open source projects begin as personal solutions to shared problems and grow in popularity, often ending up embedded in critical systems across industries like automotive and energy. Despite this widespread use—Robinson noted up to 97% of commercial software contains open source—these projects are frequently maintained by individuals or small teams with limited resources.

In this week’s The New Stack Agents, Zach Lloyd, founder and CEO of Warp, discussed the launch of Warp Code, the latest evolution of the Warp terminal into a full agentic development environment. Originally launched in 2022 to modernize the terminal, Warp now integrates powerful AI agents to help developers write, debug, and ship code. Key new features include a built-in file editor, project-structuring tools, agent-driven code review, and WARP.md files that guide agent behavior.

In a recent episode of The New Stack Agents from the Open Source Summit in Amsterdam, Jim Zemlin, executive director of the Linux Foundation, discussed the evolving landscape of open source AI. While the Linux Foundation has helped build ecosystems like the CNCF for cloud-native computing, there's no unified umbrella foundation yet for open source AI. Existing efforts include the PyTorch Foundation and LF AI & Data, but AI development is still fragmented across models, tooling, and standards.

Enterprise AI is still in its infancy, with less than 1% of enterprise data currently used to fuel AI, according to Raj Verma, CEO of SingleStore. While consumer AI is slightly more advanced, most organizations are only beginning to understand the scale of infrastructure needed for true AI adoption. Verma predicts AI will evolve in three phases: first, the easy tasks will be automated; next, complex tasks will become easier; and finally, the seemingly impossible will become achievable—likely within three years.

Anthropic's Model Context Protocol (MCP) has become the standard for connecting AI agents to tools and data, but its security has lagged behind. In The New Stack Agents podcast, Tzvika Shneider, CEO of API security startup Pynt, discussed the growing risks MCP introduces. Shneider sees MCP as a natural evolution from traditional APIs to LLMs and now to AI agents. However, MCP adds complexity and vulnerability, especially as agents interact across multiple servers.

In this week’s episode ofThe New Stack Agents, Scott Carey, editor-in-chief of LeadDev, discussed their first AI Impact Report, which explores how engineering teams are adopting AI tools. The report shows that two-thirds of developers are actively using AI, with another 20% in pilot stages and only 2% having no plans to use AI — a group Carey finds particularly intriguing. Popular tools include Cursor (43%) and GitHub Copilot (37%), with others like OpenAI, Gemini, and Claude following, while Amazon Q and Replit lag behind.

While AI training garners most of the spotlight — and investment — the demands of AI inference are shaping up to be an even bigger challenge. In this episode of The New Stack Makers, Sid Sheth, founder and CEO of d-Matrix, argues that inference is anything but one-size-fits-all. Different use cases — from low-cost to high-interactivity or throughput-optimized — require tailored hardware, and existing GPU architectures aren’t built to address all these needs simultaneously.

In the latest episode ofThe New Stack Agents, Naveen Rao, VP of AI at Databricks and a former neuroscientist, reflects on the evolution of AI, neural networks, and the energy constraints that define both biological and artificial intelligence. Rao, who once built circuit systems as a child and later studied the brain’s 20-watt efficiency at Duke and Brown, argues that current AI development—relying on massive energy-intensive data centers—is unsustainable. He believes true intelligence should emerge from low-power, efficient systems, more aligned with biological computing.

Fal.ai, once focused on machine learning infrastructure, has evolved into a major player in generative media. In this episode of The New Stack Agents, hosts speak with Fal.ai CEO Burkay Gur and investor Glenn Solomon of Notable Capital. Originally aiming to optimize Python runtimes, Fal.ai shifted direction as generative AI exploded, driven by tools like DALL·E and ChatGPT. Today, Fal.ai hosts hundreds of models—from image to audio and video—and emphasizes fast, optimized inference to meet growing demand.

Traditional headless browsers weren’t built for AI agents, often breaking when web elements shift even slightly. Paul Klein IV, founder of Browserbase and its open-source tool Stagehand, is tackling this by creating a browser infrastructure designed specifically for AI control. On The New Stack Agents podcast, Klein explained that Stagehand enables AI agents to interpret vague, natural-language instructions and still function reliably—even when web pages change. This flexibility contrasts with brittle legacy tools built for deterministic testing. Instead of writing 100 scripts for 100 websites, one AI-powered script can now handle thousands.

In a recent episode of The New Stack Agents livestream, Antje Barth, AWS Developer Advocate for Generative AI, discussed the growing developer interest in building agentic and multi-agent systems. While foundational model knowledge is now common, Barth noted that developers are increasingly focused on tools, frameworks, and protocols for scaling agent-based applications. She emphasized the complexity of deploying such systems, particularly around navigating human-centric interfaces and minimizing latency in multi-agent communication.

In this episode of The New Stack Agents, Andrew Lee, co-founder of Shortwave and Firebase, discusses the evolution of his Gmail-centric email client into an AI-first platform. Initially launched in 2020 with traditional improvements like better threading and search, Shortwave pivoted to agentic AI after the rise of large language models (LLMs). Early features like summarization and translation garnered hype but lacked deep utility.

In this on-the-road episode of The New Stack Makers, Editor in Chief Heather Joslyn speaks with Ev Kontsevoy, CEO and co-founder of Teleport, from the floor of KubeCon + CloudNativeCon Europe in London. The discussion centers on infrastructure security and the growing need for robust identity management. Citing alarming cybersecurity statistics—such as the $5 million average cost of a breach and rising attack frequency—Kontsevoy stresses that complexity is the root challenge in securing infrastructure.

Container-based Linux distributions are gaining traction, especially for edge deployments that demand lightweight and secure operating systems. Talos Linux, developed by Sidero Labs, is purpose-built for Kubernetes with security-first features like a fully immutable file system and disabled SSH access. In a demo, Sidero CTO Andrew Rynhard and Head of Product Justin Garrison explained Talos’s design philosophy, highlighting its minimalism and focus on automation. Inspired by CoreOS, Talos removes traditional tools like systemd and Bash, replacing them with machineD, a custom process manager written in Go.

AI agents hold the promise of continuously testing, scanning, and fixing code for security vulnerabilities, but we're still progressing toward that vision. Startups like Aptori are helping bridge the gap by building AI-powered security engineers for enterprises. Aptori maps an organization’s codebase, APIs, and cloud infrastructure in real time to understand data flows and authorization logic, allowing it to detect and eventually remediate security issues.