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.

Lin Sun, Solo.io’s head of open source, explained that Agentgateway allows developers to control which tools agents can access—offering flexibility to expose only tested or approved tools. This enables fine-grained policy enforcement and resilience in agent communication, similar to how service meshes manage microservice traffic. Agentgateway ensures secure and selective tool exposure, supporting scalable and secure agent ecosystems. Major players like AWS and Microsoft are also engaging in its development.

Learn more from The New Stack about the latest in open source projects like Agentgateway:

Learn more from The New Stack about the latest in open source projects like Agentgateway: 

Why Tech Giants Are Backing the New Agentgateway Project 

AI Agents Are Creating a New Security Nightmare for Enterprises and Startups 

Five Steps to Build AI Agents that Actually Deliver Business Results 

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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.

Cramer also addressed Sentry's shift from open source to fair source licensing due to frustration over third parties commercializing their software without contributing back. Sentry now uses Functional Source Licensing, which becomes Apache 2.0 after two years. This move aims to strike a balance between openness and preventing exploitation, while maintaining accessibility for users and avoiding fragmented product versions.

Learn more from The New Stack about the latest in Sentry and David Cramer thoughts on AI development:  

Install Sentry to Monitor Live Applications

Frontend Development Challenges for 2021

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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.

Developers can now offload tasks such as fixing issues, updating documentation, or managing dependencies to the Cursor agent. However, both Linear’s Tom Moor and Cursor’s Andrew Milich emphasized the importance of giving agents clear, thoughtful input. Simply assigning vague tasks like “@cursor, fix this” isn’t effective—developers still need to guide the agent with relevant context, such as links to similar pull requests.

Milich and Moor also discussed the growing value and adoption of autonomous agents, and hinted at a future where more companies build agent-specific APIs to support these tools. The full interview is available via podcast or YouTube.

Learn more from The New Stack about the latest in AI and development in Cursor AI and Linear:  

Install Cursor and Learn Programming With AI Help

Using Cursor AI as Part of Your Development Workflow

Anti-Agile Project Tracker Linear the Latest to Take on Jira

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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 company recently switched from GitHub Copilot to its own AI coding assistant, Windsurf, yielding a 10% productivity boost among 7,000 engineers. However, use of such tools isn’t mandatory—performance remains the main metric. Casey also addresses the impact of AI on junior developers, acknowledging that AI tools often handle tasks traditionally assigned to them. While ServiceNow still hires many interns, he sees the entry-level tech job market as increasingly vulnerable. Despite these concerns, Casey remains optimistic, viewing the AI revolution as transformative and ultimately beneficial, though not without disruption or risk. 
 

Learn more from The New Stack about the latest in AI and development in ServiceNow 

ServiceNow Launches a Control Tower for AI Agents

ServiceNow Acquires Data.World To Expand Its AI Data Strategy 

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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.

Developers often have no visibility into how their code is used, yet they’re increasingly burdened by legal and compliance demands from downstream users, such as requests for Software Bills of Materials (SBOMs) and conformity assessments. The CRA raises the stakes, with potential penalties in the billions for noncompliance, putting immense pressure on the open source ecosystem.

Learn more from The New Stack about Open Source Security:

Open Source Propels the Fall of Security by Obscurity

There Is Just One Way To Do Open Source Security: Together

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In this week’sThe 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. 

Recognizing developers’ hesitation to trust AI-generated code, Warp emphasizes transparency and control, enabling users to inspect and steer the agent’s work in real time through "persistent input" and task list updates. While Warp supports terminal workflows, Lloyd says it’s now better viewed as an AI coding platform. Interestingly, the launch announcement was delivered from horseback in a Western-themed ad, reflecting Warp’s desire to stand out in a crowded field of conventional tech product rollouts. The quirky “Code on Warp” (C.O.W.) branding captured attention and embodied their unique approach.

Learn more from The New Stack about the latest in AI and Warp:

Warp Goes Agentic: A Developer Walk-Through of Warp 2.0

Developer Review of Warp for Windows, an AI Terminal App

How AI Can Help You Learn the Art of Programming

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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. 

Zemlin highlighted the industry's shift from foundational models to open-weight models and now toward inference stacks and agentic AI. He suggested a collective effort may eventually form but cautioned against forcing structure too early, stressing the importance of not hindering innovation. Foundations, he said, must balance scale with agility. On the debate over what qualifies as "open source" in AI, Zemlin adopted a pragmatic view, acknowledging the costs of creating frontier models. He supports open-weight models and believes fully open models, from data to deployment, may emerge over time. 

Learn more from The New Stack about the latest in AI and open source, AI in China, Europe's AI and security regulations, and more: 

Open Source Is Not Local Source, and the Case for Global Cooperation 

US Blocks Open Source ‘Help’ From These Countries 

Open Source Is Worth Defending 

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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. 

However, to reach that point, enterprises must align their data strategies with their AI ambitions. Many have rushed into AI fearing obsolescence, but without preparing their data infrastructure, they're at risk of failure. Current legacy systems are not designed for the massive concurrency demands of agentic AI, potentially leading to underperformance. Verma emphasizes the need to move beyond siloed or "swim lane" databases toward unified, high-performance data platforms tailored for the scale and complexity of the AI era.

Learn more from The New Stack about the latest evolution in AI infrastructure: 

How To Use AI To Design Intelligent, Adaptable Infrastructure

How to Support Developers in Building AI Workloads 

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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. 

Pynt’s research found that 72% of MCP plugins expose high-risk operations, like code execution or accessing privileged APIs, often without proper approval or validation. The danger compounds when untrusted inputs from one agent influence another with elevated permissions. Unlike traditional APIs, MCP calls are made by non-deterministic agents, making it harder to enforce security guardrails. While MCP exploits remain rare for now, most companies lack mature security strategies for it. Shneider believes MCP merely highlights existing API vulnerabilities, and organizations are only beginning to address these risks.

Learn more from The New Stack about the latest in Model Context Protocol: 

Model Context Protocol: A Primer for the Developers 

Building With MCP? Mind the Security Gaps 

MCP-UI Creators on Why AI Agents Need Rich User Interfaces

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Rahul Auradkar, executive VP and GM at Salesforce, grew up in India with a deep passion for cricket, where his love for the game sparked an early interest in data. This fascination with statistics laid the foundation for his current work leading Salesforce’s Data Cloud and Einstein (Unified Data Services) team. Auradkar reflects on how structured data has evolved—from relational databases in enterprise applications to data warehouses, data lakes, and lakehouses. He explains how initial efforts focused on analyzing structured data, which later fed back into business processes. 

Eventually, businesses realized that the byproducts of data—what he calls "data exhaust"—were themselves valuable. The rise of "old AI," or predictive AI, shifted perceptions, showing that data exhaust could define the application itself. As varied systems emerged with distinct protocols and SQL variants, data silos formed, trapping valuable insights. Auradkar emphasizes that the ongoing challenge is unifying these silos to enable seamless, meaningful business interactions—something Salesforce aims to solve with its Data Cloud and agentic AI platform.

Learn more from The New Stack about the evolution of structured data and agent AI: 

How Enterprises and Startups Can Master AI With Smarter Data Practices 

Enterprise AI Success Demands Real-Time Data Platforms

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