The New Stack Podcast

Woodson Martin, CEO ofOutSystems, argues that successful enterprise AI deployments rarely rely on standalone agents. Instead, production systems combine AI agents with data, workflows, APIs, applications, and human oversight. While claims that “95% of agent pilots fail” are common, Martin suggests many of those pilots were simply low-commitment experiments made possible by the low cost of testing AI. Enterprises that succeed typically keep humans in the loop, at least initially, to review recommendations and maintain control over decisions.

Current enterprise use cases for agents include document processing, decision support, and personalized outputs. When integrated into broader systems, these applications can deliver measurable productivity gains. For example,Travel Essencebuilt an agentic system that reduced a two-hour customer planning process to three minutes, allowing staff to focus more on sales and helping drive 20% top-line growth.

Martin also believes AI will pressure traditional SaaS seat-based pricing and accelerate custom software development. In this environment, governed platforms like OutSystems can help enterprises adopt “vibe coding” while maintaining compliance, security, and lifecycle management.

Learn more from The New Stack about the latest developments around enterprise adoption of vibe coding:

How To Use Vibe Coding Safely in the Enterprise

5 Challenges With Vibe Coding for Enterprises 

Vibe Coding: The Shadow IT Problem No One Saw Coming

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

On a recent episode of the The New Stack Agents, Inception Labs CEO Stefano Ermon introduced Mercury 2, a large language model built on diffusion rather than the standard autoregressive approach. Traditional LLMs generate text token by token from left to right, which Ermon describes as “fancy autocomplete.” In contrast, diffusion models begin with a rough draft and refine it in parallel, similar to image systems like Stable Diffusion.

This parallel process allows Mercury 2 to produce over 1,000 tokens per second—five to ten times faster than optimized models from labs such as OpenAI, Anthropic, and Google, according to company tests. Ermon argues diffusion models better leverage GPUs, with support from investor Nvidia to optimize performance.

While Mercury 2 matches mid-tier models like Claude Haiku and Google Flash rather than top systems such as Claude Opus or GPT-4, Ermon believes diffusion’s speed and economic advantages will become increasingly compelling as AI applications scale.

Learn more from The New Stack about the latest developments around around large language model built on diffusion: 

How Diffusion-Based LLM AI Speeds Up Reasoning

Get Ready for Faster Text Generation With Diffusion LLMs 

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

OnThe New Stack Agents, Gavriel Cohen discusses why he built NanoClaw, a minimalist alternative to OpenClaw, after discovering security and architectural flaws in the rapidly growing agentic framework. Cohen, co-founder of AI marketing agencyQwibit, had been running agents across operations, sales, and research usingClaude Code. When Clawdbot (laterOpenClaw) launched, it initially seemed ideal. But Cohen grew concerned after noticing questionable dependencies—including his own outdated GitHub package—excessive WhatsApp data storage, a massive AI-generated codebase nearing 400,000 lines, and a lack of OS-level isolation between agents.

In response, he createdNanoClawwith radical minimalism: only a few hundred core lines, minimal dependencies, and containerized agents. Built around Claude Code “skills,” NanoClaw enables modular, build-time integrations while keeping the runtime small enough to audit easily. Cohen argues AI changes coding norms—favoring duplication over DRY, relaxing strict file limits, and treating code as disposable. His goal is simple, secure infrastructure that enterprises can fully understand and trust.

 

Learn more from The New Stack about the latest around personal AI agents

Anthropic: You can still use your Claude accounts to run OpenClaw, NanoClaw and Co.

It took a researcher fewer than 2 hours to hijack OpenClaw

OpenClaw is being called a security “Dumpster fire,” but there is a way to stay safe

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

A few weeks after Dynatrace acquired DevCycle, Michael Beemer and Andrew Norris discussed on The New Stack Makers podcast how feature flagging is becoming a critical safeguard in the AI era. By integrating DevCycle’s feature flagging into the Dynatrace observability platform, the combined solution delivers a “360-degree view” of software performance at the feature level. This closes a key visibility gap, enabling teams to see exactly how individual features affect systems in production.

As “agentic development” accelerates—where AI agents rapidly generate code—feature flags act as a safety net. They allow teams to test, control, and roll back AI-generated changes in live environments, keeping a human in the loop before full releases. This reduces risk while speeding enterprise adoption of AI tools. The discussion also highlighted support for the Cloud Native Computing Foundation’s OpenFeature standard to avoid vendor lock-in. Ultimately, developers are evolving into “conductors,” orchestrating AI agents with feature flags as their baton.

 

Learn more from The New Stack about the latest around AI enterprise development: 

Why You Can't Build AI Without Progressive Delivery 

Beyond automation: Dynatrace unveils agentic AI that fixes problems on its own 

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

Dynatrace is at a pivotal point, expanding beyond traditional observability into a platform designed for autonomous operations and security powered by agentic AI. In an interview on *The New Stack Makers*, recorded at the Dynatrace Perform conference, Chief Technology Strategist Alois Reitbauer discussed his vision for AI-managed production environments. The conversation followed Dynatrace’s acquisition of DevCycle, a feature-management platform. Reitbauer highlighted feature flags—long used in software development—as a critical safety mechanism in the age of agentic AI. 
Rather than allowing AI agents to rewrite and deploy code, Dynatrace envisions them operating within guardrails by adjusting configuration settings through feature flags. This approach limits risk while enabling faster, automated decision-making. Customers, Reitbauer noted, are increasingly comfortable with AI handling defined tasks under constraints, but not with agents making sweeping, unsupervised changes. By combining AI with controlled configuration tools, Dynatrace aims to create a safer path toward truly autonomous operations. 
Learn more from The New Stack about the latest in progressive delivery: 
Why You Can’t Build AI Without Progressive Delivery 
Continuous Delivery: Gold Standard for Software Development 
Join our community of newsletter subscribers to stay on top of the news and at the top of your game.