agentic AI
agentic AI on Beyond Market Intelligence: a running collection of 9 stories we have gathered and hand-picked because they are worth your time. Every post here touches on agentic ai in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around agentic ai, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
At VB Transform 2026, Zillow's engineering chief said AI ROI numbers only hold up if you measure before you build
At VB Transform 2026, Zillow's engineering chief, Toby Roberts, underscored a critical lesson for enterprise AI: establish measurement baselines *before* implementation. Zillow’s experience revealed that context, not just raw data, presents the most significant challenge when building AI architecture to support customers navigating complex real estate transactions. Their solution—a persistent context layer—demonstrates the value of owning this layer, alongside partners like Glean, to streamline workflows and optimize costs by leveraging smaller, task-specific models.
Complete Guide to Thinking Machines Inkling
Thinking Machines Lab’s Inkling represents a significant advancement in AI foundation models. This open-weights model, boasting 975B parameters and a 1M-token context window, prioritizes adaptability over benchmark scores. Designed as a customizable base for diverse applications—from multimodal reasoning and agentic AI to coding and audio-visual tasks—Inkling empowers developers to build specialized solutions. Explore the complete guide to understand Inkling's architecture and potential. For broader context on the evolving AI landscape, consider "What to watch for after Jensen Huang’s Japan visit."
Capital One releases VulnHunter, an open-source AI tool that finds software flaws before hackers do
Capital One has released VulnHunter, an open-source AI security tool designed to proactively identify and remediate software vulnerabilities before they can be exploited. Built internally and now available on GitHub, VulnHunter employs an "attacker-first forward analysis" and a built-in falsification engine to pinpoint exploitable code paths and suggest fixes—a departure from traditional vulnerability scanners. This move represents a significant evolution for Capital One, demonstrating a commitment to open-source collaboration as a cornerstone of its cybersecurity strategy.
Intuit scrapped its own AI agent architecture twice in four months. At VB Transform 2026, its AI VP called that the fast path
Intuit’s journey with agentic AI highlights a crucial truth: rapid iteration is essential. The company initially built a fleet of specialist agents, then pivoted to an orchestration layer, only to rebuild the entire architecture within 60 days after encountering limitations in context retention. This experience, shared at VB Transform 2026, underscores the challenges of scaling agent-based systems and the importance of prioritizing customer outcomes. As Brex demonstrated, observing agent behavior can be a powerful tool in policy creation.
Cloud Native Infrastructure Emerges as the Foundation for Trustworthy Agentic AI
A new technical analysis from the Cloud Native Computing Foundation (CNCF) establishes a clear direction: the future of agentic AI rests on the robust foundation of cloud-native infrastructure. Rather than requiring entirely new systems, agentic AI will leverage the mature ecosystem already powering distributed applications. This approach prioritizes stability and scalability. Explore how existing cloud-native technologies empower the next generation of AI. For further insights into the operational challenges of deploying AI agents, see our coverage from QCon AI Boston.
5 FREE Resources on Agentic AI
Ready to explore the rapidly evolving world of agentic AI? We've curated 5 free resources to accelerate your learning journey, empowering you to build more intelligent and autonomous systems. From foundational concepts to practical applications, these resources offer accessible entry points for anyone seeking to harness the power of AI agents. Discover how to move beyond simple prompts and begin building robust platforms.
Zero trust must now move at agent speed
The rapid adoption of AI agents demands an immediate shift in security strategy: zero trust architecture must now operate at agent speed. As Andre Durand, CEO of Ping Identity, explains, the compressed risk timeline necessitates continuous verification of every action, moving beyond traditional login checks. Enterprises must equip agents with individual identities, enforce policies deterministically, and establish frameworks for reviewing AI-generated output—lest they risk accumulating exposure through thousands of rapid requests. For deeper insights into this evolving landscape, explore "Ultrahuman’s former hardware VP raises $5.
Thinking Machines open sources first multimodal language model, Inkling, focused on low cost and 'resistance to censorship'
Today, Thinking Machines released Inkling, its first major language model under a permissive Apache 2.0 open-source license, offering enterprises a powerful new option for agentic AI workloads. This 975-billion-parameter, natively multimodal model distinguishes itself with a novel "controllable thinking effort" mechanism, balancing cost and performance. While not state-of-the-art across all benchmarks—GLM 5.2 leads in reasoning—Inkling excels in software engineering and demonstrates remarkable resistance to censorship.
'We have maybe 20 months' to rebuild for AI agents, Meta's infrastructure VP tells VB Transform 2026
The shift to agentic AI demands immediate infrastructure transformation. Meta VP of Engineering Barak Yagour, speaking at VB Transform 2026, highlighted a critical timeframe: “We have maybe 20 months to rebuild the whole thing for a world where humans and agents co-create at scale.” Automated traffic now surpasses human traffic, reshaping foundational assumptions about data consumption. Meta is prioritizing agent-aware infrastructure, focusing on dynamic controls, robust identity management, and accelerated data velocity—a flywheel effect driving innovation across agents, data, and recommendations.