AI agents
AI agents on Beyond Market Intelligence: a running collection of 168 stories we have gathered and hand-picked because they are worth your time. Every post here touches on ai agents 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 ai agents, 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.
Why SAP says enterprise AI agents need knowledge graphs and governance
At VB Transform 2026, SAP’s Max McPhee highlighted a critical distinction: truly autonomous enterprise AI agents require more than general knowledge; they demand grounding in a company’s specific context. This stems from the need for agents to understand internal processes and terminology, achievable through knowledge graphs and robust governance. SAP’s decades of experience in process control, combined with recent acquisitions like LeanIX, are strategically positioning the company to empower organizations navigating this transformative shift—a shift underscored by insights into Google’s rapidly evolving AI search.
7 Steps to Building and Deploying Your First Autonomous Agent
Ready to unlock the potential of autonomous AI agents? This article provides a clear, 7-step guide to building and deploying your first agent, covering the entire process from initial concept to live operation. We’ll equip you with the practical knowledge to move beyond traditional spreadsheet workflows and embrace a future-focused approach to data management. For a deeper understanding of the infrastructure supporting these advancements, explore "Netflix Details Its In-House LLM Serving Platform with Triton and vLLM” and discover the evolving architectures necessary for agentic AI.
OpenAI’s new voice mode makes it to the ChatGPT desktop app
ChatGPT’s desktop app now features a transformative voice mode, bringing natural language interaction directly to your workflow. This innovation allows users to seamlessly engage with both ChatGPT and Codex, completing tasks and controlling agents through spoken commands. Experience a fluid, hands-free approach to data management and AI-powered assistance. Discover how this advancement expands the possibilities of agentic coding, as explored in our recent article, "Agentic coding goes hands-free." It’s a future-focused evolution designed to empower your productivity.
AegisAI, founded by former Google security execs, lands $36M to stop AI-driven spear phishing
AegisAI, founded by seasoned security experts from Google, has secured $36 million to address the escalating threat of AI-driven spear phishing. Their innovative approach centers on AI agents that mimic human analysis, meticulously examining each message for subtle anomalies often missed by traditional security measures. AegisAI's technology provides a critical layer of defense against increasingly sophisticated attacks. For broader context on the current AI funding landscape, explore our article on Corgi’s recent funding round.
The agent evaluation gap: Enterprise AI organizations have a reality-alignment problem, not a coverage problem — and most are shipping to production anyway
Enterprise AI organizations face a critical reality-alignment problem: an “evaluation gap” where increasing agent autonomy outpaces trust in the evaluations meant to govern it. A recent VentureBeat Pulse Research survey of 157 enterprises reveals that half have already deployed an agent that passed internal evaluations but then failed a customer. Despite this, two-thirds are moving toward fully automated deployments—highlighting a concerning disconnect. This research underscores the urgent need for evaluations that accurately reflect real-world outcomes, not just passing scores.
The agent security gap: 54% of enterprises have already had an AI agent incident, and most still let agents share credentials
More than half of enterprises (54%) have already experienced a confirmed agent security incident or a near-miss, revealing a concerning gap between AI agent autonomy and the controls designed to contain them. Across 107 organizations, agents are gaining access to sensitive systems while security lags, with only a third providing each agent a unique identity and limited isolation of high-risk agents.
Why Adding More AI Agents Made Our System Slower
Scaling AI agent systems isn’t always linear. We recently encountered a surprising bottleneck: asynchronous task management. As we expanded to hundreds of LLM agents, seemingly minor CPU tasks quietly became our largest performance constraint, slowing overall system speed. This post details how we identified and addressed this hidden cost, offering practical insights for anyone building complex AI workflows. Learn from our experience – a challenge we’ve explored further, alongside broader lessons from 8.5 years of machine learning.
The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix
Enterprise AI organizations face a critical challenge: a growing trust gap between confidently delivered answers and the reliability of underlying business context. A recent VentureBeat Pulse Research study, surveying 101 enterprises, reveals that over half (57%) have already experienced AI agents producing confident, yet incorrect, responses due to inconsistent data. This isn’t a retrieval problem alone; it highlights the urgent need for a governed semantic layer and a shift toward hybrid retrieval strategies to ensure data integrity and agent trustworthiness.
AI agents aren't confidently wrong because of bad context — they're wrong because of bad data engineering
AI applications are increasingly delivering confidently incorrect answers, not due to model flaws, but a critical gap in data engineering. These failures occur when outdated or incomplete data is retrieved and presented as authoritative, bypassing standard data pipeline checks. Addressing this requires a shift in focus—from pipeline completion to data correctness, freshness, consistency, and lineage. Prioritizing these four dimensions of data observability is the key to building truly trustworthy AI systems.
Presentation: From Copy-Paste to Composition: Building Agents Like Real Software
Jake Mannix’s presentation, "From Copy-Paste to Composition," addresses a critical evolution in AI agent design. Moving beyond rudimentary architectures, Mannix outlines a framework for building agents akin to robust software – versioned, encapsulated "virtual tools." This approach leverages an intermediate protocol layer to enable key capabilities like interface mapping and dynamic schema projection. Crucially, it incorporates runtime taint tracking to proactively mitigate data exfiltration risks, all while maintaining development velocity.
Kaggle + Google’s Free 5-Day Agentic AI Course
Google and Kaggle are democratizing access to the future of AI with a newly released, free 5-day course on Agentic AI. This transformative learning experience empowers users to build sophisticated AI agents, moving beyond simple prompts to create dynamic, task-driven systems. Explore the fundamentals and advanced techniques driving this innovative field, and discover how agentic AI can reshape workflows. For deeper insights into the architectural challenges and safety considerations, explore "Presentation: From Copy-Paste to Composition."
OpenAI unveils Presence, a new platform that lets enterprises launch and manage realtime voice agents and chatbots
OpenAI introduces Presence, a new enterprise platform designed to simplify the deployment and management of AI agents across business workflows. This offering empowers eligible customers to launch voice and chatbot agents capable of answering questions, accessing systems, and taking approved actions—all while adhering to company policies. Delivered through a limited general availability program with OpenAI Forward Deployed Engineers, Presence addresses the challenge of ensuring reliable agent behavior in production environments.
Anthropic Details How It Contains Claude Across Web, Code, and Cowork
Anthropic has outlined its robust containment architectures for Claude, emphasizing a critical shift in agent safety. Rather than relying on prompts, Anthropic focuses on deterministic limits imposed on an agent’s access to filesystems, networks, and execution environments. Detailed analysis of failures at trust boundaries and egress paths prompted significant design revisions. This approach prioritizes proactive security, demonstrating a future-focused commitment to responsible AI development. For further exploration of cloud AI security frameworks, see our article, "GKE Security Blueprint."
Glow emerges from stealth at $1.2B valuation to challenge endpoint security in the AI era
Glow emerges from stealth with a $1.2 billion valuation, poised to redefine endpoint security for the AI era. Recognizing a new class of risks stemming from the rapid integration of AI agents and developer tools within enterprises, Glow offers a focused solution. Traditional endpoint security struggles to adapt; Glow addresses these novel vulnerabilities head-on, empowering organizations to confidently navigate the evolving landscape. For deeper insights into the underlying AI models driving this shift, explore our recent article on Google's Gemini 3.6 Flash.
Detecting Vulnerabilities in Agent Skills with SkillSpector: From Green Checkmark to Real Security Judgment
Static analysis tools offer a first line of defense, but detecting vulnerabilities in AI agent skills requires more than just automated checks. Our latest post, "Detecting Vulnerabilities in Agent Skills with SkillSpector," explores this critical gap, highlighting how SkillSpector moves beyond simple “green checkmark” assessments. We demonstrate how static analysis can identify malicious skills while often over-flagging useful ones, revealing the crucial role of human judgment in making informed security decisions.
Google's Gemini 3.6 Flash model cuts AI agent token costs by up to 65% on long horizon engineering tasks —and 3.5 Pro is on the way
Google DeepMind has unveiled the Gemini 3.6 Flash model, engineered to significantly reduce AI agent token costs—cutting them by up to 65% on demanding long-horizon engineering tasks. Priced competitively at $1.50/$7.50 per million input/output tokens, it joins the Gemini 3.5 Flash-Lite ($0.30/$2.50) and specialized Gemini 3.5 Flash Cyber models, all designed to enhance speed, intelligence, and scalability. These advancements prioritize efficiency, streamlining workflows and empowering developers—a strategy mirrored in Weka's recent storage platform innovations. Gemini 3.5 Pro remains
Jack Dorsey is taking on Slack with Buzz, a group chat platform for teams and their AI agents
Jack Dorsey is challenging the established workplace communication landscape with Buzz, a new group chat platform designed for teams and their AI agents. Buzz uniquely integrates human collaborators alongside AI assistants within the same conversational space, streamlining workflows and fostering a future-focused approach to productivity. This innovative platform aims to redefine team interaction, offering a more accessible and efficient alternative. For context on related AI developments, explore our article, "OpenAI says Hugging Face was breached by its own pre-release models," for deeper insights.
GitLab 19.2 Puts AI Agents to Work on the Security Backlog
GitLab 19.2 introduces agentic automation to tackle the growing security and review backlog resulting from AI-assisted coding. This release directly addresses the challenge of maintaining code quality as AI tools accelerate development. Key features include Dependency Scanning Auto-Remediation, a streamlined Security Review Flow, and the GitLab Duo CLI, all designed to empower teams. Notably, Custom Flows enter public beta, offering unprecedented flexibility. For those exploring broader AI model management strategies, consider "Yelp Unifies ML Model Training with Training Orchestrator" for additional insights.
Natural raises $30M to reinvent payments for AI agents — and take on Stripe
Natural, a startup reimagining payments for the burgeoning world of AI agents, has secured $30 million in funding. This investment signals a significant shift towards specialized financial infrastructure designed to handle autonomous transactions. Natural aims to streamline the complex architecture underpinning AI-driven payments, positioning itself as a future-focused alternative. The company's approach contrasts with established payment processors, offering a more adaptable solution for AI’s unique needs.
How to Run Claude Code Agents for 24+ Hours
Unlock sustained coding productivity with Claude Code Agents running continuously – even for 24+ hours. This guide explores how to leverage these powerful AI assistants to streamline your engineering workflows and tackle complex projects with unprecedented efficiency. Discover practical techniques for maintaining and optimizing long-running agents, transforming your coding process. For a foundational understanding of setup and configuration, see "A Beginner’s Guide to Setting Up Claude Code for High Performance Agentic Programming" and elevate your agentic programming skills.
AI confidence just dropped 17 points in six months. That’s actually great news.
A recent JumpCloud survey reveals a 17-point drop in organizational confidence regarding AI deployment – a trend signaling progress, not setback. Organizations transitioning from pilot programs to production environments are demonstrating a realistic assessment of AI’s challenges, prioritizing governance and accountability. This shift, observed across 800 IT leaders, highlights the need for robust identity infrastructure and unified environments. Those prioritizing responsible AI practices are poised to lead the anticipated 84% expansion of AI use in IT operations over the coming years.
Am I focusing on the wrong skills as a CS student in the AI era? (Need brutally honest advice) [D]
The AI landscape is rapidly evolving, prompting a critical question for aspiring Computer Scientists: are current skill priorities still relevant? Your concerns about balancing traditional software engineering fundamentals—architecture, system design, and debugging—with the rise of AI are valid. While AI-powered code generation tools are advancing, a deep understanding of underlying principles remains paramount.
Podcast: Strands Agents with Clare Liguori
Welcome to the podcast! Today, Thomas Betts speaks with Clare Liguori, technical lead for the Strands Agents SDK, a rapidly evolving open-source project. The discussion charts Strands Agents’ progression from a Python SDK to a robust, production-ready agent harness. Clare shares valuable lessons gleaned from scaling agents, including the strategic shift to a model-driven architecture. As the underlying LLMs continue to advance, explore what's next for this transformative technology—a topic further illuminated in "Many Companies Use AI.
Many Companies Use AI. Few Know How to Build an AI-Native Enterprise Data Platform.
Many companies are leveraging AI, yet few possess a practical architecture for an AI-native enterprise data platform. Building one demands more than isolated AI tools; it requires a cohesive system. Our latest article explores a robust architecture featuring data agents for streamlined integration, AI-powered quality assurance, and essential AI governance. Discover how to move beyond experimentation and establish a foundation for scalable, reliable AI initiatives. For related insights on structuring data for AI agents, see Pinecone’s introduction of Nexus Engine.