agentic AI
agentic AI on Beyond Market Intelligence: a running collection of 36 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.
How to Solve the Right Problem in the Age of Agentic AI
As agentic AI accelerates, the ability to define the *right* problem becomes paramount—and increasingly complex. Uncertainty in problem framing can lead to wasted resources and misdirected implementation. This framework offers a practical approach to proactively reduce that uncertainty, ensuring your AI investments deliver tangible value. Discover how to strategically pinpoint opportunities ripe for agentic solutions. For deeper exploration of related AI techniques, consider “Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply.”
5 Real-World Applications of Agentic AI in Enterprise Automation
Enterprise automation is undergoing a profound shift, and agentic AI is at the forefront. Discover five real-world applications transforming operations across critical departments: Site Reliability Engineering (SRE), finance, legal, migration, and security. These deployments leverage deterministic safety constraints, ensuring reliable and predictable outcomes. Explore how agentic AI empowers teams to streamline workflows and achieve greater efficiency. For deeper insights into the evolving AI landscape, see our recent article, "AI is redefining the workforce — and most planning models aren’t ready."
Anthropic's Claude Fable 5.1 and Mythos 5.1 arrive with a 75% cost reduction for Fable cache reads
Anthropic has released Claude Fable 5.1 and Claude Mythos 5.1, the latest iterations of its powerful large language models, alongside a significant 75% cost reduction for Fable cache reads. These models prioritize sustained problem-solving, demonstrating substantial improvements on benchmarks like Terminal-Bench and AutomationBench. Crucially, Anthropic is also introducing Enterprise Frontier Safeguards (EFS), allowing organizations to retain monitoring data within their own infrastructure. This release addresses evolving enterprise needs for capable, economical, and governable AI agents—a shift underscored by recent cybersecurity evaluations.
The three layers of agentic AI security: A defense-in-depth architecture for autonomous agents
Autonomous agents, capable of independent reasoning and action, introduce unique security risks that traditional application controls can’t address. Nutanix proposes a defense-in-depth architecture, structured across three critical layers: infrastructure, network, and control plane. This approach, detailed by Nutanix's Oscar Wahlberg, establishes a layered security posture, ensuring robust protection against everything from unauthorized access to runaway agent behavior.
Agentic AI Is Rewriting The Analytics Stack But There's One Skill It Still Can't Touch
Agentic AI is rapidly reshaping the analytics stack, automating tasks previously requiring significant human effort. However, a critical distinction remains: strategic oversight. While agents excel at execution, humans retain the irreplaceable ability to define nuanced goals and adapt to unforeseen complexities. Understanding where agent capabilities best align with human judgment—and why—is paramount for maximizing productivity and mitigating risk. As Gravitee highlights in "Enterprise AI's real risk isn't autonomous agents," managing the interactions *between* agents is key.
10 Essential Agentic AI Concepts Explained Simply
Agentic AI is rapidly gaining traction, yet the terminology can feel overwhelming. Don't let terms like "tool calling" and "agent loops" create confusion—the core concepts are surprisingly accessible. This post clarifies the 10 essential ideas driving this transformative technology, empowering you to understand and explore its potential. Discover how these foundational elements unlock a future-focused approach to AI. For further exploration of the AI landscape, see our recent coverage of Instinct’s impressive $350 million valuation.
Is Agentic AI Just Automation?
The rise of "Agentic AI" has sparked considerable excitement, but a critical question remains: is it truly transformative, or simply sophisticated automation? Many current agents operate as complex flowcharts, limiting their adaptability and problem-solving capabilities. This post explores why this architecture falls short and outlines a more effective approach to building genuinely intelligent agents. Delve deeper into maximizing coding agent performance with our guide, "How to Effectively Solve 100+ Tasks with Claude Code," for practical strategies.
Runable hits $21M to bet AI agents can go from building businesses to growing them
Runable, a platform focused on empowering AI agents to manage and scale businesses, has secured $21 million in funding. The company’s core proposition is enabling users to move beyond initial business building and into sustained growth through AI. Notably, Runable reports that 60%–70% of its substantial token usage—over 1 trillion tokens in the last 90 days—originates from paying customers, demonstrating early market traction.
Enterprises winning with AI agents are limiting how much the agents can do alone
Enterprises are discovering a critical truth about AI agents: unrestrained autonomy isn't synonymous with superior performance. While the initial focus was on maximizing agent independence, current deployments reveal that controlled, narrowly-scoped agents, coupled with strategic human checkpoints, are proving far more sustainable. Gartner forecasts that over 40% of agentic AI projects won't reach 2028, highlighting a widening gap between capability and responsible AI maturity.
Nvidia finds that simple linear math can replace costly AI model handoffs
Nvidia researchers have uncovered a significant inefficiency in agentic AI systems: the costly recomputation of conversation history when switching between models. To address this, they’ve introduced a cross-model KV cache transfer technique utilizing simple linear math, dramatically reducing compute costs and latency. Experiments reveal this method can be 2.7 to 25 times faster than traditional recomputation, retaining up to 98% of accuracy. This innovation paves the way for more efficient, long-horizon, multi-LLM workflows, as explored further in our article, "PagedAttention vs.
Cloudflare Cuts Astro Github Issues by 85% with AI Agents
Cloudflare significantly enhanced developer productivity by leveraging AI agents to manage GitHub issues, achieving an 85% reduction in processing time. This innovative application of agentic AI within GitHub Actions streamlines issue triage, automating workflows and accelerating software engineering cycles. Utilizing Cloudflare Workers and Flue, the system incorporates a “human-in-the-loop” approach, ensuring quality while maximizing efficiency.
One in five enterprises can't stop a runaway AI agent's spending in real time
Enterprise adoption of AI agents is revealing a critical shift: organizations are increasingly deploying multiple orchestration platforms—averaging three—to mitigate vendor risk and retain control. This trend, driven by concerns around security, permissions, and visibility, sees Microsoft AI Foundry/Copilot Studio leading usage, with Anthropic's Claude Platform gaining significant consideration. Notably, one in five enterprises still lacks real-time control over agent spending, highlighting the need for robust oversight as AI deployments evolve. Learn more about this emerging landscape with VentureBeat's coverage of Serval’s AI agent, Catalyst.
Presentation: From Models to Agents: Building Context-Aware Consumer AI at Scale at DoorDash
Sudeep Das, at DoorDash, reveals a powerful shift from traditional, isolated predictions to a scalable, agentic recommendation platform. This presentation, "From Models to Agents: Building Context-Aware Consumer AI at Scale," details their journey leveraging language-native memory and innovative techniques like RQ-VAE semantic IDs. Discover how grounded search dramatically improves relevance and conversion. For a deeper dive into the underlying workflow patterns, explore "RAG Workflow and Loop Engineering" to understand the principles driving this transformative approach.
5 Fun Agentic AI Papers to Read
If you’re seeking a foundational understanding of AI agents, prioritize these five papers—they represent a crucial starting point. Explore advancements in agentic AI, from core architecture to practical applications, with this curated selection. These papers offer concise insights into the evolving landscape, empowering you to navigate this transformative technology. For deeper coverage on the infrastructure supporting these agents, consider our article on Kubeflow’s recent technical updates and its path toward CNCF graduation.
Why Capital One built its multi-agent AI platform around open-weight models
At VB Transform 2026, Capital One’s Kel Vanee detailed the bank’s strategic shift toward building AI, not just using it. Capital One constructed a scalable, multi-agent AI platform centered around deeply customized open-weight models, leveraging proprietary data for enhanced accuracy and extensibility. This approach, underpinned by prior investments in data transformation and cloud adoption, enables the bank to optimize workflows, from fraud detection to customer service, and even automate internal infrastructure tuning.
Your agent didn’t hallucinate; it exceeded its authority
AI agents are rapidly transforming commerce, but a critical gap often emerges: separating technical capability from business authority. While content filters address safety, they don't dictate whether an agent is authorized to issue a refund, alter production systems, or commit the company to external actions. Enterprises must move beyond basic guardrails and establish explicit decision rights—defining what agents can execute, what requires approval, and what remains off-limits.
AI is exposing the limits of traditional network architecture
AI’s rapid expansion is exposing critical limitations in traditional network architectures, hindering performance, reliability, and cost-effectiveness. Legacy systems, designed for static traffic, struggle to support the unpredictable, always-on demands of continuous inference and agent communication. A recent Bloomberg study commissioned by Tata Communications revealed that while AI is a board-level priority, many enterprises operate on outdated infrastructure. To unlock the full potential of AI investments, organizations must evolve their networks into intelligent, adaptive platforms—a shift Tata Communications is actively enabling.
AI coding agents are blowing through budgets — Replit, Kilo Code, and Symbotic explain how they're managing it
The rise of AI coding agents presents a compelling evolution for development teams, though it's also sparking crucial conversations around budget management and responsible implementation. Leaders at Replit, Kilo Code, and Symbotic are navigating this shift, recognizing that while agents excel in greenfield projects, human oversight remains vital for complex brownfield environments. Kilo Code, for example, now supports over 500 models, demonstrating a move towards flexible, multi-model architectures—a strategy increasingly critical for optimizing both performance and cost.
Honest Abacus AI Review: ChatLLM, DeepAgent, AI Studio & More
Unlock the future of data management with our comprehensive review of Abacus AI. This all-in-one powerhouse seamlessly integrates over 100 AI models, autonomous agents, and a robust developer suite—all within a streamlined, cost-effective workflow. Designed for teams and power users, Abacus AI transforms complex tasks into intuitive processes. Discover how this platform empowers you to maximize productivity and innovation.
Companies are finally seeing AI ROI — and now they know how much more value it can deliver
Companies are finally realizing the substantial ROI of AI, and the SAP Value of AI Report 2026 reveals just how much further that potential extends. Based on a survey of over 2,600 business leaders, the report indicates AI now supports nearly one-third of organizational tasks, with ROI expectations significantly increasing. However, realizing this full potential hinges on strategic data governance—a challenge many organizations are only beginning to address. Explore the full findings and discover how to unlock transformative value with AI.
NTT DATA AIVista and Snowflake: Identity alone won’t secure enterprise AI agents
Recent VentureBeat research highlights a critical vulnerability: 69% of enterprises allow AI agents to share credentials, increasing security risks. NTT DATA AIVista CTO Mukesh Karki and Snowflake’s Mayank Upadhyay, presenting at VB Transform 2026, argue that securing AI agents demands more than just identity management. Enterprises require action-level authorization and tamper-resistant audit trails—essential for regulatory compliance and scalable, safe deployment of autonomous systems. Discover what’s next for AI, from the SaaS reckoning to the agent security gap, at TechCrunch Disrupt 2026.
GM redesigned its engineering workflows around AI agents — and tripled its merged pull requests
General Motors has fundamentally redesigned its autonomous vehicle engineering workflows around AI agents, yielding remarkable results. By shifting focus from simply adding AI coding assistants to automating broader processes—analyzing data, triaging issues, and running experiments—GM engineers now spend just 15% of their time writing code. This strategic shift has tripled merged pull requests, accelerating feature releases and significantly reducing defects.
MCP just got its biggest update ever — here’s what changes for AI agents
The Model Context Protocol (MCP), the connective tissue enabling AI agents to interact with software, has undergone its most significant update yet. This sweeping architectural revision, spearheaded by the Agentic AI Foundation (AAIF), a Linux Foundation initiative, introduces a fully stateless architecture, enhanced authentication, and formalized deprecation policies. This unlocks enterprise-grade scalability, allowing organizations to leverage AI agents with greater efficiency and security – a critical step toward wider adoption.
Article: An Evolutionary Architecture Pattern for Managing AI’s Pace of Change
Enterprise engineering leaders face a critical challenge: agentic AI disrupts the assumptions underlying traditional API gateways. Our new article, "An Evolutionary Architecture Pattern for Managing AI’s Pace of Change," explores the rise of AI Gateways as a vital architectural seam. Centralize guardrails, agent identity, and action policies within a single control plane to ensure platform stability and prevent costly incidents. Discover how this approach empowers predictable AI behavior while fostering innovation. For deeper insights into adaptable architectures, see "Clean Architecture for Serverless."