The enterprise AI infrastructure landscape shifted noticeably on February 13, 2026, with announcements from AWS, Cisco, and Cloudera that collectively signal something important: the industry is done experimenting and is now building for production scale. The common thread across all three companies is reliability, governance, and operational maturity—the unglamorous but essential characteristics that separate deployments that actually work from impressive demonstrations.
AWS Goes All-In on Agentic
Amazon Web Services dropped a dense cluster of announcements that together paint a picture of where enterprise AI is heading. The Nova 2 model family—available in Lite, Pro, and Omni configurations—introduces what AWS is calling “Extended Thinking,” a capability for complex multi-step reasoning that goes beyond standard language model responses. The practical target is the kind of ambiguous, multi-constraint problem that currently requires human judgment to decompose before AI can engage with it.
Nova Forge is the more strategically interesting announcement. It enables organizations to build custom frontier models by blending their proprietary data with AWS’s model checkpoints—essentially letting enterprises customize AI capabilities on top of AWS’s foundational work without training from scratch. The economics of this matter enormously. Training a frontier model from scratch costs tens to hundreds of millions of dollars and requires expertise most organizations don’t have. Building on top of existing checkpoints with proprietary data is accessible to a much broader range of enterprises.
Frontier Agents represents AWS’s answer to the agentic AI moment. These are autonomous systems capable of handling multi-day software development and security tasks without continuous human oversight. The multi-day timeline is significant—most current agentic deployments handle tasks measured in minutes or hours. Systems that can maintain context, manage state, and execute reliably across days of work represent a meaningful capability expansion.
Bedrock AgentCore’s additions address the practical barriers that have kept agentic AI in pilot status for many enterprises. Episodic memory lets agents maintain context across sessions rather than starting fresh each time. Policy controls give enterprises the governance guardrails required before deploying autonomous systems in regulated environments. Multimodal embeddings extend agent capabilities beyond text to images, audio, and other data types. These aren’t headline features—they’re the reliability and control capabilities that make production deployment viable.
SageMaker’s serverless MLflow and Reinforcement Fine-Tuning additions reduce the DevOps overhead that has made ML operations expensive and specialized. Serverless MLflow in particular eliminates the infrastructure management burden that has been a persistent barrier for organizations that want to track and version ML experiments without dedicated MLOps teams.
The hardware story is significant. Project Rainier activates a half-million Trainium2 chip cluster—the largest AI compute cluster in the world. Trainium3 UltraServers deliver 4.4 times the compute and 4 times the efficiency of Trainium2, and they’re powering Anthropic’s million-chip scaling ambitions alongside Bedrock’s production workloads. The pricing implications are substantial: Trainium3’s efficiency gains create pressure on Nvidia’s H100 pricing, with hyperscalers reportedly seeing 50% cost reductions for comparable workloads. That pricing discipline ripples through the entire enterprise AI cost structure.
Cisco Bets on AI Infrastructure as the Differentiator
Cisco’s announcement focuses on a layer that gets less attention than models but matters just as much for enterprise deployment: the networking and orchestration infrastructure that connects GPU clusters, manages data flows, and keeps everything running reliably.
The Nexus HyperFabric AI for data center orchestration guarantees 99.99999% uptime across GPU clusters serving agentic workloads. That seven-nines reliability figure matters because agentic AI systems running multi-day tasks can’t afford the kind of infrastructure interruptions that are tolerable for batch processing or stateless inference. If an agent loses connectivity in the middle of a complex workflow, the recovery and reconciliation problem is significant.
Cisco’s AI Readiness Index—an assessment tool for enterprise infrastructure—addresses a real gap in how organizations approach AI deployment. Most enterprises discover their infrastructure limitations by deploying AI and watching things fail rather than by systematically assessing readiness beforehand. A structured assessment framework that identifies gaps before deployment reduces the expensive trial-and-error cycles that have characterized many enterprise AI initiatives.
Hypershield extending zero-trust security principles to ML pipelines closes a significant vulnerability. As AI models and agents gain access to sensitive enterprise data and systems, the security perimeter around those AI systems becomes as important as the security perimeter around the data itself. Existing zero-trust frameworks weren’t designed with ML pipelines in mind.
The Tomahawk 6 silicon powering 1.6 terabits-per-second switching is the infrastructure foundation that makes the software capabilities viable. Moving the data volumes that GPU clusters at Trainium and H100 scale require demands switching capacity that previous generations of network infrastructure couldn’t provide.
Cloudera Solves the Hybrid Data Problem
Cloudera’s 2026 CDP platform addresses a challenge that affects the majority of large enterprises: data that can’t, for regulatory, security, or practical reasons, all live in the public cloud. Healthcare records, financial data, intellectual property, and operationally sensitive information often needs to remain on-premises or in private cloud environments. Most AI tooling is optimized for fully cloud-native deployments.
Cloudera’s unified hybrid lakehouse brings together cloud and on-premises data under a single governance framework, with OpenUSD support for 3D and spatial data that’s increasingly relevant for manufacturing, architecture, and design applications. The agentic metadata management capability—letting AI systems understand and navigate data landscapes automatically—addresses the data discovery problem that makes building AI applications on large enterprise data estates so time-consuming.
CDP Copilot handling schema evolution and data lineage queries conversationally is a quality-of-life improvement with real productivity implications. Data engineers and analysts currently spend significant time on these operational tasks. Conversational interfaces that handle routine data operations reduce that overhead and make data infrastructure more accessible to people without deep technical expertise.
The 40% faster inference from Cloudera AI Runtime compounds across the scale at which large enterprises run AI workloads—the aggregate efficiency gain across millions of daily inference calls is substantial.
Strategic Microsoft interoperability targeting Fortune 1000 hybrid deployments reflects where most large enterprise AI actually happens: in environments that include Microsoft infrastructure, not in greenfield cloud-native architectures. Working with existing Microsoft tooling rather than requiring replacement is a prerequisite for enterprise adoption at scale.
Amazon Connect’s Operational AI
Amazon Connect’s real-time AI task overviews and recommended actions deserve mention alongside the more headline-grabbing announcements. Contact center AI that provides agents with real-time guidance and automates routine tasks represents one of the clearest current ROI cases for enterprise AI—the combination of reduced handle times, improved first-call resolution, and reduced training requirements produces measurable financial returns on predictable timelines.
SageMaker Automated Reasoning for validating chatbot implementations addresses a governance gap that has made enterprises cautious about deploying AI in customer-facing roles. Systems that can verify chatbot behavior against defined policies and flag unexpected outputs before they reach customers reduce the compliance and reputational risks that have kept AI out of regulated customer interactions.
The Convergence Story
What’s striking about February 13’s announcements is how coherently they fit together. AWS provides the model capabilities and compute infrastructure. Cisco provides the networking and security fabric that connects it reliably. Cloudera provides the data infrastructure that feeds it with governed, accessible enterprise data. Each company is solving a different layer of the same problem: making agentic AI deployable in real enterprise environments rather than controlled demonstrations.
The Intuit and Uber 1:30 human-to-agent ratios that have been widely cited as evidence of agentic AI’s practical value require exactly this kind of infrastructure maturity. Getting to those ratios demands reliable orchestration, persistent memory across sessions, policy controls that satisfy compliance requirements, data infrastructure that makes enterprise information accessible to agents, and networking that keeps everything running without interruption. None of these individual announcements delivers that on its own. Together, they’re assembling the stack that makes it possible.
The AWS AI Conclave in Bengaluru, running alongside these announcements, places this infrastructure buildout directly in the context of India’s AI ambitions—connecting the enterprise infrastructure conversation with the broader national AI strategy being showcased at the Impact Summit.
What This Means for Enterprise Decision-Makers
The practical implication of February 13’s announcements is that the “wait and see” posture toward enterprise AI is becoming increasingly costly. The infrastructure is maturing, the governance tools are arriving, and the reliability guarantees are becoming real. Organizations that have been holding back pending production-grade capabilities are running out of reasons to wait.
The commoditization of AI infrastructure—Trainium3’s cost pressure on Nvidia, serverless MLflow reducing DevOps overhead, hybrid lakehouse resolving data gravity—is reducing the capital and expertise barriers that made early AI deployment accessible only to well-resourced technology companies.
2026 is shaping up as the year the enterprise AI conversation shifts from “should we do this?” to “how fast can we execute?” The infrastructure announcements from AWS, Cisco, and Cloudera are answering the “how” question in increasingly concrete terms. The remaining variable is organizational—whether enterprises can move as fast as the infrastructure now allows.
