From AI Experiments to Enterprise-Grade Agentic Operations
Enterprises are rapidly shifting from isolated AI pilots to production environments where AI agents execute real work across IT and engineering. This transition is forcing vendors to build robust AI operations infrastructure that connects large models, enterprise data and deterministic automation. Instead of chatbots running in sandboxes, organizations now want AI agents production-ready: governed, observable and integrated with existing systems. A new generation of enterprise automation platforms is emerging to meet that demand, providing policy-driven orchestration, shared data layers and execution engines capable of handling autonomous workflows. These platforms aim to close the long-standing gap between AI experimentation and operational deployment by pairing intelligence with a hardened execution layer. The result is an AI stack where models and agents are treated as first-class services, subject to the same controls and reliability expectations as traditional IT, while still enabling rapid innovation in agentic AI deployment.
Red Hat Ansible as the Trusted Execution Layer for AI Agents
Red Hat is positioning Ansible Automation Platform as a central execution layer that links AI-generated decisions to concrete IT actions. The latest 2.7 release, combined with a new automation orchestrator in technology preview, allows teams to blend deterministic playbooks, event-driven automation and AI-driven workflows on a single canvas. Ansible’s Model Context Protocol server serves as a universal bridge between AI tools and automation, eliminating the need for brittle custom integrations and simplifying AI operations infrastructure. Organizations can inject their own knowledge into context-aware assistants, apply policy-driven governance and track automation impact through built-in dashboards. By integrating human oversight into complex workflows, Ansible enables AI agents to operate safely at enterprise scale rather than remaining experimental. This establishes a consistent control plane where agentic AI can trigger infrastructure changes, remediation tasks or service management actions, all under the same audited, policy-based framework used for traditional automation.
Red Hat AI 3.4 and the Rise of AgentOps in Hybrid Clouds
Alongside Ansible, Red Hat AI 3.4 introduces an AgentOps vision aimed at managing AI agents across hybrid cloud environments. The platform’s Model-as-a-Service capability exposes curated models through governed APIs, giving developers a consistent interface while allowing administrators to enforce policies and monitor consumption. This MaaS layer is paired with high-performance distributed inference using vLLM and the llm-d engine, plus request prioritization to balance interactive and background workloads. Red Hat frames its strategy around four pillars: efficient inference, tight coupling of enterprise data with models and agents, accelerated deployment and management of agents, and an integrated platform capable of running any model in any agent on any hardware or cloud. Together, these capabilities support large-scale agentic AI deployment by treating models and agents as managed infrastructure resources, with observability, governance and performance tuning built into the core stack rather than bolted on later.
Xurrent’s Autonomous Agents for IT Service and Operations Management
Xurrent is extending its AI-powered service and operations management platform with autonomous agents designed to remove routine work from IT queues. Building on years of experience with Sera AI for classification, article drafting and ticket resolution, Xurrent’s new agents are positioned as digital team members capable of handling tickets end-to-end, from triage through closure, with human-defined guardrails and optional sign-off. The platform’s single-governed architecture underpins this agentic AI deployment: a shared policy and data layer unifies governance, visibility and security across workflows. Every agent, whether built by Xurrent or the customer, sees the same service catalog, data model and operational rules. An open Model Context Protocol server connects Xurrent to external AI models from any provider, preventing lock-in and enabling flexible AI operations infrastructure. This combination of governance, auditability and open connectivity allows autonomous workflows to run safely in IT service and operations environments.
Rescale’s Agentic Digital Engineering for AI-First Product Development
Rescale is bringing agentic AI to digital engineering by embedding simulation-native agents directly into R&D workflows. Its platform unifies previously siloed simulation, data and AI tools, giving engineering teams a single environment for AI-first product development across sectors such as aerospace, automotive and life sciences. The new agents automate tasks like input validation, troubleshooting, report generation and hardware selection, while keeping engineers in the loop through an agent library, deployment framework and workflow builder. Organizations report fewer simulation errors and less wasted compute, with engineers spending less time on manual setup and error resolution. Rescale also extends its AI physics operating system into an end-to-end environment for turning simulation data into production-ready surrogate models. By integrating data structuring, training, validation and deployment, the platform creates a continuous path from high-fidelity simulations to operational AI agents, aligning digital engineering pipelines with production-grade AI agents production standards.