Red Hat makes Ansible the 'Trusted Execution Layer' for Agentic AI

"The Automation Orchestrator enables combining task-based automation (such as server patching), event-driven responses to observability signals, and AI-driven recommendations into a single workflow. Regardless of the origin of an action-whether it's a human, an event, or an AI agent-everything goes through the same RBAC (Role-Based Access Control) checks, the same approval mechanisms, and the same audit trail."

"An AI agent can analyze a situation and recommend an action, but it is always a pre-approved, human-curated playbook that is actually executed on the infrastructure. The level of trust in AI determines how much human approval is required. In a development environment, that step can be skipped; in production, it remains in place by default."

"The platform now positions itself as a 'trusted execution layer' where AI agents and existing IT automation converge. Not as a replacement for existing workflows, but as the connecting layer that translates AI insights into concrete, auditable actions."

"Another addition is the Model Context Protocol (MCP) server for Ansible. This allows third-party AI tools and Red Hat's own AI tools to communicate directly with the platform without custom integrations. Teams inject organization-specific policies and technical best practices into the RAG pipeline, ensuring that AI responses are tailored to their own environment."

Ansible Automation Platform 2.7 introduces an Automation Orchestrator in tech preview that unifies task-based automation, event-driven responses, and AI-driven recommendations into one controlled workflow. Actions originating from humans, observability events, or AI agents pass through the same role-based access control checks, approval mechanisms, and audit trail. AI agents can analyze situations and recommend actions, but execution relies on pre-approved, human-curated playbooks. Human approval requirements vary by trust level, with approvals skippable in development and retained by default in production. The platform also adds a Model Context Protocol (MCP) server to let third-party and Red Hat AI tools communicate directly, while teams inject organization-specific policies and best practices into the RAG pipeline.

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