GreenLake Intelligence is not just AI-ready — it's AI-operational. A mesh of intelligent agents collaborate, learn, reason, and act in real time across your hybrid estate, continuously optimizing, enforcing policy, and troubleshooting proactively, with humans in the loop.
Enterprise IT has reached a tipping point: as organizations scale across hybrid, multicloud, and edge environments, infrastructure complexity has outpaced human capacity. Static dashboards, disconnected tools, and reactive processes can no longer keep up. GreenLake Intelligence introduces a new paradigm — an agentic Artificial Intelligence (AI) architecture purpose-built for autonomous, enterprise-scale operations.
| Target workload | Autonomous hybrid cloud operations — agentic Artificial Intelligence for IT Operations (AIOps) across data center, cloud, and edge |
|---|---|
| Infrastructure | The HPE GreenLake platform plus HPE and third-party compute, storage (including HPE Alletra Storage MP), and networking |
| Interface / control | GreenLake application programming interfaces (APIs) — RESTful, secured with OAuth 2.0 and role-based access control (RBAC) |
| Key advantage | A mesh of expert AI agents that reason, collaborate, and act in real time — with human-in-the-loop governance and full transparency |
Traditional Artificial Intelligence for IT Operations (AIOps) focuses on monitoring, analyzing, and automating with machine learning and pattern recognition. Agentic AIOps goes further — adding autonomy, contextual awareness, and proactive decision-making.
| Dimension | Traditional AIOps | Agentic AIOps (GreenLake Intelligence) |
|---|---|---|
| Approach | Predefined workflows, rules, and historical data | Autonomous, goal-driven intelligent agents |
| Behavior | Monitor, analyze, and react to anomalies | Dynamically assess, adapt to real-time change, and act |
| Decision-making | Pattern recognition | Contextual autonomy and proactive decisions aligned to goals |
| Learning | Static models | Self-learning, task prioritization, cross-system collaboration |
| Human role | Human-driven coordination across teams | Human-in-the-loop with operator-defined autonomy and guardrails |
Four layers work together — humans interact through adaptive copilots; reasoning large language models (LLMs) drive multiagent orchestration; a mixture of domain expert agents acts on live telemetry; and an AI gateway enforces governance.
| Layer | Components & role |
|---|---|
| Human interaction | Adaptive copilots — natural-language interaction, oversight, and operator-defined autonomy |
| Reasoning & orchestration | Reasoning LLMs + multiagent orchestration — chain-of-thought across HPE and third-party agents with shared context, memory, and intent |
| Mixture of experts | Compute, storage, networking, platform, observability, orchestration, resiliency, and future agents (HPE and third party) |
| Governance | AI gateway / guardrails — identity- and context-aware authentication of agent objectives and interactions |
Operationalize collective intelligence at scale — without sacrificing trust, transparency, or control.
Analysts and engineers define high-level goals, constraints, and escalation protocols that agents interpret during decision cycles.
Open industry protocols — including the Model Context Protocol (MCP) — standardize collaboration across HPE and third-party agents such as ServiceNow, Salesforce, and PagerDuty.
Agents make intelligent decisions from real-time infrastructure metrics and business logic, on-array, in the data center, or at the edge.
Agents can proactively request compute, storage, or network resources — or adjust fidelity — to cut cost, optimize performance, and hold service-level agreement (SLA) alignment.
Different goals can spawn different expert agents — designed to adapt to today's goals and future ones not yet known.
Built-in governance and explainability help meet regulatory and ethical requirements as AI takes on mission-critical operations.
MCP is a framework that ensures AI models operate effectively by providing task-specific context — input parameters, user intent, or environmental data. It helps models adapt dynamically, align outputs with objectives, and stay relevant, improving accuracy, transparency, and reliability in AI-driven workflows.
Expert agents are LLM-based, tuned to a task category, and fed domain-specific data — delivering value across day-0, day-1, and day-2 operations. Initial priority use cases:
Autonomous provisioning and workload balancing across cost, performance, and security parameters.
AI-driven workload placement, sustainability tuning, and capacity forecasting.
Agents coordinate real-time root-cause analysis with embedded observability.
HPE Aruba Networking Central leverages GreenLake Intelligence for security-first, AI-powered networking. In this example, unacceptable latency is impacting Slack for users at a site — a coordinated set of expert agents diagnoses and remediates it, with operator approval.
GreenLake Intelligence inherits the security, API frameworks, and validated partner ecosystem of GreenLake. GreenLake APIs use RESTful principles with OAuth 2.0 authentication and role-based access control (RBAC), so developers and independent software vendors (ISVs) can automate operations and build their own agents or copilots — in security, data protection, operations, and more.
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