Intelligent Capacity Orchestration: From EDA to Orchestrated Automation

AIOps Use Case

Pattern guide, not a partner integration.

Examples may reference hybrid cloud and virtualization platforms. Adapt inventory and metrics sources to your environment.

Overview

Capacity issues often emerge across systems and environments before any single dashboard breaches a threshold. Teams need continuous collection of utilization and performance data, AI-assisted identification of emerging constraints, and orchestrated responses (add capacity, reclaim capacity, or publish a capacity plan).

Challenge: Siloed capacity planning and reactive firefighting when growth or failure domains shift.

Solved with AI + automation: Automation continuously collects utilization, performance, and capacity data. AI correlates signals across services and environments. Emerging constraints are identified before service impact; governed workflows execute approved capacity changes.

Business outcomes: Fewer outages from exhaustion, predictable scaling, aligned capacity and demand plans.

Buyer question:

How do we anticipate capacity needs across hybrid environments?

Background

Traditional monitoring answers “is this host red?” Capacity orchestration answers “will this environment still meet its design intent next month?” Entry points include scheduled capacity reviews, ticket-initiated expansion requests, and event-driven alerts on utilization trends.

Solution

EDA + AAP for metric collection, trend reporting, and execution of approved scale-out or reclamation playbooks
AI for cross-system correlation and capacity risk narratives
AO for multi-step plans: gather dependencies, generate recommendations, approval, execute, validate

Who Benefits

Persona	Challenge	What They Gain
Capacity planner	Spreadsheet-driven forecasts	Live data and automated plan drafts
Platform engineer	Manual coordination across teams	Orchestrated, auditable capacity changes
Service owner	Surprise saturation events	Early warning and governed response

Prerequisites

Ansible Automation Platform 2.5 or later
Inventory and metrics accessible to playbooks or MCP tools
Automation orchestrator for multi-stage capacity workflows (Walk maturity)

EDA to AO adoption path

Coming soon:

Detailed stages (read-only capacity dashboards, AI risk summaries, orchestrated add/reclaim flows) are in development.

Stage 1: EDA + AAP only

Scheduled fact collection and threshold-based job templates.

Stage 2: AI enrichment

AI-generated capacity plans and risk summaries attached to tickets or reports.

Stage 3+: Automation orchestrator

User- or schedule-initiated workflows: dependencies, capacity plan generation, approval, execution.

Validation

Symptom	Status
End-to-end scenario tests	Content in progress