Description
Infrastructure for the Agentic Era
Overview
AI-driven workloads are exposing foundational gaps in traditional infrastructure tooling. Their demands for agility, scale, and policy-aligned governance are outpacing what conventional cloud management platforms (CMPs), infrastructure as code (IaC) frameworks, and release automation tools can deliver.
This report provides a structured lens through which to evaluate the maturity and suitability of tools for AI workload orchestration. Drawing from enterprise challenges and market patterns, we outline key findings, make forward-looking recommendations, define essential capabilities, compare tool categories, and finally, examine how infrastructure platforms for engineering (IPEs) meet these demands.
Key Findings (Observations)
- AI Workload Dynamics Break Traditional Automation: Infrastructure tools built for static environments can’t handle GPU variability, iterative model tuning, or data-intensive training/inference cycles.
- IaC Alone Is Not Enough: While IaC defines environments, it doesn’t orchestrate lifecycle events, cost controls, or runtime governance, critical for dynamic AI operations.
- CMPs and CI/CD Tools Lack AI Context: These tools excel at abstraction or delivery pipelines but lack GPU-awareness, policy enforcement, or dynamic resource scaling.
- Agentic Orchestration Is Emerging: AI-native orchestration platforms that adapt to workload conditions, apply policies in real-time, and optimize resource usage are setting a new benchmark.
- The Real Cost Isn’t Infrastructure—It’s Delay: Inefficient orchestration leads to lost experimentation time, delayed model deployment, and underused GPU resources, costs that eclipse infrastructure spend.
Recommendations
- Rethink platform investments with AI workload orchestration in mind, not just infrastructure provisioning.
- Move toward dynamic, policy-based, and event-driven environments that support model training, tuning, and inference.
- Evaluate not just provisioning speed, but runtime governance, GPU yield, and integration depth.
- Standardize on platforms that support hybrid deployments and AI workload intelligence.
- Include platform engineering control planes (IPEs) in bake-offs alongside CMPs and DevOps stacks.
Critical Capabilities for AI Workload Orchestration
- GPU-Aware Provisioning – Automatically match compute to model type and workload phase (training vs inference).
- Dynamic Resource Management – Adjust environments in response to load, usage metrics, and policy triggers.
- Policy-as-Code Enforcement – Enforce runtime constraints (duration, cost, access) natively across clouds.
- Blueprint Reusability – Support templated, repeatable environments with inputs/outputs, metadata, and tagging.
- Self-Service Interface – Offer intuitive UI/API/CLI for developers, data scientists, and platform teams.
- Integration Extensibility – Native CI/CD, GitOps, ITSM, and AI ecosystem integrations (e.g., Jupyter, MLflow).
- Cost Visibility & Optimization – Real-time cost tracking and shutdown automation for underutilized GPUs.
Capability Comparison Across Tool Categories
| Capability | IaC Tools | Config Managers | CMPs | Release Orchestration | Infra. Platform Engineering (IPE) |
| GPU-Aware Provisioning | 1 | 1 | 2 | 1 | 5 |
| Dynamic Resource Management | 1 | 2 | 2 | 3 | 5 |
| Policy-as-Code Enforcement | 1 | 2 | 3 | 2 | 5 |
| Blueprint Reusability | 2 | 2 | 3 | 3 | 5 |
| Self-Service Interfaces | 1 | 2 | 4 | 3 | 5 |
| Integration Extensibility | 2 | 3 | 3 | 4 | 5 |
| Cost Visibility & Optimization | 1 | 1 | 3 | 2 | 5 |
Comparative Analysis of Tool Categories
- Infrastructure as Code (IaC) Tools: Emerging in the early 2010s with tools like Terraform and Pulumi, IaC revolutionized static environment creation by codifying infrastructure in declarative syntax. While essential for provisioning consistency and version control, these tools lack the orchestration intelligence needed for AI. They do not manage GPU-aware allocation, cost optimization, or runtime policy enforcement. Their focus is configuration, not contextual execution, making them ill-suited for dynamic, iterative AI pipelines.
- Configuration Managers: Popularized in the DevOps wave (e.g., Ansible, Chef, Puppet), these tools focus on the post-provisioning setup of servers and applications. Their scripting and templating power is valuable for standardized deployments, but they lack any awareness of AI workloads or cloud-native orchestration. Typically used by sysadmins or SREs, they operate with no insight into real-time GPU usage, runtime variability, or model lifecycle orchestration.
- Cloud Management Platforms (CMPs): CMPs like Morpheus and CloudBolt emerged to offer governance, cost tracking, and abstraction across cloud providers. While they improve infrastructure visibility and control, they are often monolithic, UI-driven, and slow to integrate with modern AI toolchains. CMPs lack real-time orchestration, AI awareness, and integration with training/inference-specific workflows. Their origins as governance overlays limit their agility.
- Release Orchestration Tools: Designed primarily for software delivery pipelines, tools like Harness and Spinnaker manage build-test-deploy cycles. They offer excellent pipeline visualization, rollout controls, and integrations with CI systems. However, they lack dynamic environment provisioning capabilities and GPU-awareness, making them weak fits for AI/ML use cases where environment orchestration is non-linear and data-dependent.
- Infrastructure Platforms for Engineering (IPEs): A newer category designed to bridge IaC and orchestration, IPEs ingest infrastructure code and apply agentic intelligence to environment lifecycle management. They are designed for platform teams building scalable, secure, and self-service infrastructure layers. IPEs such as Torque bring built-in AI/ML workload awareness, GPU-native provisioning, runtime policy enforcement, multi-cloud integration, and extensibility across DevOps and MLOps pipelines.
The Role of Torque
Torque stands apart by delivering a full-stack approach to environment orchestration, specifically tuned for the demands of AI-driven workloads. It bridges the static nature of IaC with the dynamic requirements of runtime orchestration.
By transforming infrastructure code into governed, reusable blueprints with embedded policy and telemetry, Torque allows AI environments to be launched, scaled, optimized, and retired based on real-time signals. Its agentic architecture enables autonomous actions, whether scaling up GPUs for training jobs or shutting down idle inference endpoints to reduce cost. With native integration into CI/CD tools, ITSM platforms, and data science ecosystems, Torque empowers platform teams to provide self-service, compliance, and operational excellence. It is not a provisioning tool, it is an execution layer for AI infrastructure strategy, engineered for scale, speed, and intelligence.
Evaluation
Critical Capabilities: AI Workload Automation
Introduction: How to Use This Framework
AI-driven workloads demand agility, scale, and governance beyond what traditional infrastructure tools can provide. GPU variability, iterative training, inference cycles, and cost-intensive resources expose gaps in Infrastructure as Code (IaC), Cloud Management Platforms (CMPs), and release automation tools. To succeed, organizations need platforms that orchestrate AI workloads dynamically, enforce policy continuously, and optimize resource usage.
This framework enables enterprises to:
- Identify gaps in AI workload orchestration.
- Measure maturity across key orchestration capabilities.
- Understand business value tied to orchestration readiness.
- Evaluate readiness to support AI-driven innovation at scale.
Each capability includes a description, measurement criteria, expected business results, and a 1–5 maturity scale.
Critical Capabilities for AI Workload Orchestration
GPU-Aware Provisioning
- Description: Automatically match compute to model type and workload phase (training vs inference).
- Measurement Criteria: Are GPUs provisioned manually, with basic automation, or dynamically matched to workload needs?
- Business Value: Maximizes GPU utilization, reduces cost waste, accelerates training/inference.
Evaluation:
☐ 1 – None
☐ 2 – Manual selection
☐ 3 – Basic provisioning automation
☐ 4 – GPU-aware provisioning for major workloads
☐ 5 – Fully dynamic, policy-driven GPU provisioning
Dynamic Resource Management
- Description: Adjust environments in response to load, usage metrics, and policy triggers.
- Measurement Criteria: Are AI environments scaled manually, via fixed rules, or dynamically in real time?
- Business Value: Improves resilience, ensures performance, reduces resource waste.
Evaluation:
☐ 1 – None
☐ 2 – Manual scaling
☐ 3 – Rule-based adjustments
☐ 4 – Automated scaling with policy triggers
☐ 5 – Fully autonomous, real-time dynamic resource management
Policy-as-Code Enforcement
- Description: Enforce runtime constraints (duration, cost, access) natively across clouds.
- Measurement Criteria: Are cost, runtime, and security policies applied manually, partially, or continuously at runtime?
- Business Value: Prevents overspending, enforces governance, reduces compliance risk.
Evaluation:
☐ 1 – None
☐ 2 – Manual guardrails
☐ 3 – Detection only
☐ 4 – Policy-driven enforcement for select workloads
☐ 5 – Continuous, runtime enforcement across environments
Blueprint Reusability
- Description: Support templated, repeatable environments with inputs/outputs, metadata, and tagging.
- Measurement Criteria: Are AI environments built manually each time, or standardized into reusable templates?
- Business Value: Reduces setup time, increases consistency, enables governed reuse.
Evaluation:
☐ 1 – None
☐ 2 – Manual builds
☐ 3 – Partial reuse of templates
☐ 4 – Reusable blueprints for major workloads
☐ 5 – Enterprise-wide governed blueprint catalog
Self-Service Interface
- Description: Offer intuitive UI/API/CLI for developers, data scientists, and platform teams.
- Measurement Criteria: Do teams provision environments through tickets, scripts, or governed self-service portals?
- Business Value: Accelerates delivery, reduces friction, empowers diverse users.
Evaluation:
☐ 1 – None
☐ 2 – Ticket-based provisioning
☐ 3 – Script-driven access
☐ 4 – Limited self-service
☐ 5 – Full enterprise self-service across user roles
Integration Extensibility
- Description: Native CI/CD, GitOps, ITSM, and AI ecosystem integrations (e.g., Jupyter, MLflow).
- Measurement Criteria: Are integrations manual, partially scripted, or natively embedded?
- Business Value: Simplifies workflows, accelerates experimentation, ensures ecosystem alignment.
Evaluation:
☐ 1 – None
☐ 2 – Manual integrations
☐ 3 – Scripted connections
☐ 4 – Native integrations with select tools
☐ 5 – Fully extensible, ecosystem-wide integrations
Cost Visibility & Optimization
- Description: Real-time cost tracking and shutdown automation for underutilized GPUs.
- Measurement Criteria: Are GPU costs tracked manually, via reports, or monitored in real time with automation?
- Business Value: Reduces GPU overspend, ensures financial accountability, improves ROI.
Evaluation:
☐ 1 – None
☐ 2 – Manual reports
☐ 3 – Basic dashboards
☐ 4 – Real-time tracking + alerts
☐ 5 – Full automation with shutdown of idle resources
Summary: How to Evaluate Overall Capabilities
- Score Each Capability (1–5): Use the maturity scale for each capability.
- Calculate the Average: Add all seven scores and divide by seven.
- 1–2 = Reactive: Manual ops, high costs, limited AI orchestration.
- 3 = Transitional: Partial automation, inconsistent governance.
- 4 = Advanced: Policy-driven orchestration, reusable templates, strong integrations.
- 5 = Optimized: Continuous, dynamic, governed orchestration purpose-built for AI.
- Prioritize Gaps: Focus first on GPU provisioning, dynamic resource management, and cost optimization, as these drive the most immediate AI value.
- Strategic Goal: Reach 4–5 maturity to unlock scalable, governed AI workload orchestration that accelerates innovation and reduces cost.
This evaluation framework turns AI workload orchestration from a technical challenge into a strategic readiness model, helping enterprises measure their ability to support the agentic era of AI with speed, governance, and efficiency.
