Discover the Enterprise AI Technology Stack

Make the right vendor choices to sustainably, safely, and dynamically scale your agentic AI capabilities.

Analyst perspective

Agentic AI is a living system that must flex, adapt, and evolve.

As AI continues to transform organizations, the supporting ecosystem must evolve to sustainably and safely enable, adapt, and orchestrate agentic AI agents and capabilities. A successful technology stack extends beyond traditional components to include:

• Applications: Agentic AI-enhanced applications with autonomous capabilities that orchestrate, manage, and govern multiple AI agents.
• Data and AI Lifecycle Management Tools: Development tools purpose-built to streamline agentic workflows from prototype to production-ready autonomous systems.
• Foundation Models: Models selected to perform multistep reasoning, context retention, tool use, and autonomous error recovery in specific contexts.
• Data and AI Platforms: An underlying platform architected for real-time knowledge access, tool and action logging, and governance enforcement.
• Infrastructure: Hardware optimized to maximize inference performance and throughput while minimizing response latency for agentic workloads.

Unfortunately, the AI landscape shifts faster than any roadmap can predict. The most critical architectural decision an IT leader can make is building a technology stack designed not for today’s answers, but for tomorrow’s unknowns.

Bill Wong
AI Research Fellow, Research Development
Info-Tech Research Group

Andrew Kum-Seun
Research Director, Applications Research Development
Info-Tech Research Group

Executive summary

Your Challenge

• Organizations are rapidly moving from AI assistants that advise (e.g. chatbots) to autonomous agents that contextualize systems, trigger workflows, and make operational decisions. They are beyond task-based pilots.
• Early AI deployments are often assembled quickly with a mixture of models, tools, and technologies. Early and quick wins are traded off for architectural discipline and scalability.
• As agentic AI becomes more embedded in core processes and systems, it must meet enterprise expectations for reliability, security, governance, cost control, and vendor sustainability.

Common Obstacles

• Scaled agentic AI introduces new risks, such as integration brittleness, runaway costs, untrusted and stale data that organizations may not be capable of addressing.
• The agentic AI marketplace spans across a broad and expanding range of layers in the technology stack (from application to infrastructure) making it increasingly difficult to keep up-to-date and complex to assemble.
• Without a clear AI vision and architecture discipline, AI teams over-invest in the wrong places while under-investing in what enables scale (e.g. observability and integration reliability).

Info-Tech’s Approach

• Get familiar with the enterprise AI technology stack. Develop a foundational understanding of each layer in the stack and how they work together to enable intelligent and autonomous systems.
• Recognize the vendors in the marketplace. Learn the notable and commonly referenced vendors to discover what and who is shaping the marketplace.
• Prepare for vendor selection. Equip your teams with a structured way to evaluate and prioritize vendors and products by identifying key considerations, trade-offs, and questions to ask before moving into formal selection.

Info-Tech Insight

Agent demonstrations look alike, but operational realities do not. Sustainable, understandable, and trustworthy operations beat smarter models every time. The vendors worth betting on are the ones that make agents easy to observe, explain, debug, govern, and remove safely.

Organizations are beyond small-scale pilots

From prototypes to production-grade agentic AI

Organizations are rapidly shifting from chat-based assistants to autonomous agents that take actions and make decisions across enterprise systems. Early pilot successes are often achieved by quickly assembling task-based solutions using a mix of models, tools, and techniques. As agentic AI scales and spreads into and across critical workflows, it is expected to operate reliably, in compliance with regulations, be cost efficient, and have clear accountability.

• Early AI deployments are piecemeal and siloed, prioritizing speed to value over long-term viability.
• Enterprise-grade reliability, governance, and quality requirements become critical success factors that were not priorities in early pilots.

See our Design Your Agentic AI Prototype to learn more on agentic AI prototyping.

Scale makes the technology stack taxing

Architectural and vendor decisions that worked well in pilots begin to expose significant risks as agentic AI scales. Specific failure modes become more probable and impactful, such as unsafe actions, unauthorized access to sensitive data, brittle integrations, and runaway costs. Without robust controls and clear governance, organizations will struggle to manage agents as sustainable and secured systems.

• Runtime controls, durable and robust integrations and understandable agent explanations are required to reveal and proactively address risks across systems and teams.
• Overlapping and uncoordinated stacks lead to redundant capabilities, gaps in control, unnecessary vendor lock-in, and incomplete visibility of agent behavior and impact.

AI is a strategic priority

84% of organizations say they are looking to add more AI capabilities over the next three years. (Source: “State of Process Orchestration & Automation 2025.” Camunda, 2025; n=800)

Automations do not get easier as you scale

78% of organizations say complex workflow patterns and/or long-running processes are increasing the difficulty in automation. (Source: “State of Process Orchestration & Automation 2025.” Camunda, 2025; n=800)

Successful AI adoptions are aligned to strategic priorities and architectures

Understanding the business context is a must for all AI initiatives, especially when scaling. Map your initiatives to your strategic priorities to gain the stakeholder buy-in you need. Then, use these targets to design the architectures that maximize the value you are committed to delivering while minimizing organizational risks.

Capture your business priorities in your AI strategy and your technology vision with an AI architecture

AI Strategy

An AI strategy ties business needs with the AI capabilities and tools required to support them. Key components of the AI strategy include AI governance, data management, people, processes, and technology.

See Info-Tech’s Build Your AI Strategy and Roadmap for more information.

AI Architecture

An AI architecture is the structured framework that encompasses the components and processes necessary to develop, deploy, and manage AI systems. It aims to ensure compliance, interoperability, reliability, strategic alignment, and sustained value from AI investments.

See Info-Tech’s Define the Components of Your AI Architecture for more information.

Info-Tech Insights

Keep these points in mind as you explore and design your agentic AI technology stack:

• Application

  Configure or build your agentic AI applications so that they do more than just respond. They must translate user intent into autonomous, multistep workflows where planning, memory, and tool execution converge.

• Data & AI Lifecycle Management

  Prioritize the tools that streamline agent delivery through reusable components for prompt chaining, function calling, and agent evaluation. Your delivery toolchain should accelerate the transition from prototype to production-ready autonomous systems.

• Foundational Models

  Identify the smallest viable model capable of meeting your agent’s functional and nonfunctional requirements. Consider fit-for-purpose and mid-tier models to execute unique use cases and to optimize costs and performance.

• Agentic Execution & Orchestration Engine

  Architect your runtime engine so that it coordinates AI agents across different systems and technology stacks, synthesizes multimodal results, and continuously optimizes decision-making and workflow executions in real time. Observability and governance are central to ensuring agent alignment to broader strategic and operational goals.

• Data Platform

  Agents are only as intelligent as the data they can access. Ensure your data platform delivers real-time access to both structured and unstructured data and is architected to scale as agentic AI adoption accelerates across the organization.

• Infrastructure

  Ensure that your AI accelerators are inference optimized and your infrastructure can deliver low-latency, sequential reasoning steps and tool calls at scale and in various operational conditions.

Learn the capabilities, table-stake features, and vendors of each layer

• Application

  User-facing software that turns AI outputs into usable actions, decisions, and information.

• Data & AI Lifecycle Management Tools

  Toolchain used to build, test, deploy, and monitor AI capabilities throughout their entire AI lifecycle.

• Foundational Models

  Pre-trained models that serve as the base intelligence layer that is adapted and trained to function in specific or general contexts.

• Agentic Execution & Orchestration Engine

  The environment that coordinates how AI plans tasks, invokes tools, manages state, and executes actions across systems.

• Data Platform

  The supply of trusted, governed, structured, and unstructured data that AI uses to reason and act effectively.

• Infrastructure

  The compute, storage, and network foundations that power platforms, model training and inferences, and application workloads.

Make the right vendor and product decisions

Create vendor selection criteria that best enables you to deliver your current and future high-value AI use cases.

Selection Criteria Considerations

• Functional Use Cases – AI capabilities that support your high-priority business capabilities, processes, and tasks.
• Operability & Production Reliability – The ability to reliably deploy, run, monitor, recover, and evolve the technology stack to reflect real enterprise scenarios.
• Deployment Compatibility & Flexibility – The fit of the product with the organization’s deployment models (e.g. cloud) and regulatory requirements without architectural, functional, or technical compromises.
• Governance, Security & Compliance – The extent to which the product enforces budget caps, permissions, policies, oversight, and transparency controls so that agent actions are safe, sustainable, compliant, explainable, and visible.
• Integration & Ecosystem Fit – The robust and flexible connections of the product with new and existing platforms, data sources, tools, and standards, minimizing any regressions in agent and system performance.
• Implementation & Operational Costs – The initial and ongoing expenses required to run, monitor, scale, and maintain the product in production.

Info-Tech’s Research

FEATURED RESEARCH:
Build Your AI Solution Selection Criteria

FEATURED RESEARCH:
The Rapid Application Selection Framework

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