According to Gartner’s 2026 forecast, AI agent software spending is expected to grow from $86.4 billion in 2025 to $206.5 billion in 2026, representing a 139% year-over-year increase. This rapid growth in investment reflects rising enterprise expectations for AI agents. As organizations move beyond proof of concept (PoC) toward real-world deployment, the focus is also shifting from whether to adopt AI agents to how they can create business value.
Yet increased investment does not guarantee a smooth path to deployment. In many cases, the real challenges begin when AI agents move from PoC into production environments. An agent that performs well during testing may encounter a range of issues once connected to core enterprise systems and operational workflows, including identity, permissions, data access, and system governance. For CIOs and architecture teams, bridging the gap between PoC and production deployment is becoming a key challenge in enterprise AI agent adoption.
Table of Contents
- AI Agent Autonomy Is Changing How Enterprises Approach IT Governance
- Moving from PoC to Production-Ready Requires Overcoming Three Governance Hurdles
- Moving Toward Production-Ready Requires Key Governance Capabilities
- Scaling Up Brings the Next Stage of Cost Challenges
- From Architecture to Governance, Nextlink Helps AI Agents Achieve Reliable, Long-Term Operations
Table of Contents
- AI Agent Autonomy Is Changing How Enterprises Approach IT Governance
- Moving from PoC to Production-Ready Requires Overcoming Three Governance Hurdles
- Moving Toward Production-Ready Requires Key Governance Capabilities
- Scaling Up Brings the Next Stage of Cost Challenges
- From Architecture to Governance, Nextlink Helps AI Agents Achieve Reliable, Long-Term Operations
AI Agent Autonomy Is Changing How Enterprises Approach IT Governance
When these challenges arise, organizations may initially attribute them to model capabilities, assuming that a more capable model will improve agent performance. However, once AI agents enter enterprise environments, their ability to operate reliably depends not only on the model itself, but also on whether the existing IT architecture can support the way agents operate.
To understand this shift, it helps to look at traditional applications. Enterprise applications have typically operated according to predefined program logic: the inputs they receive, the processes they execute, and the data and systems they can access generally follow relatively clear rules and boundaries. As a result, long-established governance mechanisms—including identity and access management, data access controls, and audit logging—have largely been built around relatively predictable behavior and permissions that can be defined in advance.
AI agents introduce a different operating model. An agent can plan based on the task and its current context, then interact with different tools and enterprise systems to complete that task. Large language models (LLMs) provide the capabilities for understanding and reasoning, but enabling an agent to move from “answering questions” to “executing tasks” also requires supporting capabilities such as state management, memory, tools, permissions, and observability.
In this sense, an AI agent in an enterprise IT environment is more like a “digital worker” capable of executing tasks. Once an agent begins accessing enterprise data, using tools, or even acting on behalf of users, organizations need to consider how the agent should be identified, what it should be allowed to do, and how its task execution can be monitored.
These new governance requirements are becoming key considerations as AI agents move from PoC to production deployment.
Moving from PoC to Production-Ready Requires Overcoming Three Governance Hurdles
Hurdle 1: Unclear Identity and Access Boundaries
When an AI agent begins interacting with enterprise systems, the first question is: under whose identity should the agent access data and execute tasks?
If an agent simply inherits a user’s permissions, it may gain access beyond what is required for the task. If permissions are too restrictive, however, the agent may be unable to complete its work. Organizations therefore need to clearly identify the agent, define appropriate permissions and access boundaries based on the task, and incorporate the agent into existing identity governance mechanisms. As agents become increasingly autonomous, clearly defining “who they are and what they are allowed to do” becomes a fundamental step toward secure deployment.
Hurdle 2: Fragmented Enterprise Knowledge
The second question is: where should an agent obtain the enterprise knowledge it needs to complete a task?
In real-world enterprise environments, information is often scattered across different systems and documents. People generally know where to look for information and how to interpret what they find. For an agent, however, these fragmented sources do not automatically form a coherent knowledge source that it can readily understand and use.
Organizations therefore need to reorganize and connect their existing knowledge sources, transforming scattered information into a knowledge architecture that agents can effectively search, understand, and use. Enterprise knowledge management must evolve from a “document repository designed for people” toward a “knowledge architecture that agents can use.”
Hurdle 3: Lack of Observability
Once an agent can execute tasks autonomously, organizations face another question: if something goes wrong, can they determine where in the process the failure occurred? An agent task may involve model interactions, tool calls, data access, and multiple execution steps. Organizations therefore need visibility beyond the final output and into the task’s execution path. Model interactions, tool calls, and the points at which errors occur all need to be traceable and auditable.
Moving Toward Production-Ready Requires Key Governance Capabilities
As AI agents move from PoC into production environments, organizations need to consider the foundational capabilities required for production deployment, including security, reliability, identity management, and observability. In response to the three governance barriers discussed above, this article focuses on three key governance capabilities:
AI Agent Identity and Access Governance
Clearly identify the agent and establish least-privilege, auditable, and revocable authorization mechanisms based on the task, ensuring that agent actions remain within well-defined security boundaries.
Modernizing Enterprise Knowledge Architecture
Integrate fragmented knowledge sources and establish a knowledge architecture that is searchable, updatable, and accessible to agents for effective understanding and use.
AI Agent Observability
Establish end-to-end tracing across model interactions, tool calls, and task execution paths, enabling organizations to monitor agent operations and support further analysis and auditing.
Scaling Up Brings the Next Stage of Cost Challenges
Once AI agents have overcome these governance barriers and entered production, another question quickly emerges: How much does it actually cost?
To complete a single task, an agent may need to invoke models multiple times, retrieve data, use tools, and go through multiple planning and execution steps. Differences in task complexity, model selection, and execution paths can therefore result in different levels of resource consumption. As agent usage grows, organizations need greater visibility into where costs are incurred and whether those investments are generating corresponding business value.
This can be examined from three perspectives:
Moving from Aggregate Spending to Task-Level Cost Visibility
First, organizations need to understand how much model and compute resources are consumed by different agents and tasks in order to identify the most expensive parts of the process. In other words, cost management cannot stop at a single monthly bill. Organizations also need visibility into how much it actually costs to complete an individual task.
Scaling Compute Resources with AI Agent Demand
Second, organizations should avoid maintaining a large pool of idle resources simply to accommodate occasional demand spikes. Agent usage may change rapidly depending on the task and time, so the underlying architecture also needs to be elastic, allowing resources to scale up or down with demand and directing spending toward the periods when compute resources are actually needed.
Selecting the Right Model for Each Task
Finally, not every task requires the most capable—or most expensive—model. Simpler tasks can be handled by lower-cost models, while more complex reasoning tasks can use more capable models. When organizations use multiple models across cloud environments, establishing a consistent management approach becomes even more important, enabling them to continuously make informed choices across performance, cost, and business requirements. As AI agents expand from individual use cases into broader enterprise workflows, cost governance becomes an important foundation for long-term operations and scalable adoption.
From Architecture to Governance, Nextlink Helps AI Agents Achieve Reliable, Long-Term Operations
Nextlink has extensive experience in cloud managed services and multi-cloud management, with deep expertise in architecture governance and FinOps. As enterprises bring AI agents into core business processes and face increasingly complex governance challenges, Nextlink provides end-to-end support, from architecture assessments to managed operations.
As AI agents become embedded in core enterprise workflows, organizations need to understand how agents execute tasks, how costs flow through the environment, and how business value is generated. As agents expand from individual use cases toward broader adoption, cloud architecture and governance become an important foundation for reliable, long-term operations. Nextlink provides comprehensive AI application consulting and end-to-end implementation services, helping enterprises strike the right balance between business outcomes and technical architecture.
Contact Nextlink to establish comprehensive multi-cloud governance and turn AI investments into tangible business value.
