Agents as Infrastructure: Why Your IT Team Now Needs a New Playbook

What changes when an agent goes live

Deploying your first AI agent is not a software release in the conventional sense. It is the start of a new infrastructure category inside your organisation, one that competes for compute, credentials, and policy attention alongside the applications your IT team already operates. Understanding that distinction before you deploy saves you from retrofitting governance onto a system that was never designed to support it.

If you are still mapping out the basics, the pillar what it actually takes to get your first AI agent live is the right starting point. This article focuses on the infrastructure implications that follow once you decide to move forward.

The numbers suggest most organisations underestimate the gap. 83% of organisations say they need infrastructure upgrades to support production-grade agentic AI, and only 11% of enterprises have an agent running at genuine scale. The infrastructure work is where pilots stall.

Why agents do not fit your existing playbook

Compute consumption is unpredictable

A web application spins up a known number of containers under a known load pattern. An agent decides at runtime how many tool calls to make, whether to spawn sub-agents, and how long to reason before returning a result. That means a single agent session can consume a few tokens or many thousands, depending on what it encounters.

Rakuten’s Claude Code agent runs autonomous coding sessions of up to seven hours. During that window, it is continuously consuming compute, making decisions, and writing output without a human in the loop. Your existing auto-scaling policies, cost allocation tags, and chargeback models were not designed for a workload with that profile.

This is why a dedicated runtime governance layer is not optional. You need something that can enforce token budgets, rate limits, and cost ceilings while the agent is running, not after the invoice arrives.

Credentials behave differently

Traditional service accounts hold a fixed set of permissions tied to a specific function. An agent navigating a multi-step task may need to authenticate to a database, call an external API, read from a file store, and write back to a ticketing system, all within a single session. It may also need to store and reuse those credentials across steps.

That creates a credential surface that does not map onto your existing secrets management tooling. SerenDB’s recent release of a password manager built specifically for AI agent credential handling reflects how clearly this gap has become visible to the market. Standard vaults were designed for humans and static services. Agents need scoped, session-bound credential flows that expire when the task ends.

The AI security risks that matter most here are not exotic. They are the ordinary ones: over-privileged credentials, long-lived tokens, and secrets that persist in agent memory beyond their intended scope.

flowchart TD
    A[Agent receives task] --> B{Credential needed?}
    B -- Yes --> C[Request scoped token from vault]
    C --> D[Token bound to session + task scope]
    D --> E[Agent executes tool call]
    E --> F{Task complete?}
    F -- Yes --> G[Token revoked]
    F -- No --> E
    B -- No --> E

Policy enforcement moves to runtime

With a conventional application, you review permissions at deployment and enforce them through network controls and IAM policies. Those controls are largely static. An agent can decide mid-task to call a tool you did not anticipate, write data to a location you did not explicitly permit, or escalate a request in a way that crosses a policy boundary.

Snowflake’s Cortex AI Gateway, released in July 2026, addresses this by placing a governance layer between agent fleets and the resources they consume, enforcing cost limits and routing rules while agents are running. The category Snowflake is building into is one that did not exist two years ago: runtime agent governance. Tools like Prefactor address the adjacent problem for teams outside the Snowflake ecosystem, evaluating what agents actually do at runtime, step by step, so the decisions static controls cannot anticipate are still caught as they happen.

Your AI governance framework needs to account for this. Policies written for pre-deployment review do not catch runtime decisions.

Resource contention is real and arrives faster than expected

AI agents are now recognised as a distinct infrastructure class that competes with applications for compute and policy resources. That is not a forecast. Organisations running agent fleets alongside production applications are already managing scheduling conflicts, GPU queue contention, and API rate limit collisions.

Zapier deployed more than 800 internal agents across its organisation, reaching an 89% AI adoption rate. At that scale, the infrastructure questions become identical to those you would ask about any distributed system: who owns the scheduler, how are resources allocated across teams, and what happens when two agents compete for the same upstream API capacity?

The multi-agent systems framing is useful here. Once you have more than a handful of agents, you are operating a fleet, and fleets need fleet management.

flowchart TD
    A[Agent fleet scheduler] --> B[Compute pool]
    A --> C[Credential vault]
    A --> D[Policy gateway]
    B --> E{Capacity available?}
    E -- Yes --> F[Agent runs]
    E -- No --> G[Queue or reject]
    D --> H{Policy check passes?}
    H -- Yes --> F
    H -- No --> I[Block and log]
    C --> J[Scoped token issued]
    J --> F

What your IT team needs to add

The platform engineer and CIO roles carry the most immediate burden here. The additions are concrete.

First, a credential management layer designed for agent sessions, not human logins. Scoped tokens, short lifetimes, and automatic revocation on task completion.

Second, a runtime policy layer that can enforce cost ceilings, block unexpected tool calls, and log agent decisions for audit. Your AI agent governance policy needs to specify what that layer must do, not just that one must exist.

Third, compute allocation that treats agent workloads as a distinct class. That means separate budgets, separate scheduling queues if you are running GPU-backed models, and chargeback models that can attribute cost to a specific agent, task, or team. Agent observability tooling makes this tractable: if you cannot see what an agent spent, you cannot manage it.

Fourth, an incident process that accounts for agent-specific failure modes: runaway loops, credential misuse, and unexpected downstream writes. 43% of IT leaders cite legacy API integration as their biggest agentic AI infrastructure gap, and most agent incidents trace back to a boundary the agent crossed that the team did not know was crossable.

None of this requires building from scratch. It requires adapting what you already operate, deliberately, before the agent fleet grows large enough to make retrofitting expensive.

Where to start

The infrastructure gaps described here are addressable, but they are easier to close before your first agent reaches production than after your fifth is already running. Work through the agent readiness assessment to identify which gaps apply to your environment and in what order to close them.