When Your Next Platform User Is an AI Agent

AI agents—software systems capable of interpreting goals, selecting tools and executing actions—are becoming direct users of internal developer platforms.

Unlike developers navigating a portal, agents consume services through APIs, use nonhuman identities and act according to machine-readable policies. They can request infrastructure, change configurations, investigate failures and initiate remediation without waiting for a person to perform each step.

However, that speed results in a different risk model, whereby platform teams must govern not only what an agent can access but which actions it can perform, in which environment, for how long and under whose authority.

Build a Machine-Facing Governance Layer

Platforms designed for developers typically concentrate on self-service workflows, standardized environments and reduced cognitive load. Those capabilities remain important, but an agent requires deterministic APIs, structured responses and policy decisions it can interpret without a graphical interface.

The platform also needs protections designed for machine-speed consumption, including quotas, rate limits, concurrency controls and idempotent operations that prevent repeated requests from creating duplicate resources.

“Complete auditability, strict guardrails, clearly defined identities, granular access controls, and human intervention at practical, risk-based checkpoints are all going to be critical,” says Ron Browning, CEO and co-founder of Dyna Software.

Agents should be able to stop when a policy denies an action rather than search automatically for another route. Platforms also need a kill switch capable of terminating an agent’s access immediately and rollback mechanisms for reversing its infrastructure changes.

Policy-as-code can give agents consistent, machine-readable answers about permitted actions while preserving central control. The internal developer platform effectively becomes the enforcement point between an agent’s intent and the systems that could execute it.

“A new-age platform’s role will be to ensure that every agent has the right access to the right tools and resources, for the right task, for only as long as it is required,” Browning says.

Give Every Agent an Owner and Expiration Date

AI agents should not be placed behind shared service accounts or treated as generic automation. Each agent needs a unique identity and an accountable human owner.

Its onboarding record should document its business purpose, creation date, intended systems, approved interactions, data access and risk classification. Platform teams should also record whether the agent can delegate work to another agent or workflow.

Those attributes can determine a standard permission profile. An agent that generates development environments, for example, may need access to a cloud API and infrastructure templates but no ability to modify production, view customer data or change security controls.

Identity governance must continue after onboarding. Agent permissions should be reviewed as applications, integrations and responsibilities change. Credentials should be short-lived, approvals should expire, and access should be removed automatically when the agent or task is no longer required.

“Reviews and expiry of approval are controls that can help make sure you do not miss something and create serious risk,” Browning says.

This lifecycle prevents abandoned agents from becoming persistent privileged identities. It also gives security and platform teams a reliable inventory of which nonhuman users exist, what they own and who is responsible when something goes wrong.

Apply Least Privilege to Every Action

Agent permissions cannot be defined only at the account level. Controls must consider the requested action, target resource, environment, initiating system and duration.

“Access should be granted according to the specific tool, task, resource, environment and duration involved,” Browning says.

This becomes more difficult when one platform action triggers downstream changes in several systems. Provisioning an application might call infrastructure, networking, identity, secrets and observability services. Platform teams must evaluate that entire chain rather than approving only the first API request.

Production deployments, security-policy changes, access to sensitive information and requests that could create significant costs may need human approval, while low-risk, bounded and easily reversible actions can proceed automatically.

Auditability must cover the same chain. The record should identify the agent, its human owner, the initiating user or system, credentials used, model and tool versions, original request, available context and every downstream action.

“The goal is to create a reliable, chronological story showing what the AI user was asked to do, what information and authority it had, what decisions and actions followed, which controls were applied, what changed, and what the final impact was,” Browing says.

Expand Autonomy According to Evidence

Platform teams should introduce agents through risk tiers rather than grant broad autonomy immediately. An agent may begin in observation or recommendation mode before receiving permission to execute low-risk changes.

Its autonomy can expand as it produces consistent results under controlled conditions. Metrics should include successful task completion, policy violations, human overrides, failed actions, rollback frequency and unexpected downstream effects.

Human checkpoints will remain necessary where consequences are difficult to reverse, but manually approving every action will not scale.

“Greater autonomy should be supported by repeated positive results, not granted all at once. It should be earned through evidence, limited by risk, continuously monitored, and maintained only while the evidence continues to support it,” Browning says.