Topic starter
06/06/2026 2:27 am
TL;DR: AI agents are moving from API-only workflows to full computing environments, with Daytona CEO Ivan Burazin arguing that every agent will need its own sandbox, identity, and access surface as task complexity rises, according to WorkOS. The identity model shifts from seat-based software to machine-majority consumption, and that breaks assumptions about how access, accountability, and privilege are provisioned.
Editorial analysis by NHI Mgmt Group, based on content published by WorkOS: “Composable computers for agents: A conversation with Daytona CEO Ivan Burazin”.
Key questions
Q: What breaks when AI coding agents run in allow-default sandboxes?
A: Allow-default sandboxes fail because they preserve most host capabilities and rely on deny rules to catch every dangerous path.
Q: Why do machine-majority workflows change IAM and governance decisions?
A: Because the unit of consumption shifts from a person to a machine task, traditional provisioning, recertification, and licensing models stop reflecting actual usage.
Q: How do security teams prevent identity drift in AI agent deployments?
A: They should keep the human sponsor, the agent runtime, and any delegated service account separate in both policy and telemetry.
Practitioner guidance
- Define agent runtime boundaries Map which agent tasks require a dedicated sandbox, which require only API access, and which should be denied a full computing environment altogether.
- Separate sponsor and agent identity Assign distinct identities, credentials, and approval paths for the human sponsor, the agent runtime, and any delegated service accounts to prevent authority bleed.
- Review entitlement models for machine consumption Replace seat-centric access assumptions with machine-centric consumption, task scope, and runtime access duration when evaluating agent permissions.
Bottom line: AI agents are moving toward dedicated computing environments, which makes runtime boundary control more important than simple API authorisation.
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06/06/2026 4:07 am
Agent computers turn identity from a session problem into a runtime problem: once an AI agent needs a dedicated computing environment, IAM can no longer stop at authenticating a caller. The control question becomes how that runtime is bounded, observed, and retired across tasks. That is a structural shift for NHI governance because the protected object is no longer just a secret or token, but the whole execution surface the agent inhabits.
A question worth separating out:
Q: Should organisations treat AI agent governance as a one-time rollout or an ongoing programme?
A: It should be treated as an ongoing programme, because agent workflows change as integrations, permissions, and tasks evolve. A baseline that worked in one quarter may not hold after a workflow update or new system connection. Continuous review is what keeps monitor mode useful after the first policy cycle.
👉 Read our full editorial: Composable computers for AI agents and the new identity model