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Enforce ownership, identity, guardrails and audit

Certification decides what an agent is allowed to do. Enforcement makes sure it stays that way after it is deployed. At any moment, you should be able to say who owns each agentic asset, what it can access, what it can do and what it did.

Why agents need their own accountability​

Today, most agents run on a developer's personal token or a shared service account. They inherit the access of whoever set them up, outlive that person, and act without a clear record of who authorized what.

With access and accountability in Port, every agent is a first-class actor with an owner, its own scoped identity, guardrails on what it can do and an audit trail of what it did.

Pain pointWithout PortWith Port
No ownerThe first hour of every agent incident goes to finding who is responsible, while the agent keeps running.Every agent has an owning team and on-call coverage, so incidents reach the right person immediately.
Unbounded accessAgents inherit their creator's permissions and rely on limits in prompts they can ignore, so a small mistake or a manipulated prompt can reach systems the task never needed.Each agent runs under its own scoped identity, with guardrails enforced outside the prompt, so a mistake can only reach what the agent was certified for.
No audit trailYou can't explain incidents to customers or auditors, or prove compliance.Every creation, approval and promotion is recorded and runs can be traced back, so you can link any action to the human, agent and policy behind it.

Who owns it?​

Every agentic asset needs an owner who is accountable for it:

  • Assign an owning team to every agent, skill, MCP server and plugin, using Port's ownership model.
  • Run production agents like services, with an SLA, on-call coverage and an escalation path. If an agent misbehaves at 2 AM, the right person is paged.
  • Flag assets without an owner, and route them to a team for adoption or retirement.

Ownership is the first thing to enforce, because every other control depends on it. Approvals, alerts and kill-switch decisions all need someone to go to.

What can it access?​

Every agent should act under its own non-human identity, never under a person's:

  • One scoped identity per agent - every agent gets its own identity, with its own RBAC permissions and team membership that decide which Context Lake data it can access. Every action traces back to one agent, not to the person who set it up.
  • Dynamic policies - decide access at runtime from catalog data with dynamic permissions, for example letting an agent act only on services its owning team owns, or requiring approval when the target is in production.
  • Who can trigger it - only the teams an agent is available to can start it.

Golden paths create these identities automatically when an agent is scaffolded, so developers never handle them directly.

What can it do?​

Guardrails limit what an agent can do, even with valid credentials:

  • Scoped tools - give each agent only the tools its job needs. Use MCP connectors to create logical MCP servers that expose the tools for a job, not every tool a vendor offers. Then choose the allowed MCP tools per Port AI agent, or limit the allowed tools for external agents that use the Port MCP server.
  • Allowed workflows - limit which Port workflows an agent can run, using the agent's allowed workflows or, for the Port MCP server, an allowed actions list. An agent can't run a workflow that isn't on its list, even if its prompt asks it to.
  • Step-up approval for risky actions - require human approval for actions that are irreversible, external-facing or touch production. See tools and approvals.
  • Policy as code - express rules as scorecards and workflow conditions that Port evaluates, not as instructions in a prompt. For example, a production agent may only load certified skills and connect to approved MCP servers. A rule that lives only in a prompt is not enforced.

What did it do?​

When something goes wrong, you need to know exactly what happened:

  • Lifecycle events - creation, approval, promotion and permission changes are recorded on each asset in the Context Lake.
  • Agent runs - with run data ingested from your agent platforms, each run can be traced back to the agent, the skills it loaded, the tools it called and the human who triggered or approved it. For Port AI agents, see AI invocations.
  • Platform changes - the audit log records every change to Port's configuration.

Together, these let you answer which human, which agent and which policy authorized each action.

When an agent steps out of bounds​

Scorecards flag agents that drift outside these boundaries, for example a production agent that runs on a personal token or loads an uncertified skill. See contain agents with a kill switch for how to respond, from notifying the owner to revoking the agent's credentials.