In every engineering organization I’ve worked with, some of the most important automations are the ones no one remembers building. I’ve seen this pattern repeat, and AI agents are about to make it far more common.
A platform engineer creates a workflow that invokes an AI agent when a deployment fails. The agent gathers context from monitoring and deployment systems, recommends a response and, after human approval, can trigger a rollback. At first, one team uses the workflow manually with limited access and a narrow purpose. Later, an external alert triggers it automatically, another team starts relying on its recommendations, and the resulting actions begin affecting production.
The dependency can remain invisible until an incident forces someone to ask who owns the workflow, which systems it can reach and what else relies on it. The incident did not create those unanswered questions. It exposed them.
Organizations have always accumulated local scripts, one-off automations, undocumented exceptions and tools that depend on the memory of the person who built them. AI agents and connected workflows increase the cost of that ambiguity. They can be triggered by external systems, reach multiple tools and take action faster than informal coordination can keep up. What once looked like a local shortcut can become a dependency the company notices only when something breaks. At agent speed and scale, that is one form of agentic chaos.
Companies therefore need a practical threshold for deciding when a local agent, workflow or connected tool has become a shared operational system. That threshold has been crossed when other people or systems can trigger it, when teams depend on its output or actions, when it gains meaningful access to company systems, or when changes in its behavior can affect operations, customers or risk.
Managing these systems requires tracking more than code and deployments. An agent’s behavior depends on its instructions, available context, selected model, tool permissions and the other workflows it can invoke. Expanding access, replacing a data source or modifying an instruction can materially change what the agent is able to decide and do, even without a traditional code deployment.
An inventory matters not because every experiment needs central approval, but because shared systems should be visible before an incident reveals how much depends on them. The next software inventory must extend beyond services and repositories. It should show which agents, workflows and connected tools exist, why they were created, who is accountable for their operation, which systems they can reach, what other processes depend on them and where human approval is required. It should also preserve enough operating history to explain how an important action occurred and whether the system is still serving its original purpose.
Governance should rise with reach and consequence. A prototype using synthetic data requires less oversight than an agent that can modify production infrastructure. A workflow used by one employee carries fewer organizational consequences than one whose output guides customer or security decisions. The goal is not to prevent teams from experimenting or to treat every automation as critical infrastructure. It is to strengthen ownership, review and traceability as adoption and impact grow.
Once an agent, workflow or connected tool crosses that threshold, the ownership gap has to be closed deliberately. The person or team that introduced it may understand its original purpose without controlling every system it reaches. A platform team can provide the shared environment, visibility and approved paths, while the team using the system remains accountable for its intended outcome and operating rules. Owners of affected systems define access to their domains, and human review points are assigned according to the consequences of the action.
Changes to instructions, permissions and dependencies should be reviewed according to their impact, while ownership must be reassigned as teams change and obsolete automations retired.
Retirement is especially easy to overlook. An abandoned agent may retain credentials, connections and the ability to act, while an old workflow can continue feeding another process long after its original owner has stopped maintaining it. Discovering those dependencies during an incident is a costly way to learn that the automation had become infrastructure.
Platform engineering has already faced one version of this problem. Developers needed faster access to complex infrastructure, and companies needed consistent standards and safe paths for action. Self-service worked when platforms made approved actions easy to discover and execute without forcing every request through a specialist queue. In building self-service systems for engineering organizations, I have seen the same principle repeatedly: autonomy scales when approved paths, ownership and context are built into the operating environment.
AI applies that principle to a software organization in which employees create workflows and agents increasingly execute them. The platform team’s role is not to centralize every decision or eliminate the local experimentation that makes AI useful. It is to make the operating environment legible: what exists, who owns it and where independent action must stop. When those answers are missing, agents make the gap visible by acting through it. This is the shift I see platform teams making now: from building portals for people to building the shared operating environment where agents and engineers work from the same context and the same guardrails.
Companies already maintain inventories of applications, services, repositories and cloud resources because operational dependence matters more than how a system began. The same test should apply to agents and workflows. The question is no longer whether an automation was formally launched, but whether people and systems already depend on it. AI agents can create new forms of sprawl and risk, but they also reveal agents and workflows that became important without formal ownership, review or retirement. Recognizing those dependencies early allows companies to expand AI use without leaving critical systems invisible or ownerless.
Zohar Einy is the co-founder and CEO of Port.