{"id":3330,"date":"2021-10-20T07:26:24","date_gmt":"2021-10-20T07:26:24","guid":{"rendered":"https:\/\/germannvermogensverwaltung.primexdigital.com\/?p=3330"},"modified":"2026-10-06T15:30:29","modified_gmt":"2026-10-06T15:30:29","slug":"the-agent-development-life-cycle","status":"publish","type":"post","link":"https:\/\/germannvermogensverwaltung.primexdigital.com\/index.php\/2021\/10\/20\/the-agent-development-life-cycle\/","title":{"rendered":"The Agent Development Life Cycle"},"content":{"rendered":"

\"AI<\/p>\n

These gaps show why AI https:\/\/214rentals.com\/practical-tips-and-guidelines-for-programming-car-keys.html<\/a> agent lifecycle management cannot stop at provisioning. The goal of AI agent lifecycle management is not simply to track agents. A mature AI agent lifecycle management process ends only when the agent’s identity, access, and execution paths are no longer active.\u200d A new model, tool, data source, owner, or business purpose can change its risk.<\/p>\n<\/p>\n

\"AI<\/p>\n

Decommissioning should include disabling endpoints, revoking credentials, removing service accounts, preserving required logs, archiving evidence, notifying users and updating catalogs. Credentials, service accounts and permissions should be scoped to the agent\u2019s approved role. Testing an AI agent requires more than checking whether the software runs. The lifecycle begins by identifying the business problem that the agent is meant to solve and deciding whether an agent is the right approach. It helps enterprises move beyond ad hoc reviews by creating repeatable processes to approve, test, deploy, monitor, update and decommission agents throughout their lifecycle.<\/p>\n<\/p>\n