Agentic AI is reshaping identity automation, but not every agentic solution is built for enterprise reality. A purely agentic approach, where one AI agent both builds and runs identity workflows without human oversight, is probabilistic by nature: it reasons through each step at runtime, so when an application's UI changes or an edge case appears, workflows can break silently or take the wrong action. For sensitive identity work like provisioning and deprovisioning, that unpredictability is a real risk. This guide breaks down what to evaluate before adopting agentic AI for disconnected apps, and why deterministic execution with human-in-the-loop support is the more reliable architecture at enterprise scale.
Evaluate any agentic AI solution against five criteria:
- Execution method: Is workflow execution deterministic and repeatable, or probabilistic and prone to silent failures on critical identity tasks?
- Application coverage: Does it reach beyond the browser to on-prem thick clients, mobile, and custom internal apps, or only the easy-to-automate web apps?
- Scalability and cost: Can it run daily syncs across thousands of users without compute and token costs becoming prohibitive?
- Performance: Can it process large user sets with predictable speed, or do reasoning-heavy steps turn routine jobs into multi-hour or multi-day runs?
- Security readiness: Can it handle MFA and multi-step logins and pass bot-detection systems like Cloudflare without disabling your controls?
Download the buyer's guide: Evaluating Agentic AI Solutions for Disconnected Applications (PDF)
