Agentic AI works through a repeating cycle of planning, acting, and observing. Given a goal, the system breaks it into steps, decides which tool or data source to consult for each step, executes that step, and then evaluates the outcome before deciding what to do next. This loop lets it handle tasks that unfold over several turns and unexpected branches, such as verifying an identity, checking eligibility, and then completing a booking, instead of only producing a single response to a single input.
Autonomy without limits is risky in regulated MENA sectors like banking, healthcare, and government services, where an AI acting on its own in a sensitive workflow, such as releasing funds or approving a claim, can create compliance exposure. Agentic AI deployed in these contexts is typically paired with explicit action boundaries, so the system can plan and execute routine multi-step work while sensitive or high-risk actions are still routed to a human for approval.
In Eshal: Eshal uses agentic architecture with guardrails. The AI plans and executes multi-step workflows within pre-defined action boundaries set by Dynamic Action Gating. This lets the concierge handle tasks such as multi-step returns or appointment changes autonomously, while higher-risk actions, such as payment changes or account closures, are flagged for human review rather than executed automatically.