Use agents to remove repetitive scheduling work while keeping consequential decisions and boundaries with people. A practical approach combines explicit preferences, narrow authority, and simple escalation.
Delegate repetitive schedule changes to an agent that has narrow, well-defined authority and explicit user preferences.
Design agent behaviour around rules: what it can book, when it should ask, and what data it can access or modify.
Use escalation and audit trails so humans make important decisions and can review automated actions.
# The basic problem A customer needs a consultation next week. The customer prefers Tuesday or Thursday afternoons, ideally between 2:00pm and 5:00pm. Human schedulers run into repetitive work: constantly checking calendars, waiting for cancellations, and coordinating across participants.
The practical question is not whether an agent can replace a person. It's what exact, repetitive steps an agent can do reliably so people can focus on decisions that require judgment.
# A simple, workable pattern Give the agent a narrow brief and explicit preferences. In this appointment example the brief includes:
When a slot opens that fits the rules, the agent can act: book the slot, send the confirmation, and update calendars. If a candidate slot falls outside the brief, the agent should escalate or propose options to a human.
# Guardrails that matter Concrete limits reduce risk and confusion. Useful guardrails include:
Keep records of every automatic action so humans can audit what happened and why.
# Escalation and transparency Design agents to surface uncertainty. If multiple constraints conflict, or if the booking affects other obligations, the agent should present options instead of acting. Notifications should explain the rule that triggered the action and provide a clear undo path.
A simple flow looks like this: detect open slot → check rules → if in-rule then act and notify → if out-of-rule then propose to human with context and options.
# Treat autonomy as a configurable dial Start conservative. Let the agent run with tight rules for low-risk tasks. Track outcomes and user satisfaction. Gradually widen authority where rules perform reliably and the cost of error is low.
Some organisations limit agent actions early on, then expand them after monitoring. Others place governance controls closer to data and identity so the agent can't exceed its authority even if the logic is wrong.
# Why this matters for organisations
# Practical next steps for teams
For much of the past two years, the general belief in enterprise AI has been that more autonomy equals better performance. Build agents that can plan, decide, and act across multi-step workflows, and give them as much room to run as possible. That assumption is now being tested at scale, in real production environments
Presented by EDB As enterprises give AI agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually stops it?
An AI agent is not defined by how intelligently it talks. It's defined by what it's trusted to do. Give a language model a chat window, and you have an interface. Give it access to production APIs , identity, business logic, memory, and the authority to execute actions on your behalf, and you have something categorical
There is a moment in every AI agent deployment when someone asks the obvious question: should the sp...
The conversation around AI agents has moved quickly from demos to organisational design. Microsoft’s 2025 Work Trend Index for Singapore reported that 56 per cent of Singapore leaders were already using agents to fully automate workstreams or business processes, while 46 per cent expected their teams to build multi-age
AI (Artificial Intelligence) နည်းပညာက အá€á€¯á€¡á€á€»á€á€”်မှာ နေရာá€á€á€¯á€„်းမှာ ရှá€á€”ေပါပြီዠဒါပေမဲ့ “AI ကá€á€¯ ဘယ်ကနေ စလေ့လာရမလဲአအမြန်ဆုံး á€á€á€ºá€™á€¼á€±á€¬á€€á€ºá€¡á€±á€¬á€„်â
Loading more related stories...
Open the app view to save this story, compare related coverage, and continue from the same source.