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The problem this is designed to solve is one that has grown more pressing as AI-assisted communication has become common in customer service teams. When agents are sending dozens of emails a day and AI is drafting many of them, the risk of a non-compliant, culturally inappropriate, or brand-inconsistent message going out is real and difficult to catch through manual review alone. Organizations operating across multiple regions face an added layer of complexity where the same message can create legal exposure in one market that it would not in another. 

Governance addresses this by letting administrators write policies in plain language, such as blocking competitor mentions or requiring a legal disclaimer on every outgoing email, and the system automatically translates those into enforceable checks that run at send time. No coding or engineering work is required to set a policy up. Every outbound email is evaluated in real time, and if a violation is detected the message is flagged or blocked and the user is prompted to revise before it leaves the system. A full audit log captures what was checked, what was flagged, and how it was resolved, giving compliance and supervisor teams the visibility they need without having to sample emails manually. 

A simulation mode allows teams to test policies in a non-blocking state first, observing how they would perform in real scenarios before switching to full enforcement. Out-of-the-box policies for common needs including profanity filtering, groundedness checks, and email template adherence are available to get organizations started quickly. 

Governance sits inside the Quality Management Agent in Dynamics 365 Customer Service alongside the existing Quality Evaluation skill, which scores interaction quality. Together they form a single framework covering both communication compliance and service quality, applied consistently across human-authored and AI-generated content. For organizations managing customer service at scale across multiple regions or in regulated industries, this is a meaningful shift from reactive compliance review to proactive enforcement built directly into the communication workflow. 

The problem Microsoft is responding to is one that many contact center operators will recognize. AI tools have been added to contact centers in pieces over recent years: one product for self-service, another for agent assistance, separate tools for quality management and analytics. Each one may work in isolation, but together they create fragmentation. Customers repeat themselves when transferring between AI and human agents. Supervisors work across disconnected dashboards. AI programs deliver results in pilots but stall when organizations try to scale them. The root cause is that these tools were never designed to share context or learn from each other. 

The three agents Microsoft has introduced address this end to end. The Customer Assist Agent handles customer-facing interactions across voice and digital channels, including a new real-time voice AI capability that can manage natural conversation with low latency, handle interruptions, and switch between languages mid-call.  

It escalates to a human agent when needed, carrying the full conversation context so the customer does not have to start over. The Quality Assurance Agent moves quality evaluation from a periodic manual sampling exercise to continuous real-time monitoring across both AI-led and human-led interactions, flagging emerging issues and surfacing mitigation steps for supervisors before problems escalate.  

The Service Operations Agent assists administrators with environment setup, workflow configuration, and conversation orchestration, including dynamic queue prioritization based on real-time availability. 

All three agents are built on Copilot Studio rather than a closed CCaaS-specific stack, which means they can participate in broader business workflows and share governance with the rest of the Microsoft ecosystem. Pricing is consumption-based through Copilot Credits rather than per-seat licensing, allowing organizations to scale spend in line with actual usage. The Customer Assist Agent and Quality Assurance Agent are now generally available. The Service Operations Agent is in public preview in the United States.

What makes this release different from existing Business Central agents is who it is built for. Most Business Central capabilities assume the user is working inside the ERP. Expense Agent is aimed at employees who never open Business Central at all — field workers, drivers, technicians, and occasional office staff who incur expenses but have no reason to navigate a financial system. The agent meets them in Outlook, a dedicated web app, or Microsoft 365 Copilot Chat, and a Business Central license is not required for expense submission through those channels. 

When a user shares a receipt, the agent extracts the relevant data, categorizes the expense against configured policies, and prepares the report for review. Mileage, per diem, corporate card, and project-related expenses are all supported. The level of autonomy the agent operates with is configurable, allowing finance teams to decide how much they trust the agent to act independently before a human reviews. 

The capability enters public preview in May 2026 in English for the United States, with additional languages and regions planned from July 2026 onward. For organizations dealing with slow reimbursement cycles, compliance gaps in manual expense processes, or friction between field staff and finance teams, this is a meaningful shift in how everyday expense management can work inside Business Central. 

The problem this addresses is one that most contact center operations will recognize. When a call ends, critical work still needs to happen: records need updating, follow-up messages need sending, and compliance requirements need logging. Today, those actions are often delayed, dependent on agents completing them manually, or triggered by separate systems that can fall out of sync, particularly when a caller disconnects unexpectedly mid-flow. Records can remain locked or reflect stale data as a result. 

The new capability works through activity-based triggers in Copilot Studio. Voice agents can now listen for end-of-conversation events and react differently depending on how the call ended, whether the customer hung up, the call was transferred externally, or the conversation escalated to a human agent. Each scenario can trigger a different post-call workflow, all configured within the existing Copilot Studio flow without additional custom development. 

For contact center operations managing large call volumes, the practical benefit is consistency and speed. Post-call records update immediately, recovery workflows fire automatically when customers drop during sensitive steps like payment or authentication, and reporting reflects accurate call termination data. It closes a gap that has required workarounds in most contact center setups to date.

Project quoting has traditionally involved a lot of work outside the system. Teams adjusting for delivery center costs, role billing rates, or margin targets typically jump between spreadsheets and the ERP, recalculating manually and carrying assumptions across tools. The result is quotes built on estimates that are difficult to trace, and decisions made without a clear side-by-side comparison of the available options. 

What-if Analysis introduces a dedicated simulation workspace on the Draft quote. Users create one or more scenarios, each isolated from the live quote, and adjust quantities and pricing across dimensions such as resourcing unit, role, or custom pricing. As changes are made, the system instantly recalculates revenue, cost, gross margin, and budget variance. Multiple scenarios can be compared side by side, and when a preferred approach is identified, it is applied directly to the draft in a single step. 

The feature is currently available in preview for both Project Operations Core and the ERP-integrated deployment. For organizations that regularly bid on complex projects where margin and competitiveness have to be balanced carefully, this moves that analysis from a separate offline exercise into the same tool where the quote lives. It reduces the risk of a quote going out based on numbers that were not properly modeled, and gives stakeholders a clearer evidence base for pricing decisions before they are finalized.