Governance built into the foundation of your agent program is what separates a successful production deployment from one that stalls—or fails publicly. This guide explains how to design, manage, and scale customer-facing, real-time voice agents using Microsoft Copilot Studio, with a focus on governance, reliability, and enterprise readiness.

Imagine a customer calling your contact center about a billing dispute. A real-time voice agent answers, identifies the customer, references their account history, resolves the issue, and—when needed—hands off to a live agent with full context preserved. Human agents focus on exceptions, not routine queries.

Now imagine that same scenario without agent governance. The agent was built, published directly to production, and never tested for escalation. Monitoring was not enabled. The first signal of a problem is a customer complaint—or a data exposure.

Customer-facing agents are becoming the front door for how organizations engage with customers, handling intent and outcomes across conversational AI experiences. What began as chat has evolved into always-on agents that resolve issues, take action, and now support real-time voice across digital and contact center environments using platforms like Copilot Studio. The opportunity is massive—but so is the cost of getting the foundation wrong. Just as self-service and Q&A agents redefined support at scale, this shift will fundamentally reshape how companies operate.

Why real-time voice agents require a different governance lens

Most organizations already govern internal AI tools designed for known users and controlled environments. Customer-facing agents operate under fundamentally different conditions. There are unknown users, public channels, brand exposure, and direct access to customer data and downstream systems. Failures in these customer experience events mean operational, regulatory, and reputational consequences.

This is why governance cannot be treated as a final approval step. As real-time voice agents scale, governance must be built into how they are designed, deployed, monitored, and evolved from the start. Organizations that treat governance as an accelerant—rather than a constraint—can move faster and more confidently than those who bolt it on later.

Principle: Governance as a design principle can streamline approval, which leads to accelerated scale and adoption.

Why real-time voice agents raise the stakes

Text‑based agents require governance, but real‑time voice introduces stricter operational constraints. Latency budgets are tighter, failures are immediately apparent to customers, and interruption handling, turn‑taking, session state, and escalation behavior directly affect service reliability.

Voice agents are typically deployed in high‑impact scenarios such as billing, orders, and service disruptions, where they integrate with Dynamics 365 Contact Center workflows. In these environments, agents must identify callers, reference active cases, execute actions, and escalate predictably.

For real‑time voice, escalation is a first‑class system requirement. Handoffs to human agents must preserve full conversational context and session state, and be validated under load before production traffic is routed.

Model selection also becomes operationally significant. Copilot Studio real‑time voice agents use purpose‑fit models to balance latency, quality, and reliability while remaining governed through a centralized control plane.

What good looks like: A production voice agent deployment has been tested for escalation behavior, latency under load, and handoff context preservation before any customer traffic is routed to it. Monitoring is active from day one, not added after the first incident.

A governance framework for the full agent lifecycle

Governing customer-facing agents effectively requires capabilities that span the full agent lifecycle. This is especially critical for business-to-consumer (B2C) agents, which operate in always-on, customer-facing contexts and must handle real-time interactions, actions, and sensitive data at scale—particularly in high‑stakes modalities like voice.

Copilot Studio provides this governance as a managed agent platform, enforcing controls through managed operations and managed security across the full lifecycle. That goes from build access and data connectivity to release, monitoring, and auditability. Rather than relying on documentation or custom wiring, governance is centralized in the Microsoft Power Platform control plane and consistently applied across chat, voice, and contact center scenarios.

The following five‑stage governance framework reflects how managed capabilities come together across the full lifecycle of customer-facing agents:

1. Govern the builder
2. Govern the build
3. Govern the release
4. Govern the runtime
5. Govern the lifecycle

Stage 1: Govern the builder

Before a single topic is created, agent governance starts with who is allowed to build and what they are allowed to connect.

• Define builder roles and environments. Specify who can create agents and which environments they can work in, using role‑based access in the Power Platform admin center.
• Set data access boundaries early. Apply data loss prevention (DLP) policies before development to determine which connectors and data sources agents can use.
• Maintain environment separation. Use distinct development, test, and production environments to validate changes before deploying them to customer‑facing scenarios.
• Standardize on managed solutions. Package agents in managed solutions to support versioning, controlled promotion, and rollback across environments.

What good looks like: A new agent builder requests access and is provisioned into a dedicated development environment. DLP policies are pre-applied. They cannot publish to any customer-facing channel without an administrator approval step.

Stage 2: Govern the build

How an agent is built determines how safe and predictable it is in production.

• Configure authentication by channel. Decide whether sessions are authenticated (Microsoft Entra ID or supported identity provider [IdP]) or anonymous, and design data access accordingly. (For public-facing scenarios like 800 numbers and public websites, anonymous real-time voice sessions are common.)
• Set generative AI behavior explicitly. Define and check grounding, topic scope, and allowed behaviors rather than relying on default settings.
• Validate escalation paths. Test and verify handoff to live agents with full conversation context preserved for all voice scenarios.
• Apply content moderation intentionally. Define clear engagement boundaries, enforce agent governance and policy controls, and rigorously red‑team and validate edge cases before deploying to production.

What good looks like: Testing escalation paths before publishing an agent to a customer-facing channel, so you can go live with more confidence. Catching errors before the first live escalation is critical to creating a good customer experience.

Stage 3: Govern the release

Moving an agent from development to production requires controlled, auditable steps.

• Standardize promotion paths. Promote agents through dev, test, and production using managed solutions and Power Platform pipelines with an auditable change history.
• Apply pre‑production validation gates. Require checks for conversation quality, escalation behavior, latency under load, and data access before publishing.
• Plan and test rollback. Define and validate rollback procedures for production issues prior to go‑live.
• Separate publish authorization. Require explicit approval to publish agents to customer‑facing channels, independent of build permissions.

What good looks like: An agent must pass a defined pre-production checklist and receive administrator approval to publish before any customer traffic reaches it. Every version promotion is tracked in the solution history.

Stage 4: Govern the runtime

Once an agent is live, governance shifts from control to visibility and response.

• Enable runtime observability. Turn on conversation transcripts and analytics in Copilot Studio before routing customer traffic.
• Define operational thresholds. Monitor metrics such as escalation rate, resolution rate, latency, and session completion, with alerts for deviations.
• Establish incident response. Define processes for detecting, triaging, and mitigating production issues in voice agents integrated with Dynamics 365 Contact Center.
• Monitor usage and capacity. Track session volume, message usage, and capacity limits to support scaling and stability.

What good looks like: Early detection through active monitoring. Voice agents that interact with customers without active monitoring are operating without a safety net. Issues that could persist for hours without analytics can be caught in minutes with these guards in place.

Stage 5: Govern the lifecycle

Voice agents are not static. They evolve as scenarios expand, customer needs change, and the platform advances. Managing change safely is as important as the initial deployment.

• Version agent configuration. Track changes to topics, actions, authentication, and generative AI settings using application lifecycle management (ALM) and source control.
• Validate changes pre‑production. Test all updates in non‑production environments to avoid regressions in core scenarios, including voice flows and escalation behavior.
• Coordinate releases operationally. Communicate deployment windows to IT and contact center operations teams.
• Evolve governance as scale grows. Reassess role-based access control (RBAC), DLP policies, environment strategy, and publishing permissions as agent count and channel coverage expand.

Platform capabilities that support agent governance

Copilot Studio provides a centralized control plane for building, operating, and governing customer‑facing agents. The platform capabilities below directly enable the governance framework described above and should be configured before scaling B2C deployments:

• Power Platform admin center: Central governance surface for environments, DLP policies, user access, and capacity management; the primary enforcement layer for agent governance.
• Environment management: Separate development, test, and production environments to support validation and controlled promotion of customer‑facing agents.
• Data loss prevention (DLP) policies: Environment‑level connector controls that define which data sources and services agents can access before any connections are established.
• Managed solutions and Power Platform pipelines: Package agents as managed solutions and promote them through environments with version tracking, rollback support, and an auditable change history.
• Microsoft Entra ID and channel authentication: Configure customer‑facing authentication using Entra ID or supported identity providers to enable secure, scoped access to customer data.
• Generative AI controls and content moderation: Per‑agent configuration for grounding, topic scope, allowed behaviors, and content filtering, applied deliberately prior to public deployment.
• Conversation transcripts and analytics: Built‑in logging and analytics providing runtime visibility into agent behavior, escalation patterns, and coverage gaps.
• Dynamics 365 Contact Center integration: Native escalation to live agents with case context preservation and unified conversation history for voice deployments.
• Azure Speech: Underlying speech infrastructure for real‑time voice agents, with implications for latency, reliability, and capacity planning.
• Dataverse security model: Row‑level and business‑unit security controls governing agent access to customer records in Dynamics‑integrated scenarios.

Security, privacy, and compliance for customer-facing agents

For IT and security teams, governance of customer-facing agents must also address data handling, regulatory requirements, and audit readiness. These are not secondary concerns—they’re often the first gate any enterprise B2C deployment must pass through.

Customer data and PII in voice interactions

Real-time voice agents generate conversation transcripts that may contain personally identifiable information. Establish clear retention policies for these transcripts before deployment. Define who has access to conversation logs, how long they are retained, and whether they are subject to deletion requests under applicable privacy regulations.

Regulatory considerations

Depending on your industry and geography, customer-facing AI agents may be subject to requirements under General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or sector-specific regulations in financial services or healthcare. Review applicable requirements with your legal and compliance teams before deploying agents to regulated customer scenarios. DLP policies in the Power Platform admin center are a key compliance control.

Audit logging and compliance evidence

Power Platform and Copilot Studio support audit logging through Microsoft Purview and the Power Platform admin center. Ensure audit logging is enabled before production deployment and that logs are retained according to your organization’s compliance requirements.

Credential and secret management

Agents that connect to external systems require credentials and connection strings. Do not store secrets in agent configuration directly. Use environment variables in Power Platform or Azure Key Vault references to manage credentials securely, with access controlled through role assignments.

Note for architects: Security and compliance review should be a gate in Stage 3 (govern the release), not an afterthought discovered during audit. Engage your security and compliance teams in the pre-production validation checklist.

Five anti-patterns that derail production AI deployments

Organizations that have scaled B2C agents successfully tend to have avoided the same set of avoidable mistakes. These are the patterns most likely to cause problems once customer traffic is live.

1. Skipping environment separation: Building and publishing agents in the same environment, or directly in production, allows untested changes to reach customers and is one of the most common causes of early deployment issues.
2. Publishing voice agents without tested escalation: Escalation to a live agent is a core part of voice agent design. Untested handoff paths that fail to preserve customer context degrade the experience more than having no agent at all.
3. Granting broad DLP exceptions under schedule pressure: Temporarily relaxing DLP policies often becomes permanent, introducing data access risk and audit gaps that are difficult to remediate later.
4. Treating monitoring as a post‑launch activity: When transcripts, analytics, and alerts are not enabled before go‑live, production issues surface through customer complaints rather than operational signals.
5. Building open‑ended agents without defined scope: Broad, general‑purpose agents are harder to test, govern, and improve than agents scoped to specific customer scenarios with clear success criteria.

How to operationalize voice agents

As teams move from pilots to production, a small set of patterns consistently differentiates voice agent deployments that scale.

• Start with well‑defined customer scenarios rather than broad open‑ended agents. Clear scope simplifies risk assessment, testing, and measurement. A voice agent designed for order status or billing inquiries is easier to govern and iterate on than one intended to answer arbitrary customer questions.
• Treat real‑time voice as an extension of existing digital agent governance, not an exception. Teams that have already governed chat‑based agents in Copilot Studio are well positioned to apply the same controls to voice, while accounting for stricter latency, escalation, and runtime requirements.
• Design escalation as a primary flow, not a fallback. Agents integrated with Dynamics 365 Contact Center should preserve full conversational and case context on handoff. Predictable escalation maintains continuity; dropped context undermines trust.
• As programs scale, three governance questions remain central:
  • Which customer scenarios are appropriate for automation versus human handling?
  • Where does real‑time voice materially improve the experience versus add operational complexity?
  • How quickly can production issues be detected and resolved once agents are live?

Using Copilot Studio as a governance foundation for agents

Copilot Studio and Power Platform provide a centralized environment for building, operating, and governing agents, which becomes increasingly important as deployments expand from internal use cases to customer‑facing channels.

Establish governance once in Copilot Studio, and scale it across chat, voice, and backend‑driven agents without fragmentation. As a centralized control plane, the platform helps you enforce consistent policies and maintain operational oversight as agents expand across channels, regions, and customer scenarios.

For organizations already using Copilot Studio, many of the governance capabilities described here are available today. Support for real-time voice agents in Copilot Studio is now generally available in North America, with deployments delivered first through Dynamics 365 Contact Center. Language support, additional regions, and broader publishing channels will expand over time as part of Copilot Studio’s ongoing roadmap.

Learn more in the announcement blog for real-time voice agents.

Governance readiness checklist for customer-facing voice agents

Before deploying a customer-facing or real-time voice agent to production, verify governance readiness across these core dimensions.

Access and environment

• Separate development, test, and production environments are provisioned
• Role-based access is configured—developers cannot publish directly to production
• Advanced connector policy is applied to all environments before development begins
• Publishing permissions for customer-facing channels require administrator approval

Build and configuration

• Authentication and identity are configured appropriately for the channel (authenticated or anonymous)
• Generative AI settings, grounding, and content moderation are configured deliberately
• Credential and secret management uses environment variables or Azure Key Vault references
• The agent is packaged in a managed solution with tracked versioning

Testing and release

• Escalation paths to live agents have been tested with context preservation verified
• Latency and behavior have been validated under simulated load
• A pre-production validation checklist has been completed and signed off
• A rollback procedure has been defined and tested
• Audit logging is enabled and log retention meets compliance requirements

Runtime and operations

• Conversation transcripts and analytics are active before first customer interaction
• Operational thresholds (escalation rate, session completion rate) are defined with alerts
• An incident response procedure is defined and communicated to operations teams
• Usage monitoring is in place for capacity planning
• A change management process is defined for updating live agents

Getting started with customer-facing agents

Organizations ready to operationalize B2C agents should begin with the following steps:

• Align on priority scenarios. Agree on customer scenarios, scope, success criteria, and escalation requirements before any development begins.
• Set up environments and governance. Configure separate dev, test, and production environments and apply DLP policies before granting developer access. Define role‑based access and require administrator approval for publishing to customer‑facing channels.
• Engage security and compliance early. Review applicable regulatory requirements and establish data retention policies for conversation transcripts.
• Build and validate deliberately. Start with a scoped agent, use managed solutions, and be sure to test and verify escalation paths.
• Confirm readiness before go‑live. Complete the governance readiness checklist and enable monitoring and escalation thresholds prior to routing customer traffic.

With the right foundation in place, teams can scale customer‑facing and real‑time voice agents—while maintaining the reliability, security, and operational integrity IT teams are responsible for protecting.

Resources for governing AI agents

• An overview of real-time voice agents in Copilot Studio
• Extend AI voice support: Introducing real-time voice agents in Microsoft Copilot Studio
• Power Platform admin center documentation
• Dynamics 365 Contact Center documentation

Governance starts early

Establish a governance foundation with Copilot Studio that scales across chat, voice, and backend-driven agents.

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The post The in-depth guide to managing real-time voice agents at scale appeared first on Microsoft Copilot Blog.

That’s why we’re excited to announce the general availability of real‑time voice agents in Microsoft Copilot Studio launching in Dynamics 365 Contact Center. These agents are designed for nuanced, high‑impact interactions where voice experiences need to adapt in the moment—while still operating within trusted, enterprise‑grade solutions.

Real-time voice agents build on the momentum of Copilot Studio as a proven, enterprise-scale platform. Over 80% of Fortune 500 companies now have active agents built using our low-code/no-code tools, creating a strong foundation for bringing real-time, conversational voice experiences into their customer workflows.¹ This widespread adoption shows how ready organizations are to extend their existing Copilot-powered agents into natural, responsive voice interactions.

Why AI voice support needs to go off script

For decades, menu‑based interactive voice response (IVR) systems provided predictability, reliability, and compliance at scale. Over time, organizations layered in speech recognition and automation to reduce friction and manage growing call volumes more efficiently. These approaches remain critical today and continue to power successful customer service operations across industries.

What’s changed is not the importance of voice, but the expectations customers now bring into voice interactions. Conversations rarely follow a straight line. Customers interrupt, clarify, change direction mid‑call, or introduce urgency without warning. When that happens, rigid interaction models can struggle to keep up, even when the underlying systems are reliable.

Meanwhile, across industries, contact centers are under pressure to do more with less. Interaction volumes continue to rise, margins are tighter, and customer expectations are shaped by digital experiences that feel fast, personal, and responsive. Many teams are already exploring AI through chatbots, voice automation, or workflow tools, but scaling those experiments into production voice experiences that customers trust is far harder than running a pilot.

Voice, in particular, exposes gaps immediately. Latency, awkward handoffs, or missing context are noticed in real time, often at the most critical moments. That’s why simply adding automation isn’t enough. As organizations move toward agentic, AI‑first service models, voice needs to work as part of a unified service layer: one where understanding, reasoning, and action happen together, and where context carries forward if escalation is required.

Meeting this bar doesn’t require replacing the voice systems that already work. It requires extending them with capabilities designed specifically for live, conversational interactions. This makes it possible to move beyond scripted interactions without treating voice as an isolated AI experiment.

Meet real-time voice agents for AI voice support

Real‑time voice agents represent a new premium mode within voice agents that sits under the broader category of conversational AI. They are optimized for low‑latency, interruptible, speech‑to‑speech conversations with real‑time reasoning. This distinction helps teams choose the right interaction model for each scenario without treating all voice interactions as equal.

These agents can move from intent to action to confirmation within a single interaction. They can retrieve or update information mid‑conversation and take action as the interaction unfolds. Customers can get faster resolution, while service teams can maintain consistent experiences and centralized operational control. This also frees human agents to focus their time and energy on conversations where judgment and empathy matter most.

High-volume self-service, designed for everyday voice interactions

Real-time voice agents are built for the high volume, business-to-consumer (B2C) interactions that define customer service at scale. These everyday inbound calls make up the majority of customer engagement across industries. To support this breadth, Copilot Studio enables organizations to grow their voice strategy—starting with deterministic, template-driven flows and expanding into dynamic, real-time voice agents as needs evolve.

Copilot Studio provides a documented set of external voice agent templates designed for unauthenticated, customer-facing interactions across both Copilot Studio and Dynamics 365 Contact Center, covering many of the core workflows customer service teams handle today, including:

• Billing and payments, which are among the most frequent and sensitive contact center interactions. Customers want clarity and resolution in the same call, not a handoff or follow-up. These scenarios often start with deterministic flows—confirming identity, checking balances, or processing payments—but can switch to dynamic, real-time voice agents that explain charges, respond to questions, and adapt tone as situations become more urgent.
• Order and reservation support across retail, travel, and hospitality, which often requires more dynamic handling. What begins as a simple status check can quickly shift into a change request or issue resolution. Real-time voice agents adapt to these pivots by grounding responses in live order or reservation data. They’re designed to act while preserving relevant context, supporting organizations as they evolve from structured flows to more flexible conversational automation.
• Eligibility and verification scenarios in healthcare, financial services, telecom, and the public sector, which rely on accuracy and trust. Deterministic steps—collecting required information, confirming eligibility criteria—can be paired with dynamic voice capabilities that answer questions as they arise and help preserve continuity if escalation or additional support is needed.
• Appointment scheduling and changes often involve interruptions and evolving preferences. Organizations can begin with predictable scheduling flows and grow into dynamic, real-time voice agents that let customers book, reschedule, or confirm details conversationally, without unnecessary restarting or repeating information as needs shift.
• Account and membership management, which often spans multiple tasks, from updating personal details to managing subscriptions or reviewing benefits. Deterministic updates can be combined with dynamic, context-aware voice interactions that stay connected to live account data, keeping these conversations efficient, accurate, and natural from start to finish.

From conversation to resolution, without breaking flow

Real‑time voice agents are built for real customer service environments, where escalation is a natural part of resolution rather than a failure of automation. At launch, these experiences are delivered through Dynamics 365 Contact Center, with conversation context carrying forward automatically, helping reduce the need for customers to restate information when human judgement is required.

As part of the Copilot Studio product roadmap, real‑time voice agents will also extend to Microsoft Teams Phone and additional Copilot Studio digital apps and channels, enabling organizations to bring consistent, context‑aware voice experiences to more customer touchpoints over time.

Voice automation designed for trust, control, and evolution at scale

For IT teams, voice automation has always been high impact but high risk. Feedback from early internal deployments reinforces the importance of pairing conversational intelligence with enterprise‑grade governance and lifecycle controls.

Innovation in voice does not require giving up control or predictability. Voice is a high‑stakes interaction surface, and customers notice immediately when experiences feel unreliable or inconsistent.

Copilot Studio takes a deliberate approach to real‑time voice by selecting the right models for the right moments, including the latest frontier models, optimized for the right balance of quality, latency, and reliability, without placing that complexity on customers. Built on Microsoft’s enterprise foundations for security, governance, and operational oversight, real‑time voice agents help organizations innovate with confidence while preserving trust.

Get started with Copilot Studio

Support for real-time voice agents is now generally available in North America for Dynamics 365 Contact Center, with language support, additional regions, and broader customer touchpoints expanding over time as part of Copilot Studio’s global rollout.

Learn more about real-time voice agents and sign back in to Copilot Studio to start scaling customer support with agents today.

New to Copilot Studio? Discover how you can transform your business by building, evaluating, managing, and scaling custom AI agents—all in one place. Sign up for a free trial of Copilot Studio today.

Build voice agents that adapt

Create and manage high-impact, flexible voice agents—designed for enterprise scenarios.

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¹ Source: Microsoft usage data, 2026.

The post Extend AI voice support: Introducing real-time voice agents in Microsoft Copilot Studio appeared first on Microsoft Copilot Blog.

Microsoft Copilot Studio and Agent Builder in Microsoft 365 Copilot are designed to help customers reliably create agents that scale and deliver real, sustained business value—not just prototypes. Recent enhancements focus on making it easier to move from building an agent to running one confidently across complex, dynamic environments, with consistent quality and the ability to evolve as business needs change.

Discover the latest capabilities in agent evaluations, exciting updates for computer-using agents (including expanded model support), a new Agent Academy Operative training path, and more. Plus, learn how you can use these capabilities to help ensure your agents are ready for scale.

Build trust at scale with enhanced agent evaluations in Copilot Studio

Agents aren’t “set and forget.” Prompts evolve, models update, and data changes—which raises a critical question as agents take on real work: can we trust them at scale? Agent evaluations answer that question with evidence. They’re designed to turn expectations into measurable checks, help teams catch regressions early, and provide a repeatable way to assess agent quality as behavior and context evolve.

For example, a finance leader rolling out an agent for expense policy guidance or month‑end analysis needs to trust its behavior before moving beyond a pilot. With enhanced agent evaluations in Copilot Studio, teams can now validate performance using their own scenarios, policies, and production data—measuring quality, usability, and responsiveness across a full test set instead of isolated cases.

Side‑by‑side comparisons then help catch regressions before changes go live. Meanwhile, built‑in transparency and session replays support internal and external stakeholder review. The result is a clear, evidence‑based path from experimentation to trusted deployment.

Available in public preview, here’s a quick rundown of the latest eval enhancements.

Holistic and multi-dimensional agent evaluation

• Set-level grading framework: You can now evaluate agents across an entire test set instead of individual test cases, enabling an accurate measure of overall quality. By consolidating results from multiple tasks, makers can better understand real-world performance by seeing how agents maintain quality across a range of scenarios.
• Multiple graders per test set: With the ability to apply multiple grading approaches—such as quality, performance, and usability assessments—to the same test set, teams can gain a more complete evaluation without the complexity of managing separate test sets.
• Comparative testing: Teams can compare multiple agent versions side by side, which can make it easier to spot regressions and validate improvements before pushing the best version live.

Improved transparency and control

• User reactions and feedback: Makers can now provide quick feedback on evaluation results using a simple thumbs up or thumbs down action. This feedback helps Copilot Studio capture signals about evaluation accuracy, grader alignment, and edge cases, which means our team can continuously refine our evaluation models and improve result quality for agent makers.
• Open activity map in evaluation: Direct integration with the activity map gives teams immediate insight into how agents executed tasks, helping identify where issues occurred faster and improve optimization.
• Enterprise-grade auditing: Advanced session replays, action logs, and Microsoft Purview integration offer detailed visibility into agent behavior, helping makers preserve quality and streamline troubleshooting.

Streamlined workflow and data integration

• CSV downloadable format: Makers can now download a ready-to-use comma-separated values (CSV) template that follows the exact structure required for importing test cases into evaluation. Instead of creating files from scratch—and running into formatting errors, missing columns, or failed imports—teams can rely on a validated template that can help shorten setup time and remove unnecessary friction.
• Import production data into evaluation: Real-world production data can now be imported directly into evaluations, providing high-quality test sets that reflect actual user interactions. This is designed to improve evaluation accuracy and help makers tune agents more closely to their specific audiences.
• Import and export of test sets, test cases, and results: Makers can import or export test sets, individual test cases, and evaluation results. This helps simplify teamwork and support repeatable testing across environments—essentials for enterprise-scale agent development.

Scale automation across real-world systems with nimbler computer use

Most organizations don’t lack ideas for automation. Instead, the challenge tends to be with fragmented systems, limited APIs, legacy desktop tools, and workflows that go across multiple departments. Replacing everything isn’t realistic. But maintaining brittle, script-based automation isn’t sustainable either.

Copilot Studio’s computer-using agents (CUAs) can address this gap by interacting directly with web and desktop interfaces, supporting automation across systems that weren’t designed to integrate. They facilitate automation in complex, dynamic environments where traditional robotic process automation (RPA) falls short.

Consider a customer support organization handling service requests across disconnected systems. When a customer submits a support request, a computer-using agent can:

1. Retrieve customer and entitlement details from the customer relationship management (CRM) system.
2. Create or update a case in the service management system.
3. Pull relevant troubleshooting steps from a knowledge base.
4. Update the case status and resolution checklist in Microsoft SharePoint.
5. Notify the assigned service representative and escalate if service-level agreements (SLAs) are at risk.

This would be impossible with RPA alone because of the need to transcend systems. Although pieces could be automated, a person historically would need to initiate each step. With computer use, the organization can now accelerate this process and mitigate missed steps, without requiring a redesign of existing systems.

And the latest updates enhance the value of your computer-using agents, adding key capabilities that enable improved flexibility, security, and scalability:

• Expanded model availability: We’ve added Claude Sonnet 4.5 as an additional model choice for CUAs. You can choose between Anthropic models and OpenAI’s Computer-Using Agent to get the best possible results for your task.
• Built-in credentials: Simplify and secure authentication with built-in credentials that require minimal setup. Users simply input their username and password once, and Copilot Studio stores the credentials securely.
• Enterprise-grade logging and auditing: New monitoring tools, integrated with Microsoft Purview, enhance computer-using agent session visibility. This includes detailed logs of agent activity and session replays with screenshots that support traceability and compliance processes.
• Cloud PC pool: Powered by Windows 365 for Agents, this scalable, managed cloud infrastructure integrates with Microsoft Entra and Intune. These PC pools auto-scale based on workload demand, helping you handle spikes without over-provisioning.

We know the more tools that help drive operational efficiency while maintaining control over automated workflows, the more confident teams can be about adopting computer use. That’s why these updates help elevate computer-using agents as a more reliable, adaptable solution for enterprises looking to scale their use of agentic automation.

Learn to build multi-agent systems with the Agent Academy Operative path

Finished the Recruit training from the Copilot Studio Agent Academy and looking to go deeper? The new Operative path unlocks the next level of training for agent makers who are ready to build their skills. It’s designed for practitioners who already have their first agent working and want to expand their skills to build more sophisticated, production-ready solutions.

The Operative path walks learners through building a complex, multi-agent hiring automation system, using it as an applied learning example that can be adapted to any business scenario.

Along the way, participants develop critical skills such as writing clear and effective agent instructions, selecting and evaluating AI models, and applying advanced prompt patterns, agent flow integration, and Model Context Protocol (MCP). The curriculum also emphasizes operational readiness, including feedback loops, telemetry, and AI safety throughout the agent lifecycle.

By the end of the path, learners can gain a deeper understanding of how to design, build, and architect scalable multi-agent systems that can evolve with business needs. For creators ready to move from basic agents to more advanced, reliable solutions, the Operative path provides a practical and structured next step.

What else is new and improved in Copilot Studio

Now, let’s take a quick look at some other exciting updates—all generally available (GA)—that further enhance your Copilot Studio (and Agent Builder) experience:

• Copy agents from Agent Builder into Copilot Studio to scale impact: Agents that start as individual ideas in Agent Builder and prove team-wide value can now be opened directly in Copilot Studio for a more extensive maker experience. This unlocks advanced features such as topics, automations, expanded publishing channels, and enterprise governance controls, including data loss prevention and application lifecycle management. For example, a support representative’s personal helper agent can be expanded into a shared tool that categorizes tickets, suggests responses, and routes issues to the right specialists—without rebuilding from scratch.
• Query your agent inventory from Azure Resource Graph: The Microsoft Power Platform agent inventory, which organizes and displays all your published Copilot Studio and Agent Builder agents, is now generally available. Admins can query this inventory programmatically using Azure Resource Graph to access detailed data about both draft and published agents across the tenant, using Azure portal, CLI, PowerShell, or REST API.
• Generate icons for your agents using AI in Agent Builder: Makers can now generate custom agent icons directly in Agent Builder using AI. Instead of browsing or creating artwork manually, they simply describe how the icon should look—using the agent’s description or a custom prompt—and get a unique icon designed to stand out in the Agent Store.
• Try the Copilot Studio extension for Visual Studio Code: The Copilot Studio extension lets teams version, edit, and deploy agents directly from Visual Studio Code, making it easier to align with existing software development workflows.

The big takeaway: Stronger Copilot Studio tools for more scalable agent experiences

These updates aren’t just new features; they strengthen the tools teams rely on to create agents that scale with their business. By enhancing flexibility, security, and visibility, these updates are designed to make it easier to scale agents without starting over each time.

This continuity helps makers innovate quickly while IT teams maintain control over governance, compliance, and performance—bridging the gap between rapid iteration and enterprise-grade reliability. Why? Because at the end of the day, the best agents are those that are built to grow with your needs, and with these updates, that evolution becomes more attainable every month.

Stay up to date on all things Copilot Studio

Check out all the updates as we ship them, as well as new features releasing in the next few months here: What’s new in Microsoft Copilot Studio.

To learn more about Microsoft Copilot Studio and how it can transform productivity within your organization, visit the Copilot Studio website or sign up for our free trial today.

Create your next agent

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The post New and improved: Agent evaluations, computer use, and advanced maker training appeared first on Microsoft Copilot Blog.

Grok 4.1 Fast is a fast‑reasoning, text‑generation model (generation of images and other media types are not supported) that is designed for large context, deep tool use, and can be used to handle complex workflows. This addition reflects our ongoing commitment to give you more flexibility when designing and optimizing agents—so you can choose the right model for every business scenario.

Expanding our model line-up

Copilot Studio aims to give makers the ability to evaluate and use the model best suited to transform their business. With the addition of xAI Grok 4.1 Fast, we’re building on that commitment.

Alongside OpenAI and Anthropic models, xAI adds even more depth to your multi‑model lineup—while still keeping responsible AI principles at the center. Before rollout, every model in your Copilot Studio lineup goes through security, safety, and quality evaluations.

When using Grok 4.1 Fast in Copilot Studio, customer data is not retained or used to train xAI’s models. xAI’s models are hosted outside Microsoft-managed environments, and when you use Grok 4.1 Fast in Copilot Studio, your relationship with xAI will be independent of Microsoft and governed by xAI’s Enterprise Terms of Service and Data Protection Addendum.

Unlocking the power of model choice

Starting today, Grok 4.1 Fast is available in preview in early access environments, and is off by default. Your organization’s admin must explicitly opt in to use the model before US-based makers can build with it.

If an admin doesn’t opt in, nothing changes and makers keep their current model options. Existing agents continue running exactly as they do today.

Learn more about admin opt-in controls:

• Allowing external language models for generative responses
• How to connect to xAI’s models

Choice is everything

Build with confidence by choosing the model that fits your use case—and your goals—best.

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The post More choice, more flexibility: xAI Grok 4.1 Fast now available in Microsoft Copilot Studio appeared first on Microsoft Copilot Blog.

For anyone building agents in Copilot Studio, we know that model choice enables you to solve your unique business problems and realize continuous improvement in agent performance. That’s why in the past 60 days, we’ve enabled major models like GPT-5, GPT-5.1, Claude Sonnet 4.5 and Claude Opus 4.5 in Copilot Studio on the same day as their public release. And with new Copilot Studio platform capabilities like agent evaluations, we’re making it even easier to test your agent with multiple models to find the right fit.

Hear directly from the team on how we are enabling model choice across Copilot Studio and Microsoft 365 Copilot:

Claude Opus 4.5 will replace Claude Opus 4.1 in Copilot Studio as an experimental model. As with any experimental model, we encourage testing it in non-production scenarios. To use Anthropic models in Copilot Studio, admins must opt in via the Microsoft 365 Admin Center. You can learn more about doing so here. If you have already opted into using Anthropic models in your tenant, there’s no action required.

You can learn more about Claude Opus 4.5 from Anthropic’s launch announcement.

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The post Announcing Claude Opus 4.5 in Microsoft Copilot Studio appeared first on Microsoft Copilot Blog.

Experimental models give you the opportunity to test cutting-edge capabilities before they reach general availability. You can evaluate performance against your use cases and existing models to prepare for deployment. As experimental models, we recommend using them in non-production scenarios while we complete our evaluation gates. This approach allows you to stay ahead of the curve, experimenting with the latest AI models while we ensure they meet quality standards.

You can learn more about using the latest models in Copilot Studio here.

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The post Available now: GPT-5.1 in Microsoft Copilot Studio appeared first on Microsoft Copilot Blog.

Anthropic models are available to customers in early release cycle environments worldwide today. They will roll out to preview in all environments within two weeks, and be ready for use in production by the end of the year.

Unlocking the power of choice

Copilot Studio will continue to use OpenAI as the default model for new agents, and now you’ll also have the flexibility to choose from Anthropic models, Claude Sonnet 4 and Claude Opus 4.1. You can use these models in Copilot Studio in two ways:

• Orchestration: Build, orchestrate, and manage agents powered by Anthropic models for advanced reasoning, workflow automation, flexible agentic tasks, and tool use.
• Prompt builder: The prompt builder drop-down menu makes it simple to choose your optimal model for each scenario.

With more options and control over model selection, we empower you to choose the best model for your use case – no matter the industry, function, or process.

How to get started with Anthropic models in Copilot Studio

Admins control how Anthropic models are used within their environment, with clear options for enabling, managing, and restricting access.

• Opt-in at launch: Anthropic models must first be enabled by your admin in the Microsoft 365 Admin Center (MAC) before they can be used across your tenant.
• Environment management: Once enabled in MAC, Anthropic will be on by default in the Power Platform Admin Center (PPAC). There, admins have additional controls to manage how Anthropic models are accessed by makers in Copilot Studio environments.
• Automatic fallback: If Anthropic models are disabled, agents built with it will automatically switch to the default model, OpenAI GPT-4o. No additional configuration required.

Step by step: Building an HR onboarding agent in Copilot Studio

Let’s say you’re an HR professional building an agent to automate employee onboarding. Here’s how you’d get started:

1. Create a new agent: Open Copilot Studio and begin creating your HR onboarding agent.
2. Connect knowledge sources: Link relevant HR documents, FAQs, and policy resources to your agent.
3. Design prompts and flows: Set up conversational logic and workflows your agent will use to interact with new hires.
4. Select your model: In the settings panel, you’ll find the option to select your agent’s primary model for reasoning and responding. You can choose from available options, including OpenAI GPT-5 or the newly added Claude Sonnet 4 and Claude Opus 4.1 models.

5. Configure AI tools: Model choice extends to custom prompts as well, allowing you to tailor your agent’s behavior to the strengths of each model for different tasks. For example, you might select one model for conversational employee FAQs and communications but use a different model for compliance checks and policy interpretation.
6. Deploy and test: Launch your agent, test onboarding scenarios, and iterate based on feedback.

When it comes to determining the right model for the right job, the answer depends on your unique goals. When building agents, there is no universal solution. The right model is the one that fits your use case. And with multi-agent orchestration in Copilot Studio, you can even coordinate multiple agents with different primary models, and they will work seamlessly together.

Experiment, share, and shape what’s next

We’re just getting started with model choice in Copilot Studio. Whether you’re using OpenAI, Anthropic, or both, you have the flexibility to choose the right model for your agent. We encourage you to explore, test different models for your use cases, and share your feedback. Your insights help us improve every day and shape what’s next for agentic AI at Microsoft.

Looking for more information to help you get started?

• Learn more about model choice in Copilot Studio
• Read the blog on expanding model choice in M365 Copilot with Claude in Researcher
• Join us at the Power Platform Community Conference in Las Vegas and connect with makers, experts, and innovators shaping the future of Copilot Studio

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The post Anthropic joins the multi-model lineup in Microsoft Copilot Studio appeared first on Microsoft Copilot Blog.

This capability enhances security for AI agents by enabling organizations to connect their own monitoring system such as Microsoft Defender as well as security platforms by other providers, or their own custom-built tools. These integrations allow for real-time evaluation and control of agent behavior during runtime.

When connected, the external systems become part of the agent’s decision-making process. They can block unsafe actions, even if the agent plans to execute them. For example, if the external system determines that the agent is planning to send an email that is oversharing information, it can block the email from being sent.

Admins can apply these protections across multiple agents and environments using the Power Platform Admin Center – no code required.

Copilot Studio agents: secure by default

AI agents face unique threats. One major risk is injection of prompts to the agent from an external source (also known as cross prompt injection attacks, or XPIA), where malicious prompts trick agents into leaking data or misusing tools. Copilot Studio includes default protections against both XPIA and user prompt injection attacks (UPIA). These defenses block suspicious prompts in real time, reducing the risk of data loss or unauthorized actions.

However, for organizations with advanced security needs, built-in protections may not be enough. That’s where the real-time protection comes in with an additional layer of defense.

Real-time protection in action

With advanced runtime protection, Copilot Studio calls the connected security system during the agent’s runtime. The system reviews the agent’s planned actions and decides whether to approve or block them. If it detects a threat, it stops the agent immediately and notifies the user. If the action is safe, the agent continues without delay or disruption.

This setup gives organizations stronger control over agent behavior while preserving a smooth user experience. It supports a “bring your own protection” model, allowing integration with:

• Microsoft Defender (available today – learn more)
• Third-party security providers
• Custom-built monitoring tools

This flexibility helps organizations align security for AI agents with internal policies, industry standards, and regional compliance.

Instant alerts, actionable logs

In addition to the ability to block threats before they happen, Copilot Studio creates detailed audit logs for every interaction with the external system. Admins can use these logs to track attempted breaches, identify vulnerable agents, and improve future deployments.

These logs also help evaluate how well the external monitoring system performs. Admins can analyze trends, refine policies, and guide agent creators in building more secure agents. This feedback loop strengthens overall security for AI agents.

How advanced real-time protection works

When a user sends a prompt, the agent formulates a plan to respond. This plan includes the tools and actions it will use. Before the agent begins execution, Copilot Studio sends this plan to the external monitoring system via an API call. The data includes:

• The user’s prompt and chat history
• Tool details and input values
• Metadata like agent ID, user ID, and tenant ID

The external system has one second to respond. If it approves the action, the agent proceeds. If it blocks the action, the agent stops and informs the user. If no response arrives in time, the agent assumes approval and continues.

Setup and management

Admins can configure external monitoring in the Power Platform Admin Center. They can apply settings to one environment, multiple environments, or specific environment groups. Different environments can use different monitoring systems. If needed, admins can disable the integration with a single setting.

Data sharing and compliance

To enable split-second decisions, Copilot Studio shares specific data with the external system. This includes prompts, chat history, tool inputs, and metadata. This data sharing is not customizable. Organizations should only enable the feature if they’re comfortable with the data being shared.

External providers may handle data differently than Microsoft. Some may store or process data outside your region. It’s important to review your provider’s policies and ensure they meet your compliance standards.

Why this feature matters

Advanced security for AI agents is no longer optional. As agents are increasingly equipped with autonomous triggers and take on more complex and sensitive tasks, organizations need real-time oversight. External monitoring gives them the tools to enforce compliance, detect and block threats, and gain visibility – without compromising performance.

This new, groundbreaking capability in Copilot Studio empowers organizations to take control of their AI agent security strategy. It’s a critical step toward safer, more reliable AI deployments.

Next steps

The public preview is rolling out worldwide, with availability to all customers by Wednesday, September 10th. To learn how to get started, visit the Microsoft Learn documentation for advanced real-time protection during agent runtime.

Resources

• Learn more in the feature’s public documentation
• Link to the developer documentation
• Learn about Microsoft Defender’s solution for agent threat protection
• Configure the feature in the Power Platform Admin Center PPAC

The post Strengthen agent security with real-time protection in Microsoft Copilot Studio appeared first on Microsoft Copilot Blog.

Human expertise remains irreplaceable in many business processes. From insurance claim assessments to legal document reviews, no matter how helpful automation is – human oversight is often still necessary. That’s why we’re excited to announce the preview of request for information (RFI) in Microsoft Copilot Studio, a powerful new capability that bridges the gap between automated efficiency and human insight. This helps your workflows maintain the highest quality while moving at the speed of business.

What is request for information in Copilot Studio?

Request for information is a new human-in-the-loop action built into Copilot Studio agent flows. This capability enables automated workflows to pause execution and collect input from designated human reviewers. When your automated business process encounters a decision point requiring human expertise, input, or review, the RFI action sends a structured request to the right people via email. Subsequently, it collects their responses and seamlessly integrates that input back into the workflow automation.

Unlike approvals with yes/no decisions only, RFI in Copilot Studio offers flexibility and dynamic functionality. You can request specific inputs with different data types, including text, numbers, dates, email addresses, and yes/no responses. Furthermore, you can customize each input with placeholder text, dropdown options, or multi-select capabilities to ensure you get exactly the information your workflow needs.

How does request for information work in agent flows?

Request for information integrates directly into your Copilot Studio agent flows. Here’s how it works:

• Configure your request – First, define the title, message, and assignees for your information request, along with the specific inputs you need from human reviewers.

• Collect responses – Next, the workflow pauses while designated humans receive an email request and provide the required information through an intuitive form interface.

• Continue processing – Finally, once responses are submitted, the workflow resumes automatically, making the human input available as parameters for subsequent actions.

Request for information use cases

Here’s how RFI transforms real business workflow scenarios:

Financial services compliance automation: During loan applications, workflows can pause to collect additional verification documents from compliance officers. This includes income verification or regulatory approvals, helping to ensure thorough review without manual process monitoring.

Insurance claims processing workflow: When automated claim review identifies missing documentation, RFI can request specific details from field adjusters or assessors – for example, damage assessments, repair estimates, or links to photo evidence – before continuing with automated claim processing.

Legal document review workflow: Contract approval workflows can request specific input from legal counsel on complex clauses or compliance requirements that need professional judgment. This helps to maintain both efficiency and legal rigor.

The beauty of RFI in Copilot Studio lies in its workflow flexibility. You can combine multiple input types, assign requests to multiple reviewers (with first-response processing), and configure fields as optional or required based on your specific business automation needs.

Key benefits of request for information feature

Enhanced workflow quality control: By incorporating human oversight at critical decision points, you help ensure that automated processes maintain high accuracy and appropriate judgment for complex or sensitive business cases.

Reduced manual workflow monitoring: Instead of constant supervision, you can strategically pause automated workflows only when human input is genuinely needed. This reduces overall manual oversight requirements.

Improved business decision making: Human reviewers provide context-aware insights and handle edge cases that may be challenging for automated systems alone in your Copilot Studio workflows.

Scalable workflow review process: Multiple assignees can be designated for requests, providing more coverage and redundancy while maintaining efficient response times in your automation.

Getting started

Request for information is available exclusively in Copilot Studio agent flows. This gives you the power to seamlessly blend human expertise with automated workflow efficiency.

To get started with RFI in your workflow automation:

1. Familiarize yourself with RFI capabilities in our comprehensive Copilot Studio documentation
2. Open your agent flow in Copilot Studio and add the “Request for information” action from the Human in the loop connector
3. Configure your workflow request by defining the title, message, assignees, and required input fields for your automation
4. Deploy confidently, knowing RFI integrates seamlessly with your existing Copilot Studio agent flow actions

We have one question for you: What human expertise will you strategically incorporate into your automated workflows?

Helpful Resources

• Request for information documentation
• Agent flows introduction

Tell us about your experience

Have you tried request for information in your Copilot Studio workflows? We’d love to hear about your automation experience through the feedback tools in Copilot Studio or by contacting us directly. Your insights help us continue improving intelligent workflow automation capabilities that truly serve your business needs.

Ready to transform your workflow automation? Start incorporating request for information into your Copilot Studio agent flows today and discover the power of strategic human-AI collaboration.

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The post Introducing request for information in Copilot Studio agent flows appeared first on Microsoft Copilot Blog.

May 2025 was a big month for Microsoft Copilot Studio and there are a ton of features to catch up on. In this edition of our monthly roundup, we’re recapping the biggest news from Microsoft Build 2025, giving a couple of important updates, and announcing new resources for Copilot Studio adoption and training. 

Microsoft Build 2025 roundup: Our biggest announcements

Microsoft Build 2025 brought a wave of updates to Copilot Studio and Microsoft Dataverse, the operational database for agents, introducing powerful new tools for multi-agent systems, enterprise data access, and custom AI tuning. Here’s a quick overview of what dropped in Copilot Studio.

Multi-agent orchestration: Copilot Studio now supports multi-agent orchestration, allowing agents built with Microsoft 365, Microsoft Azure AI, and Microsoft Fabric to collaborate by delegating tasks and sharing results to complete complex workflows. Copilot Studio will also support the open Agent2Agent (A2A) protocol, allowing agents to connect to those built on third-party platforms.

Computer use in agents: The new computer use capability, currently available to eligible United States-based customers, allows Copilot Studio agents to perform tasks across desktop and web applications, automating repetitive processes like data entry and document processing through AI-powered UI interactions.

Bring your own model and Microsoft Copilot tuning: Makers can access more than 11,000 models in Azure AI Foundry and fine-tune them using enterprise data for even more context-rich and valuable agent responses.

Other updates include:

• Model Context Protocol (MCP)’s general availability and updates.
• In-conversation agent recommendations and handoff.
• Code interpreter for Python-powered logic using natural language.
• Instant agent sharing across your organization.
• New maker controls for knowledge, including options for file collections, code interpreter, moderation, and user feedback.
• SharePoint and WhatsApp channels to publish agents to.
• Security and governance updates.

Get all the details on these announcements in Corporate Vice President Lili Cheng’s blog post. For an even more in-depth update, learn more about how Dataverse supports all these in Corporate Vice President Nirav Shah’s blog post from Microsoft Build 2025. You can also see a roundup of all the Microsoft Dataverse sessions presented at Microsoft Build 2025.

Build, publish, and monetize agents with the Agent Store

Welcome to the Agent Store, your centralized, curated marketplace for agents built by Microsoft, trusted partners, and customers. Accessible through the left side navigation in Microsoft 365 Copilot Chat, the Agent Store makes it easier to browse, try out, and share agents for your business processes without having to build them from scratch. These agents work seamlessly across your Microsoft 365 ecosystem, so you can install an agent once and use it in multiple places. It’s all about speed, trust, and impact at scale.

Right now, the Agent Store has more than 70 agents, ranging from knowledge assistants to complex multi-modal orchestrators. You’ll have access to more as makers and software development vendors build and share new agents in the coming months—and the store will offer personalized agent discovery based on your organizational context. For developers, the Agent Store also provides a platform to share your innovative agents with millions of Microsoft Copilot users and grow your user base.

With robust support through Copilot Studio and the Microsoft 365 Agents Toolkit, the Agent Store includes both low-code and pro-code development options. Whichever way you prefer to build and deploy your agents, this marketplace helps you reduce development time and costs and get them out there faster.

Learn more about finding and publishing agents through the Agent Store on the Microsoft 365 Developer Blog.

Publish custom engine agents to Microsoft Copilot Chat: Now generally available

The ability to publish custom engine agents built in Copilot Studio directly to Copilot Chat is now generally available and automatically enabled. This means all customers can now unlock powerful new ways to customize and extend Copilot experiences with rich, domain-specific intelligence, right inside the tools people use every day.

Previously announced in public preview, this feature allows makers to publish agents built in Copilot Studio to Copilot, with full access to the features that make agents intelligent and useful. That includes topics, orchestration selection, autonomous triggers, analytics, and Azure AI integrations. These custom agents surface seamlessly across Microsoft 365 apps, including Microsoft Teams, Word, Excel, PowerPoint, and Office.

Custom engine agents in Copilot support messages, quick replies, Adaptive Cards, multi-turn interactions, and multi-message responses—all backed by robust governance and analytics in the Microsoft Power Platform admin center. Now you can confidently scale custom Copilot experiences across your workforce, without changes to existing agents.

General availability brings simplified deployment and seamless integration, turning every custom agent into a first-class participant in the Microsoft 365 Copilot experience. Read more about publishing and deploying agents on Microsoft Learn.

Microsoft Graph connectors are now Copilot connectors

Microsoft Graph connectors are now called Copilot connectors, a name that better reflects their role in powering the Microsoft 365 Copilot experience.

Copilot connectors bring external data into Microsoft 365 so Copilot and Copilot Studio agents can retrieve, apply reasoning to, and act on knowledge beyond the Microsoft 365 ecosystem. These connectors eliminate the need for duplicative uploads or clunky copy-paste workflows.

For developers and makers, Copilot connectors are the bridge between your existing systems and your AI-powered solutions. Whether you’re supporting sales teams to query customer relationship management (CRM) system records, surfacing insights from product documentation, or building custom agents grounded in real enterprise data, connectors make it possible to create truly contextual and intelligent experiences.

The change in name reflects a broader shift: AI isn’t just reading data anymore—it’s working with it. More than 40 Copilot connectors are already generally available or in public preview, including key sources like Gong, PagerDuty, and Unily, so connect your agents and systems in the Microsoft 365 admin center. Read more about building Copilot connectors on Microsoft Learn.

Accelerate agentic operations with new adoption resources

We’re excited to introduce two new resources designed to help your organization get started with Copilot Studio quickly and effectively. Whether you’re launching your first agent or scaling across teams, these tools are built to simplify the building process and drive measurable impact across your organization.

Microsoft Copilot Studio Adoption page

This centralized hub offers comprehensive guidance for seamless integration across teams. It includes:

• Step-by-step setup and deployment guidance.
• Role-based training for business users, developers and admins.
• Best practices and support documentation.
• Tools to help developers and leaders build and scale agents with confidence.

This page is your go-to destination for enabling success from day one. Explore the Copilot Studio AI Agents Hub.

Copilot Studio scenarios in the Microsoft Scenario Library

Explore 35 new custom agent scenarios across seven functional areas—each designed to showcase real-world value and accelerate adoption. Every scenario includes:

• A clear use case with key performance indicators (KPIs) and key users.
• Agent functionality and flow breakdown.
• Architecture and key considerations.

These resources are crafted to help you unlock the full potential of Copilot Studio—empowering your teams to innovate, automate, and scale with ease.

Free Microsoft Copilot Studio training for Microsoft Power Platform makers

If you or members of your team are already familiar with Microsoft Power Platform, you may be wondering how the skills you have built using Microsoft Power Apps or Microsoft Power Automate translate to the world of agents. Microsoft worked with Shane Young, a Microsoft most valuable player (MVP) of 20 years, as part of a paid collaboration to bring you more than four hours of free, hands-on training on YouTube.

This series of videos will help you get started with Copilot Studio at your own pace so you can walk away with your own agents, ready to use. The training series includes demos, step-by-step builds, and deep dives into product-specific features, broken down into four sections:

1. Introduction and demos (7 videos)
2. How to build a conversational agent (8 videos)
3. How to build an autonomous agent (10 videos)
4. Reusing your Microsoft Power Platform skills (4 videos)

Thousands of Microsoft Power Platform makers have already gone through the training, and we encourage you to spread the word and join in. Start watching or share with your team. You may be the makers who create the next best agent in the Agent Store.

More ways to stay up to date on all things Copilot Studio

Check out all the updates live as we ship them, as well as new features releasing in the next few months.

To learn more about Copilot Studio and how it can transform your organization’s productivity, visit the Copilot Studio website or sign up for our free trial. 

The post What’s new in Copilot Studio: May 2025  appeared first on Microsoft Copilot Blog.