Microsoft is pushing Copilot Studio beyond the old “single agent, single task” model and into something much closer to an enterprise orchestration layer. In its latest update, the company says multi-agent systems are now generally available, alongside changes to the Prompt Builder, broader model choice, and tighter governance controls that are aimed at making production AI both more capable and more manageable. The headline is not just that agents can do more, but that they can finally work together in a structured way across Microsoft Fabric, the Microsoft 365 Agents SDK, and open Agent-to-Agent (A2A) protocols.
Copilot Studio has spent the past year evolving from a low-code tool for building conversational helpers into a serious platform for enterprise automation. Microsoft’s own roadmap shows a steady move toward reusable prompts, richer orchestration, more model options, and stronger admin controls, with each monthly update adding another layer of operational maturity. The April 2026 update continues that trajectory by emphasizing coordination rather than isolated agent creation.
That shift matters because most real business processes do not live inside a single app, a single data source, or a single team. A service desk agent may need ticket history, an HR policy agent may need access control context, and a finance agent may need a separate approval workflow. The practical problem has never been whether one agent can answer a question; it has been whether many agents can share context, respect boundaries, and finish a task without brittle handoffs.
Microsoft is framing this update as an answer to that problem. The company says Copilot Studio agents can now coordinate with Fabric agents for enterprise data and analytics, be orchestrated alongside experiences built with the Microsoft 365 Agents SDK, and communicate using A2A, an open protocol meant to support interoperation with first-party, partner, and third-party agents. That is an important clue about where Microsoft sees the market going: not toward one monolithic assistant, but toward networks of specialized assistants that collaborate.
There is also a governance story underneath the product story. Microsoft is pairing greater agent flexibility with more controls around prompt behavior, moderation sensitivity, evaluation automation, and integration surfaces such as MCP apps and the Apps SDK. In practice, that suggests the company wants customers to build more aggressively while also giving IT and security teams enough leverage to keep those systems inside policy.
Microsoft’s blog describes a common enterprise failure mode: teams build separate agents for separate functions, then discover that real workflows span data, policy, and action across multiple systems. When that happens, a one-agent approach quickly turns into custom glue code, duplicated business logic, and inconsistent user experiences. Multi-agent coordination is meant to reduce that friction by letting each agent specialize while still participating in a larger workflow.
It also changes the economics of reuse. Instead of rebuilding the same retrieval steps or business rules in multiple places, teams can expose capabilities once and compose them across experiences. That reduces duplication and helps organizations standardize how tasks are executed, which is often the difference between a promising pilot and a durable platform.
The update also signals that Microsoft expects AI deployments to become more distributed over time. Different business units may choose different models, different tools, and different integration patterns, but they still need to collaborate. A2A support is Microsoft’s answer to that reality, and it is a notable bet on interoperability as a competitive advantage.
Fabric integration is about data and analytics at scale. Microsoft says Copilot Studio agents will be able to work with Fabric agents to reason over enterprise data more directly, which matters because a lot of enterprise AI failures come from shallow context. If the agent cannot see the right operational data, it can sound convincing while still being wrong.
There is also a hidden architectural benefit here: fewer one-off connectors. When an agent can reason over a more complete business context, it is less likely to need bespoke plumbing for every question. That can make implementations easier to support at scale, especially when dozens of departments want their own copilots.
That is a big deal for enterprises that are already building across Teams, Microsoft 365, and custom apps. Instead of forcing every team into a single authoring model, Microsoft is trying to make agent behavior portable. The practical effect should be less duplicated logic, more shared services, and easier cross-app workflows.
In a market where companies are increasingly mixing vendors, models, and tools, interoperability becomes a survival trait. Microsoft is essentially saying that Copilot Studio should be able to participate in cross-platform systems rather than requiring every workflow to stay inside Microsoft’s own boundary. That makes the platform more attractive to large organizations with heterogeneous stacks.
The new setup divides work across multiple sub-agents handling areas like Azure, Microsoft 365, pricing, and trials, while a main orchestrator coordinates responses. That means the system can answer more complex questions without forcing one model to carry the burden of every product detail. It is also a good illustration of why multi-agent systems can outperform a single generalist agent when the knowledge surface gets too large.
That approach also supports context-aware experiences. Microsoft says the system can tailor responses depending on where the customer is on the site, which suggests that agent orchestration is being used not just to answer questions but to adapt answers to journey stage and intent. That is a subtle but important shift from generic Q&A to stateful service delivery.
It also hints at an important enterprise lesson: the more an agent becomes mission critical, the less acceptable brittle architecture becomes. If agent performance affects sales, support, or employee productivity, then orchestration quality is no longer an engineering nicety. It becomes a business KPI.
That might sound incremental, but it matters operationally. Prompt tuning often involves a lot of small iterations: revise the instruction, test, inspect output, adjust knowledge, and repeat. If each loop requires jumping between editors, context loss becomes a tax on productivity. Microsoft is trying to remove that tax.
The timing is important too. Enterprises are moving past novelty experiments and into situations where prompts must encode policy nuance, domain terminology, and business rules. A cleaner editing loop makes it more realistic for non-engineers and hybrid teams to handle the work without escalating every change to a development backlog.
The broader implication is that Copilot Studio is now as much a behavior design tool as a workflow builder. Makers are not only wiring actions together; they are shaping how the system reasons, retrieves, and responds. That is a much higher bar, but it is also where enterprise AI is headed.
At the same time, the Prompt Tool now supports Claude Opus 4.6 and Claude Sonnet 4.5 in paid experimental preview in the United States, adding to a broader multi-model strategy that gives makers more room to choose the right balance of quality, latency, and cost. Microsoft has been clear that Copilot Studio is becoming a multi-model environment rather than an all-in-one-model environment.
That is why Microsoft’s moderation controls are significant. Lowering sensitivity can unblock legitimate scenarios, but it also raises the stakes for oversight. In practice, organizations will need careful review processes to make sure content controls are loosened only where justified. Flexibility without policy discipline can create new risks as quickly as it solves old ones.
It also creates competitive insulation. If Copilot Studio can serve as the orchestration layer above several models, Microsoft becomes less vulnerable to model-level shifts in the market. The platform value then comes from governance, integration, and workflow control rather than from any one model alone.
ServiceNow and Azure DevOps connector quality improvements are now generally available, which should help agents retrieve better operational data and answer more actionable questions. Microsoft also says evaluation automation APIs are generally available through Microsoft Power Platform APIs and connectors, making it easier to wire quality checks into CI/CD pipelines. Those are the sort of details that determine whether AI systems stay useful after launch.
The same logic applies to evaluation automation. Once teams can run checks programmatically, they can treat agent quality more like software quality and less like a one-time launch event. That is an important shift for enterprises that need repeatable validation before they expand usage.
That raises the bar for usability and compliance at the same time. Real-time meeting assistance can be powerful, but it also touches on consent, data retention, and sensitive business conversations. Organizations will need to be deliberate about where and how these capabilities are deployed.
This is where governance becomes central. The more an agent can do, the more important it is to define who can create it, what it can access, and how it behaves under stress. Microsoft has been steadily adding controls in recent months, and this update fits that pattern by pairing broader interoperability with stronger production discipline.
That is why the combination of moderation settings, evaluation automation, and external app connectivity is so important. It suggests Microsoft is trying to make Copilot Studio a platform that can scale responsibly, not just rapidly. For enterprise buyers, that distinction will matter a great deal.
That puts pressure on rivals in several directions. Low-code platforms must compete on speed and enterprise readiness, model providers must compete on platform neutrality and integration, and workflow vendors must show they can support multi-agent coordination rather than isolated task automation. The competitive field is moving up the stack.
For Microsoft itself, the opportunity is significant. If Copilot Studio becomes the place where enterprises coordinate their agent estate, the company can deepen its role across Microsoft 365, Fabric, Power Platform, and the Azure ecosystem. That is exactly the kind of platform gravity Microsoft has historically been good at creating.
The longer-term bet is clearer: Microsoft wants Copilot Studio to become the place where enterprises design, govern, and coordinate AI work across their stack. If that vision holds, the platform will matter less as a chatbot builder and more as an operating layer for agent collaboration. That is a much bigger ambition, and a much more consequential one.
Source: Microsoft What’s new in Copilot Studio: Updates to multi-agent systems | Microsoft Copilot Blog
Copilot Studio has spent the past year evolving from a low-code tool for building conversational helpers into a serious platform for enterprise automation. Microsoft’s own roadmap shows a steady move toward reusable prompts, richer orchestration, more model options, and stronger admin controls, with each monthly update adding another layer of operational maturity. The April 2026 update continues that trajectory by emphasizing coordination rather than isolated agent creation.That shift matters because most real business processes do not live inside a single app, a single data source, or a single team. A service desk agent may need ticket history, an HR policy agent may need access control context, and a finance agent may need a separate approval workflow. The practical problem has never been whether one agent can answer a question; it has been whether many agents can share context, respect boundaries, and finish a task without brittle handoffs.
Microsoft is framing this update as an answer to that problem. The company says Copilot Studio agents can now coordinate with Fabric agents for enterprise data and analytics, be orchestrated alongside experiences built with the Microsoft 365 Agents SDK, and communicate using A2A, an open protocol meant to support interoperation with first-party, partner, and third-party agents. That is an important clue about where Microsoft sees the market going: not toward one monolithic assistant, but toward networks of specialized assistants that collaborate.
There is also a governance story underneath the product story. Microsoft is pairing greater agent flexibility with more controls around prompt behavior, moderation sensitivity, evaluation automation, and integration surfaces such as MCP apps and the Apps SDK. In practice, that suggests the company wants customers to build more aggressively while also giving IT and security teams enough leverage to keep those systems inside policy.
Why multi-agent systems matter now
The most important part of this release is not the feature list itself. It is the acknowledgement that scaling AI inside an organization is primarily an orchestration problem, not a chatbot problem. That is a meaningful change in how Microsoft positions Copilot Studio, because it elevates agent design from prompt tuning to systems design.Microsoft’s blog describes a common enterprise failure mode: teams build separate agents for separate functions, then discover that real workflows span data, policy, and action across multiple systems. When that happens, a one-agent approach quickly turns into custom glue code, duplicated business logic, and inconsistent user experiences. Multi-agent coordination is meant to reduce that friction by letting each agent specialize while still participating in a larger workflow.
From isolated copilots to coordinated systems
The real advantage of multi-agent architecture is composability. A specialist agent can be optimized for one domain, while an orchestrator agent decides when to hand work off, merge results, or ask for clarification. That creates more maintainable systems than one giant prompt trying to cover every edge case.It also changes the economics of reuse. Instead of rebuilding the same retrieval steps or business rules in multiple places, teams can expose capabilities once and compose them across experiences. That reduces duplication and helps organizations standardize how tasks are executed, which is often the difference between a promising pilot and a durable platform.
- Specialization improves quality because each agent can stay focused on a narrower job.
- Reuse reduces duplicated logic across departments.
- Orchestration makes complex workflows more reliable than ad hoc handoffs.
- Context sharing can improve answer quality when multiple systems are involved.
- Governance becomes easier when behavior is centralized and observable.
Why enterprises should care
For enterprises, this is about more than convenience. It is about turning AI from a front-end experience into a business process layer that can sit across departments without forcing each team to rebuild the same capability. That is especially important for regulated industries, where data access, auditability, and policy compliance matter as much as model quality.The update also signals that Microsoft expects AI deployments to become more distributed over time. Different business units may choose different models, different tools, and different integration patterns, but they still need to collaborate. A2A support is Microsoft’s answer to that reality, and it is a notable bet on interoperability as a competitive advantage.
Fabric, M365 Agents SDK, and A2A
The most strategically significant piece of the announcement is the trio of integration paths: Fabric, the Microsoft 365 Agents SDK, and A2A. Together, they outline how Microsoft wants Copilot Studio to sit in the middle of a broader enterprise AI ecosystem rather than merely at the edge of it.Fabric integration is about data and analytics at scale. Microsoft says Copilot Studio agents will be able to work with Fabric agents to reason over enterprise data more directly, which matters because a lot of enterprise AI failures come from shallow context. If the agent cannot see the right operational data, it can sound convincing while still being wrong.
Fabric as the data backbone
The Fabric angle is important because it ties conversational AI to governed data estates. Rather than treating analytics as a separate world from AI, Microsoft is trying to make data reasoning part of the agent fabric itself. That should be appealing to organizations that already treat Fabric as their central analytics layer.There is also a hidden architectural benefit here: fewer one-off connectors. When an agent can reason over a more complete business context, it is less likely to need bespoke plumbing for every question. That can make implementations easier to support at scale, especially when dozens of departments want their own copilots.
- Better context means fewer hallucination-prone gaps.
- Less custom wiring can shorten deployment timelines.
- Tighter analytics integration helps business users ask operational questions more naturally.
- Governed data access remains critical for compliance-heavy environments.
Microsoft 365 Agents SDK orchestration
The Microsoft 365 Agents SDK piece is equally important, because it bridges the low-code and pro-code worlds. Microsoft says teams can orchestrate Copilot Studio agents alongside agents built for Microsoft 365 experiences, which means functionality does not need to be rebuilt every time an experience moves between surfaces.That is a big deal for enterprises that are already building across Teams, Microsoft 365, and custom apps. Instead of forcing every team into a single authoring model, Microsoft is trying to make agent behavior portable. The practical effect should be less duplicated logic, more shared services, and easier cross-app workflows.
A2A interoperability
A2A is the clearest signal that Microsoft wants Copilot Studio to fit into a broader AI market. The company says its A2A support allows agents to directly communicate with and delegate work to other agents using an open protocol. That is not just a technical feature; it is a strategic statement about the future of enterprise AI.In a market where companies are increasingly mixing vendors, models, and tools, interoperability becomes a survival trait. Microsoft is essentially saying that Copilot Studio should be able to participate in cross-platform systems rather than requiring every workflow to stay inside Microsoft’s own boundary. That makes the platform more attractive to large organizations with heterogeneous stacks.
What Microsoft is learning from Ask Microsoft
Microsoft’s own Ask Microsoft web agent is being used as a proof point for the multi-agent direction. According to the company, traffic growth and expanding knowledge sources strained the older single-agent architecture, creating slower response times and motivating a redesign around generative orchestration and sub-agents. That is the kind of “customer zero” story that often reveals where a platform is headed next.The new setup divides work across multiple sub-agents handling areas like Azure, Microsoft 365, pricing, and trials, while a main orchestrator coordinates responses. That means the system can answer more complex questions without forcing one model to carry the burden of every product detail. It is also a good illustration of why multi-agent systems can outperform a single generalist agent when the knowledge surface gets too large.
Why decomposition improves performance
Decomposition is one of the oldest systems engineering tricks, and it applies cleanly to agent design. If one agent can specialize in pricing and another in troubleshooting, each can be tuned more precisely and updated independently. The orchestrator can then stitch together the results into something coherent for the user.That approach also supports context-aware experiences. Microsoft says the system can tailor responses depending on where the customer is on the site, which suggests that agent orchestration is being used not just to answer questions but to adapt answers to journey stage and intent. That is a subtle but important shift from generic Q&A to stateful service delivery.
- Smaller agents are easier to tune.
- Domain-specific logic is easier to maintain.
- Orchestrated answers can feel more coherent to users.
- Journey-aware responses can improve conversion and support outcomes.
What the Ask Microsoft example implies
The Ask Microsoft example suggests Microsoft sees multi-agent design as the answer to scale limits, not merely a premium feature. As knowledge bases grow and user traffic climbs, one agent has diminishing returns. Multiple coordinated agents can distribute the load while keeping answers relevant.It also hints at an important enterprise lesson: the more an agent becomes mission critical, the less acceptable brittle architecture becomes. If agent performance affects sales, support, or employee productivity, then orchestration quality is no longer an engineering nicety. It becomes a business KPI.
Prompt Builder gets faster and more practical
Microsoft’s second major theme is prompt authoring. The company argues that as agent experiences grow more sophisticated, prompt quality becomes increasingly important, and the old workflow was too fragmented. The new immersive Prompt Builder, now generally available, is meant to collapse that workflow into a single place inside the agent’s Tools tab.That might sound incremental, but it matters operationally. Prompt tuning often involves a lot of small iterations: revise the instruction, test, inspect output, adjust knowledge, and repeat. If each loop requires jumping between editors, context loss becomes a tax on productivity. Microsoft is trying to remove that tax.
Why embedded prompt editing matters
Embedded prompt editing is really about shortening the feedback loop. When makers can update instructions, switch models, add inputs or knowledge, and test immediately in one workspace, they are more likely to refine a prompt thoroughly rather than settling for “good enough.” That should translate into better production quality and faster delivery.The timing is important too. Enterprises are moving past novelty experiments and into situations where prompts must encode policy nuance, domain terminology, and business rules. A cleaner editing loop makes it more realistic for non-engineers and hybrid teams to handle the work without escalating every change to a development backlog.
Where this helps most
Microsoft points to scenarios like clinical documentation, where terminology can trigger overbroad safeguards or where prompt precision affects downstream usefulness. That example is telling because it shows how prompt authoring and governance are becoming intertwined. Better tooling is useful, but only if the system still respects the organization’s content and compliance requirements.The broader implication is that Copilot Studio is now as much a behavior design tool as a workflow builder. Makers are not only wiring actions together; they are shaping how the system reasons, retrieves, and responds. That is a much higher bar, but it is also where enterprise AI is headed.
Model choice and moderation controls
Microsoft is also expanding the set of controls available to makers. The company says content moderation settings for prompts are now generally available in supported regions, and that these controls can reduce harmful-content sensitivity on managed models when default safeguards are too restrictive. This is especially relevant in industries where otherwise benign language may be flagged because of domain-specific context.At the same time, the Prompt Tool now supports Claude Opus 4.6 and Claude Sonnet 4.5 in paid experimental preview in the United States, adding to a broader multi-model strategy that gives makers more room to choose the right balance of quality, latency, and cost. Microsoft has been clear that Copilot Studio is becoming a multi-model environment rather than an all-in-one-model environment.
Why model choice is becoming a governance issue
Model choice used to be a technical preference. In enterprise AI, it is increasingly a governance decision. Different models vary in reasoning depth, speed, pricing, and behavior under policy constraints, which means platform teams need more than a default selection; they need a managed portfolio.That is why Microsoft’s moderation controls are significant. Lowering sensitivity can unblock legitimate scenarios, but it also raises the stakes for oversight. In practice, organizations will need careful review processes to make sure content controls are loosened only where justified. Flexibility without policy discipline can create new risks as quickly as it solves old ones.
- More models create more tuning options.
- Moderation controls can reduce false positives in regulated domains.
- Experimental preview status means makers should test cautiously.
- Regional availability still matters for multinational deployments.
The strategic value of a multi-model stack
A multi-model stack gives Microsoft a way to keep Copilot Studio relevant to a broader set of customer needs. Some teams will prioritize speed and cost; others will want stronger reasoning or longer context handling. By exposing those choices inside the platform, Microsoft reduces the temptation for customers to build around it.It also creates competitive insulation. If Copilot Studio can serve as the orchestration layer above several models, Microsoft becomes less vulnerable to model-level shifts in the market. The platform value then comes from governance, integration, and workflow control rather than from any one model alone.
Automation, meetings, and retrieval quality
Beyond the headline features, Microsoft says it has released several updates across automation, meetings, retrieval, and integration quality. These updates may look smaller individually, but they matter because they are the connective tissue that makes an AI platform dependable in daily use.ServiceNow and Azure DevOps connector quality improvements are now generally available, which should help agents retrieve better operational data and answer more actionable questions. Microsoft also says evaluation automation APIs are generally available through Microsoft Power Platform APIs and connectors, making it easier to wire quality checks into CI/CD pipelines. Those are the sort of details that determine whether AI systems stay useful after launch.
Better integrations mean better answers
Integrations are not just plumbing. In an agent system, they are often the source of truth. If the connector returns incomplete or poorly structured data, the agent’s response quality drops even if the model itself is strong. That is why connector quality work is a meaningful release, even if it does not sound glamorous.The same logic applies to evaluation automation. Once teams can run checks programmatically, they can treat agent quality more like software quality and less like a one-time launch event. That is an important shift for enterprises that need repeatable validation before they expand usage.
Meetings, transcripts, and real-time collaboration
Microsoft also says agents for Microsoft Teams meetings can now access real-time meeting transcripts and group chat. That opens the door to use cases like answering questions mid-meeting, surfacing relevant information, and tracking follow-ups as discussion unfolds. In other words, the agent is moving closer to the live workstream.That raises the bar for usability and compliance at the same time. Real-time meeting assistance can be powerful, but it also touches on consent, data retention, and sensitive business conversations. Organizations will need to be deliberate about where and how these capabilities are deployed.
Governance, MCP, and the broader ecosystem
Microsoft’s update is also about trust. The company says MCP apps and Apps SDK support have expanded how agents connect to external work apps, making it easier for agents to take action across a broader ecosystem. That is a major theme in the current AI platform race: the winner is not just the company with the best model, but the one that can safely connect to the most business systems.This is where governance becomes central. The more an agent can do, the more important it is to define who can create it, what it can access, and how it behaves under stress. Microsoft has been steadily adding controls in recent months, and this update fits that pattern by pairing broader interoperability with stronger production discipline.
Why governance now sits at the center
Governance used to be the part of the story that came after deployment. In an agentic world, it has to be designed in from the start. Once agents can talk to other agents, query external systems, and execute tasks, the risk surface expands quickly.That is why the combination of moderation settings, evaluation automation, and external app connectivity is so important. It suggests Microsoft is trying to make Copilot Studio a platform that can scale responsibly, not just rapidly. For enterprise buyers, that distinction will matter a great deal.
- MCP support broadens integration reach.
- Apps SDK support helps agents take action, not just answer.
- Governance controls are becoming more important as autonomy grows.
- Evaluation APIs support repeatable quality management.
Competitive implications for Microsoft and its rivals
This update also tells us something about the market. Microsoft is not merely reacting to the generative AI wave; it is trying to shape the next layer of the stack. By emphasizing orchestration, interoperability, and governance, Microsoft is positioning Copilot Studio as an enterprise control plane for agent systems.That puts pressure on rivals in several directions. Low-code platforms must compete on speed and enterprise readiness, model providers must compete on platform neutrality and integration, and workflow vendors must show they can support multi-agent coordination rather than isolated task automation. The competitive field is moving up the stack.
Who is most exposed
Vendors that only offer point solutions may struggle if customers increasingly demand orchestrated systems that span multiple apps and model families. Likewise, platforms that cannot handle governance at scale may lose enterprise deals even if their demos are impressive. Microsoft is betting that buyers will reward operational maturity over flashy novelty.For Microsoft itself, the opportunity is significant. If Copilot Studio becomes the place where enterprises coordinate their agent estate, the company can deepen its role across Microsoft 365, Fabric, Power Platform, and the Azure ecosystem. That is exactly the kind of platform gravity Microsoft has historically been good at creating.
Strengths and Opportunities
Microsoft’s latest Copilot Studio update is strongest where enterprise buyers need it most: coordination, control, and practical reuse. It is not just adding more AI features; it is building a framework that can survive contact with real organizational complexity. That gives the company several clear advantages if the rollout executes cleanly.- Multi-agent orchestration can reduce brittle handoffs across systems.
- Fabric integration improves enterprise context and analytics reach.
- Microsoft 365 Agents SDK support encourages reuse across productivity workflows.
- A2A interoperability future-proofs the platform for heterogeneous AI ecosystems.
- Prompt Builder improvements shorten iteration cycles for makers.
- Moderation controls help organizations adapt AI behavior to domain requirements.
- Evaluation automation supports more disciplined CI/CD-style AI operations.
- Expanded model choice gives teams more flexibility on speed, cost, and reasoning depth.
Risks and Concerns
The same features that make this update attractive also introduce new risks. Multi-agent systems are more powerful, but they are also more complex to observe, debug, and govern. The more autonomy Microsoft enables, the more carefully customers will need to manage permissions, failure modes, and quality drift.- Complex orchestration can create new debugging challenges.
- Broader integrations increase the blast radius of configuration errors.
- Looser moderation settings may require stricter human oversight.
- Experimental model previews can be risky for production dependency.
- Real-time meeting access raises privacy and consent questions.
- A2A interoperability may complicate trust boundaries across vendors.
- Multi-model sprawl can make policy and cost management harder.
Looking Ahead
The most likely near-term trend is continued expansion of Microsoft’s agent ecosystem across channels, workflows, and development surfaces. The company has already signaled that more updates are coming in April 2026, and that should be read as a sign that the platform is still being actively layered out rather than stabilized. For customers, that means more opportunity, but also more need for planning.The longer-term bet is clearer: Microsoft wants Copilot Studio to become the place where enterprises design, govern, and coordinate AI work across their stack. If that vision holds, the platform will matter less as a chatbot builder and more as an operating layer for agent collaboration. That is a much bigger ambition, and a much more consequential one.
- Watch for broader GA rollout timing across eligible customers.
- Monitor how A2A support develops with third-party ecosystems.
- Track governance enhancements as agent autonomy increases.
- See whether more models are added to the prompt and orchestration stack.
- Pay attention to voice and workflow features promised for the next wave of updates.
Source: Microsoft What’s new in Copilot Studio: Updates to multi-agent systems | Microsoft Copilot Blog
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