AI agents are quickly being incorporated into core company processes across every industry. However, before adopting agentic AI systems, organizations must ask themselves a series of questions: What are our goals and use cases? What guardrails do we have in place? How will we manage data access and integration? How can we ensure our agentic systems are trustworthy? And how will we monitor and oversee these new digital teammates?
This is just scratching the surface. The decision-making process should, by default, be well-considered and lengthy — at least in terms of the factors considered. But after adoption, how can companies ensure the agentic systems they onboarded can scale? That’s the question Nitasha Chopra, VP and COO of Microsoft Copilot Studio, explores in a recent blog post.
Six Key Trends
“In 2025, we laid the groundwork for what scalable, impactful agentic work should look like,” says Chopra. “In 2026, we believe the organizations that benefit most will be the ones that build on that foundation.”
Chopra lays out six trends — and how Microsoft supports them — that she believes define how organizations with agentic AI in place can ensure it is well entrenched in the business and able to scale.
Ability For Anyone to Turn Intent into Agents
Chopra notes that historically, when a company wanted to launch an agent, it needed to translate the desired outcome into technical specifications. This process excluded non-technical users. With tools like Copilot Studio and Agent Builder in Microsoft 365 Copilot Chat, users can now create agents using natural language, dramatically lowering the barrier to entry and making agent creation faster and more widely adopted.
Agents that Can Own Workflows End-to-End
There has been a shift — an incredibly quick shift — in how agents are being deployed. Not long ago, AI agents were primarily used as assistants; now, they can handle workflows independently. Chopra points to the capabilities of agent flows and Microsoft’s Workflows Agent to create repeatable, automated business processes.
Power to Coordinate Agents for Real Outcomes
Agent sprawl is already becoming an issue in the agentic AI industry. That’s why leaders in the field are developing methods to contain it (think ServiceNow’s AI Agent Orchestrator, Workday’s Agent System of Record, or Microsoft’s Agent 365). But coordinating agents isn’t just about managing sprawl — it’s about ensuring that agents from various systems can communicate and work together effectively.
“Instead of designing one agent to handle every step, organizations can now compose agents that mirror how teams already work,” says Chopra. Microsoft’s solution is to advance open protocols such as Agent2Agent (A2A).
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Flexibility to Control Your Agent Models
Chopra explains how it quickly became clear that different tasks require different agents with different requirements or permissions. For that, companies need the flexibility to choose different models with distinct attributes to suit a specific agent without creating a fragmented experience or losing oversight.
Agents that can act across your systems:
Instead of just acting on suggestions made by AI agents, companies should embrace the fact that, thanks to capabilities like Model Context Protocol (MCP) and Microsoft’s computer use, agents can actively connect to systems and interact independently with interfaces.
The productivity gains from this approach are massive. Companies can reduce delays in workflows, eliminate manual errors, and ensure visibility as steps are meticulously calculated and logged.
Capability to scale agents without sacrificing control:
At the root of everything related to scalable AI is responsible AI — and that means governance. Chopra explains how Microsoft, through Copilot Studio, has addressed the challenge of scaling without losing control through a series of innovations, including lifecycle management, embedded enterprise controls, and Agent Evaluation, which enables automated agent testing within the Copilot Studio environment.
Final Thought
“Organizations that have all six [capabilities] aren’t just experimenting with agents,” says Chopra. “They’re operationalizing them, turning curiosity into confidence, and transmuting innovation into sustained business value.”
I want to close this article by adding my thoughts on the steps companies should take to adopt these trends. Organizations must ensure that strict governance and access controls are in place so they are always clear about who can create, deploy, and modify agents. With that governance comes a requirement for training and change management, so users consistently ask themselves one of the fundamental question we touched on at the start of this article: What is my goal?
Organizations should never fail to establish clear accountability and ownership for agents that run end-to-end workflows or automatically manipulate systems. Agents may be taking the lead, but there must always be a human in the loop where the buck stops. And when agents are integrated into systems, ensure secure, permissioned access to both external and internal resources.
When it comes to model choice, organizations should select vendors that offer a wide array of models, while carefully balancing cost management with performance optimization. This becomes especially important when deploying agents with different requirements at scale.
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