Microsoft’s Copilot strategy is entering a new phase, and the shift matters far beyond a single product update. What began as an AI assistant for drafting, summarizing, and answering is now evolving into an execution layer for enterprise work, with Microsoft pairing its own models with Anthropic’s technology to handle long-running, multi-step tasks inside Microsoft 365 Copilot. That pivot is a strong signal that the race in enterprise AI is no longer just about who writes the best prompt response; it is about who can safely let software do the work. In practical terms, Microsoft is betting that the future of productivity software will be defined by autonomous AI agents, not just chatbots.
For years, enterprise AI was mostly framed as a helper. It could summarize a meeting, draft an email, or generate a slide, but a person still had to move the work forward, check the output, and connect the dots between systems. Microsoft’s latest Copilot wave, built around Copilot Cowork, changes that framing by emphasizing long-running work that unfolds over time and by bringing Anthropic-powered capabilities into the Microsoft 365 ecosystem.
That matters because enterprise software has always been constrained by the shape of the interface. Traditional applications were designed for human operators clicking through menus and forms, not for agents that need to traverse workflows, act on data, and keep state across multiple steps. Microsoft is now openly describing Copilot as model diverse by design, with OpenAI and Anthropic both playing roles in the stack, which suggests the company is treating models as interchangeable components rather than a single strategic bet.
The change also reflects a broader market reality. McKinsey’s 2025 survey says AI is already in use across most organizations, and that adoption is increasingly shifting from experimentation toward scaling and agentic use cases. The same survey found that 62% of organizations are experimenting with AI agents, while the broader picture shows that AI use is now mainstream in business functions rather than confined to pilots. That is important because Microsoft’s move is not happening in a vacuum; it is responding to a market where companies are ready to demand more than content generation.
That shift is what gives the announcement weight. It means Copilot is no longer just an add-on that improves convenience; it becomes a platform that can affect workflow design, staffing, and process ownership. If an agent can update a spreadsheet, draft a presentation, or carry a task across email and documents, then the software itself begins to participate in the business process.
The change is also a competitive signal. Microsoft is acknowledging that no single model is sufficient for all enterprise work, which is why it is blending OpenAI and Anthropic capabilities in the same experience. That is a strong rebuke to the idea that enterprise AI will remain a single-vendor stack.
That is crucial because enterprise buyers have long worried that AI tools bypassed the very controls they spent years building. Microsoft’s pitch is that Copilot can be useful without becoming a compliance blind spot. If that promise holds, Copilot becomes more than a productivity feature; it becomes a control point for AI at work.
That distinction is important because enterprises do not buy “intelligence” in the abstract. They buy repeatable business outcomes, reduced cycle times, and fewer manual handoffs. Agents become compelling when they can be trusted to execute familiar workflows reliably, especially where repetitive work drains employee time.
Microsoft’s own Wave 3 narrative fits that definition. It talks about creating, editing, and refining content from start to finish, and about agents in chat that can schedule meetings or send email without context switching. Those are not trivial upgrades; they are signs that AI is being embedded as an active participant in the workflow.
This is also why software architecture is becoming a competitive differentiator again. For a human user, a polished interface can hide complexity. For an agent, the underlying structure matters much more than the surface design. That is a quiet but profound inversion of how enterprise software has been sold for the last two decades.
That is more than a technical preference. It is a business strategy. A model-diverse platform can reduce vendor lock-in, give Microsoft leverage in partner negotiations, and make the Copilot ecosystem more attractive to large customers that want options. It also helps Microsoft tell a more credible enterprise story, because enterprises rarely want to depend on a single AI provider for every use case.
The practical effect is visible in Microsoft 365 Copilot, where Anthropic models can now be used in specific experiences and where customers in certain regions can choose Anthropic models across Word, Excel, and PowerPoint. That broader availability suggests Microsoft sees model selection as a feature, not a complication.
For Anthropic, the upside is obvious. Microsoft distribution gives Claude a larger enterprise footprint and a stronger credibility story in workplace software. Anthropic’s presence inside Microsoft products also positions it as a serious contender in enterprise productivity rather than just a standalone model vendor.
For rivals like Google Workspace, Salesforce, and standalone agent startups, the message is equally clear. Microsoft is trying to make the productivity suite itself the AI agent platform, which could make it harder for third-party tools to justify their own layer unless they offer something distinctly better. That is a classic platform move: absorb the use case before the ecosystem can form around it.
This is not just an IT concern. If an agent can send email, approve requests, or update records, then its identity becomes part of the control plane for the business. Enterprises will need to know not just what the agent did, but under whose authority, with which token, and against which policy. Without that answer, autonomy becomes a liability.
Microsoft’s Agent 365 positioning reinforces that direction by describing a control plane for agents that can deploy, govern, and manage them across environments. That is a sign that Microsoft understands the problem is no longer just model performance; it is lifecycle management.
That matters because debugging an autonomous workflow is not the same as troubleshooting a normal app. If an agent makes the wrong call somewhere in the middle of a chain, you need to know which input, tool call, or policy triggered the error. Without that traceability, the organization cannot learn from mistakes or certify the system for broader use.
Microsoft appears to be leaning into that problem by emphasizing transparency, reviewability, and reversibility inside Office apps. That approach is sensible because it keeps humans in the loop while preserving a clean audit trail. It is also far more enterprise-friendly than letting agents operate in opaque side channels.
That dual expectation is exactly why agentic AI is attracting attention. A simple assistant may improve productivity, but an agent can potentially change the structure of work. Once a system can handle parts of a workflow end to end, management starts to think in terms of throughput, controls, and role redesign rather than just prompt quality.
Microsoft’s new positioning aligns with that shift. It is not selling Copilot merely as a convenience layer; it is framing it as a business platform tied to productivity, security, identity, and governance. That is a more mature and more defensible enterprise story.
In practice, that means the success of Copilot Cowork will depend less on headline demos and more on whether organizations can safely adopt it inside existing workflows. If the agent can save time but introduces security friction, the enterprise case weakens quickly. If it can operate inside controls that admins already understand, adoption becomes much more plausible.
IBM’s work on agentic governance and identity reinforces that boundary by stressing secure delegation, policy enforcement, and audit-ready accountability. The underlying principle is simple: machines may act quickly, but humans must remain accountable. That is the real meaning of trustworthy automation.
The challenge, of course, is that many organizations still have immature governance frameworks. The DesignRush article highlights this gap, and that is a legitimate concern. It is one thing to demo an agent creating work; it is another to prove that the same agent can be allowed into production without exposing the business to compliance and security failures.
This is one of the most underappreciated consequences of the Copilot shift. Agentic AI does not eliminate enterprise debt; it exposes it faster. Companies with clean data, clear access rules, and well-defined workflows will see benefits sooner, while everyone else will discover that the bottleneck was always the stack beneath the model.
This is not a small change for the software industry. It means product teams will need to think about agent compatibility the way they already think about mobile compatibility or accessibility. If software is hard for an AI agent to understand or navigate, it may become less competitive inside AI-heavy enterprises.
That also affects low-code and no-code tooling. Microsoft Copilot Studio and Agent 365 are part of a broader effort to make agents easier to build, govern, and deploy. The likely outcome is that more business process automation will be assembled by nontraditional builders, while professional developers own the foundations that make those automations safe.
That stack is powerful because it keeps users close to their work and keeps administrators close to the controls. It also helps Microsoft defend its ecosystem position, because the more AI work happens inside Microsoft 365, the harder it becomes for a rival platform to displace it. Platform gravity matters, and Microsoft is trying to increase it.
There is also the broader market risk that AI agent hype outruns practical business value. McKinsey’s data shows adoption is widespread, but scaling and profit impact remain uneven. That means many organizations may experiment with agents without seeing immediate returns, which could lead to disappointment if vendors overpromise and underdeliver.
What should enterprises watch most closely? Not the marketing language, but the operational details. The winners in this phase will be the vendors that make AI execution feel boringly safe—predictable, observable, reversible, and easy to govern. In enterprise software, boring is often what scales.
Source: DesignRush Microsoft’s Copilot Expansion Accelerates the Rise of Autonomous AI Agents, Experts Say
For years, enterprise AI was mostly framed as a helper. It could summarize a meeting, draft an email, or generate a slide, but a person still had to move the work forward, check the output, and connect the dots between systems. Microsoft’s latest Copilot wave, built around Copilot Cowork, changes that framing by emphasizing long-running work that unfolds over time and by bringing Anthropic-powered capabilities into the Microsoft 365 ecosystem.That matters because enterprise software has always been constrained by the shape of the interface. Traditional applications were designed for human operators clicking through menus and forms, not for agents that need to traverse workflows, act on data, and keep state across multiple steps. Microsoft is now openly describing Copilot as model diverse by design, with OpenAI and Anthropic both playing roles in the stack, which suggests the company is treating models as interchangeable components rather than a single strategic bet.
The change also reflects a broader market reality. McKinsey’s 2025 survey says AI is already in use across most organizations, and that adoption is increasingly shifting from experimentation toward scaling and agentic use cases. The same survey found that 62% of organizations are experimenting with AI agents, while the broader picture shows that AI use is now mainstream in business functions rather than confined to pilots. That is important because Microsoft’s move is not happening in a vacuum; it is responding to a market where companies are ready to demand more than content generation.
Why Microsoft’s Copilot Shift Matters
Microsoft is not merely adding another model option. It is redefining what Copilot is supposed to do, and that redefinition has strategic consequences for product design, licensing, and enterprise trust. The company’s March 2026 announcements position Copilot as a place where people can move from conversation to creation to execution without leaving the Microsoft 365 environment.From assistant to worker
The biggest change is philosophical. A classic assistant waits for a prompt and returns an answer; an AI agent can keep going, chain tasks together, and work across systems with limited supervision. Microsoft’s own language now emphasizes “long-running, multi-step work,” which is a step beyond the old assistant model and closer to delegated digital labor.That shift is what gives the announcement weight. It means Copilot is no longer just an add-on that improves convenience; it becomes a platform that can affect workflow design, staffing, and process ownership. If an agent can update a spreadsheet, draft a presentation, or carry a task across email and documents, then the software itself begins to participate in the business process.
The change is also a competitive signal. Microsoft is acknowledging that no single model is sufficient for all enterprise work, which is why it is blending OpenAI and Anthropic capabilities in the same experience. That is a strong rebuke to the idea that enterprise AI will remain a single-vendor stack.
The real significance for Windows and Microsoft 365
For Microsoft 365 customers, the practical impact is more immediate than the branding. Copilot is moving deeper into Word, Excel, PowerPoint, Outlook, and chat, making the apps themselves more capable of producing finished work rather than offering suggestions. Microsoft says the new experiences are grounded in Work IQ, respect existing permissions, and preserve governance controls such as sensitivity labels and tenant-level protections.That is crucial because enterprise buyers have long worried that AI tools bypassed the very controls they spent years building. Microsoft’s pitch is that Copilot can be useful without becoming a compliance blind spot. If that promise holds, Copilot becomes more than a productivity feature; it becomes a control point for AI at work.
- Copilot is moving toward workflow ownership, not just assistance.
- Model diversity is becoming part of Microsoft’s enterprise value proposition.
- The platform is being built to work inside existing security and compliance boundaries.
- Microsoft 365 is increasingly being positioned as an AI operating surface.
The Enterprise AI Agent Trend
The DesignRush piece is right about the direction of travel: enterprise AI is moving from assistance to execution. That trend is supported by both vendor announcements and industry surveys, which show that organizations are no longer satisfied with simple chat interactions. They want systems that can complete work, coordinate steps, and produce outcomes.What “agentic” really means
The term agentic AI gets thrown around often, but in enterprise software it has a practical meaning. It refers to systems that can plan, act, evaluate, and continue across multiple steps rather than stopping at a single answer. IBM’s descriptions of AI agent types and governance frameworks underline that agents are increasingly being treated as a distinct operational layer, not just a chat interface with better branding.That distinction is important because enterprises do not buy “intelligence” in the abstract. They buy repeatable business outcomes, reduced cycle times, and fewer manual handoffs. Agents become compelling when they can be trusted to execute familiar workflows reliably, especially where repetitive work drains employee time.
Microsoft’s own Wave 3 narrative fits that definition. It talks about creating, editing, and refining content from start to finish, and about agents in chat that can schedule meetings or send email without context switching. Those are not trivial upgrades; they are signs that AI is being embedded as an active participant in the workflow.
Why modular systems are winning
One of the strongest arguments in the DesignRush article is that tightly coupled applications are harder for agents to traverse. That is a sound observation. Modular, well-instrumented systems are easier for agents to use because the task boundaries are clearer, the APIs are more explicit, and the behavior is more predictable.This is also why software architecture is becoming a competitive differentiator again. For a human user, a polished interface can hide complexity. For an agent, the underlying structure matters much more than the surface design. That is a quiet but profound inversion of how enterprise software has been sold for the last two decades.
- Agents need clear task boundaries.
- Modular systems are easier to automate safely.
- APIs, telemetry, and permissions matter more than flashy UI.
- The best enterprise software will be the easiest for both humans and agents to operate.
Microsoft’s Multi-Model Strategy
Microsoft’s decision to incorporate Anthropic into Copilot is one of the most important strategic moves in the current AI market. It signals that Microsoft is no longer presenting OpenAI as the exclusive engine behind its enterprise AI story. Instead, it is adopting a multi-model approach that is designed to optimize for task fit, customer choice, and operational resilience.Why model diversity matters
Different models have different strengths. Some perform better on creative drafting, some on reasoning, some on structured workflows, and some on long-context tasks. Microsoft’s enterprise messaging now reflects that reality, arguing that no single model should be treated as the universal answer for all workplace jobs.That is more than a technical preference. It is a business strategy. A model-diverse platform can reduce vendor lock-in, give Microsoft leverage in partner negotiations, and make the Copilot ecosystem more attractive to large customers that want options. It also helps Microsoft tell a more credible enterprise story, because enterprises rarely want to depend on a single AI provider for every use case.
The practical effect is visible in Microsoft 365 Copilot, where Anthropic models can now be used in specific experiences and where customers in certain regions can choose Anthropic models across Word, Excel, and PowerPoint. That broader availability suggests Microsoft sees model selection as a feature, not a complication.
Competitive implications for OpenAI, Anthropic, and rivals
This shift creates an awkward but important competitive dynamic. Microsoft remains deeply connected to OpenAI, yet it is also expanding with Anthropic, which reduces the perception that OpenAI is the sole gatekeeper of Microsoft’s AI ambitions. For OpenAI, that means less exclusivity inside one of the most powerful enterprise software ecosystems in the world.For Anthropic, the upside is obvious. Microsoft distribution gives Claude a larger enterprise footprint and a stronger credibility story in workplace software. Anthropic’s presence inside Microsoft products also positions it as a serious contender in enterprise productivity rather than just a standalone model vendor.
For rivals like Google Workspace, Salesforce, and standalone agent startups, the message is equally clear. Microsoft is trying to make the productivity suite itself the AI agent platform, which could make it harder for third-party tools to justify their own layer unless they offer something distinctly better. That is a classic platform move: absorb the use case before the ecosystem can form around it.
- Microsoft is reducing dependence on a single model vendor.
- Anthropic gains scale and enterprise visibility.
- OpenAI loses some exclusivity inside Microsoft 365.
- Competitors face a stronger, more integrated Copilot platform.
Infrastructure Gaps and the Control Problem
The hardest part of agentic AI is not making agents act. It is making them act safely, traceably, and in ways that fit real enterprise governance. As autonomy increases, the burden shifts toward identity, logging, access control, observability, and data quality. Those are the less glamorous layers, but they are the ones that determine whether agents can actually be deployed at scale.Identity is not a side issue
A major issue raised by both Microsoft and IBM is that agents need to be treated differently from human users. They should have their own identities, permissions, and delegated access scopes rather than inheriting a person’s full authority by default. IBM’s agent identity guidance is especially direct on this point, warning that without unique identity and governed delegation, privilege sprawl and audit failures become inevitable.This is not just an IT concern. If an agent can send email, approve requests, or update records, then its identity becomes part of the control plane for the business. Enterprises will need to know not just what the agent did, but under whose authority, with which token, and against which policy. Without that answer, autonomy becomes a liability.
Microsoft’s Agent 365 positioning reinforces that direction by describing a control plane for agents that can deploy, govern, and manage them across environments. That is a sign that Microsoft understands the problem is no longer just model performance; it is lifecycle management.
Observability and audit trails
The second major infrastructure gap is visibility. When a human user works through a process, intent is often obvious enough to reconstruct after the fact. When an agent performs a sequence of steps across multiple systems, that intent must be captured by design. IBM’s governance and observability messaging makes the case that enterprises need to track what agents are doing across workflows and measure outcomes in real time.That matters because debugging an autonomous workflow is not the same as troubleshooting a normal app. If an agent makes the wrong call somewhere in the middle of a chain, you need to know which input, tool call, or policy triggered the error. Without that traceability, the organization cannot learn from mistakes or certify the system for broader use.
Microsoft appears to be leaning into that problem by emphasizing transparency, reviewability, and reversibility inside Office apps. That approach is sensible because it keeps humans in the loop while preserving a clean audit trail. It is also far more enterprise-friendly than letting agents operate in opaque side channels.
- Identity must be agent-specific.
- Authorization should be scoped and delegated.
- Observability is essential for trust.
- Audit trails turn autonomy into something governable.
AI Adoption Is Now a Management Problem
The McKinsey survey numbers are useful because they show that the AI conversation has moved beyond novelty. AI is now embedded in business operations across a wide range of functions, and the challenge has shifted from whether to adopt AI to how to manage it. That is why the question of governance is now just as important as model quality.Efficiency is only part of the story
According to McKinsey’s 2025 findings, 80% of organizations say efficiency is a core goal of their AI efforts, while 64% say AI is already helping them innovate. Those figures matter because they show AI is being judged both as a cost tool and as a growth tool. Enterprises are no longer asking only whether AI saves time; they are also asking whether it opens new ways of working.That dual expectation is exactly why agentic AI is attracting attention. A simple assistant may improve productivity, but an agent can potentially change the structure of work. Once a system can handle parts of a workflow end to end, management starts to think in terms of throughput, controls, and role redesign rather than just prompt quality.
Microsoft’s new positioning aligns with that shift. It is not selling Copilot merely as a convenience layer; it is framing it as a business platform tied to productivity, security, identity, and governance. That is a more mature and more defensible enterprise story.
Enterprise readiness versus consumer excitement
The consumer narrative around AI often focuses on delight and speed. Enterprise buyers care about trust, compliance, retention, permissions, and operational consistency. That difference is why Microsoft’s Copilot expansion is significant: it tries to satisfy both the user’s desire for convenience and the administrator’s need for control.In practice, that means the success of Copilot Cowork will depend less on headline demos and more on whether organizations can safely adopt it inside existing workflows. If the agent can save time but introduces security friction, the enterprise case weakens quickly. If it can operate inside controls that admins already understand, adoption becomes much more plausible.
- AI adoption is now a management discipline.
- Efficiency and innovation are both driving budgets.
- The real buyer is often IT, security, or operations leadership.
- Consumer-style AI excitement is not enough for enterprise scale.
Governance, Compliance, and the Human Boundary
Autonomy is only useful if it comes with clear limits. That is why governance is becoming central to the enterprise AI debate, and why Microsoft and IBM alike are emphasizing oversight rather than unchecked delegation. The more authority an agent receives, the more explicit the rules must be.Defining where the machine stops
One of the hardest organizational questions is deciding which actions an agent can take independently and which still require human approval. Microsoft’s current approach suggests a gradual model: agents can help execute work, but governance and permission boundaries remain in place. That is the right default for enterprises, especially in regulated sectors.IBM’s work on agentic governance and identity reinforces that boundary by stressing secure delegation, policy enforcement, and audit-ready accountability. The underlying principle is simple: machines may act quickly, but humans must remain accountable. That is the real meaning of trustworthy automation.
The challenge, of course, is that many organizations still have immature governance frameworks. The DesignRush article highlights this gap, and that is a legitimate concern. It is one thing to demo an agent creating work; it is another to prove that the same agent can be allowed into production without exposing the business to compliance and security failures.
Data quality still decides outcomes
There is also a less glamorous issue lurking underneath all the agent talk: data readiness. AI systems are only as useful as the systems they can reach, and fragmented or inconsistent data will produce brittle outcomes. That means many organizations will need to invest in cleanup, standardization, and integration before autonomous workflows can deliver real value.This is one of the most underappreciated consequences of the Copilot shift. Agentic AI does not eliminate enterprise debt; it exposes it faster. Companies with clean data, clear access rules, and well-defined workflows will see benefits sooner, while everyone else will discover that the bottleneck was always the stack beneath the model.
- Governance must define allowed actions.
- Compliance needs traceability and retention.
- Data quality determines whether agents help or harm.
- Human approval still matters for high-stakes actions.
What This Means for Software Development
The longer-term implication of Copilot’s evolution is that software development itself is being reshaped. If business users can ask a system to create artifacts, build workflows, or assemble applications from within the productivity suite, then the divide between user and builder gets thinner. That does not eliminate developers; it changes what they focus on.Developers shift upward in the stack
Routine work is increasingly the kind of thing agents can absorb. That pushes developers toward the higher-value parts of the system: architecture, security, integration, performance, and reliability. In other words, the coding job becomes less about typing every line and more about shaping the systems in which AI can operate safely.This is not a small change for the software industry. It means product teams will need to think about agent compatibility the way they already think about mobile compatibility or accessibility. If software is hard for an AI agent to understand or navigate, it may become less competitive inside AI-heavy enterprises.
That also affects low-code and no-code tooling. Microsoft Copilot Studio and Agent 365 are part of a broader effort to make agents easier to build, govern, and deploy. The likely outcome is that more business process automation will be assembled by nontraditional builders, while professional developers own the foundations that make those automations safe.
The new productivity stack
A useful way to think about the next phase is as a new productivity stack with three layers. First is the model layer, where OpenAI and Anthropic compete and coexist. Second is the orchestration and control layer, where Microsoft is building governance, identity, and observability. Third is the workflow layer, where employees and agents interact inside the apps where work already happens.That stack is powerful because it keeps users close to their work and keeps administrators close to the controls. It also helps Microsoft defend its ecosystem position, because the more AI work happens inside Microsoft 365, the harder it becomes for a rival platform to displace it. Platform gravity matters, and Microsoft is trying to increase it.
- Developers will focus more on architecture and reliability.
- Agent compatibility will become a design requirement.
- Low-code tools will accelerate business-led automation.
- Microsoft is building a three-layer AI productivity stack.
Strengths and Opportunities
Microsoft’s Copilot expansion has several strengths that make it strategically compelling. It combines model diversity, deep enterprise distribution, and a control story that speaks directly to IT and security teams. Just as importantly, it aligns with the direction the market is already heading, rather than trying to create a new category from scratch.- Enterprise trust remains central to the pitch.
- Multi-model flexibility reduces dependency on one vendor.
- Native workflow integration lowers friction for adoption.
- Governance and observability strengthen compliance readiness.
- Copilot Studio and Agent 365 expand the platform into agent management.
- Microsoft 365 distribution gives the company enormous reach.
- Human-in-the-loop design should make regulated adoption easier.
Risks and Concerns
The biggest risk is that autonomy arrives faster than governance maturity. Organizations may be tempted to deploy agents broadly because the demos are impressive, only to discover that permissions, audit trails, and data quality are not ready. That gap could create security incidents, compliance headaches, or simply disappointing results.- Shadow autonomy could emerge if teams bypass controls.
- Identity confusion may complicate audits and approvals.
- Data fragmentation could undermine task quality.
- Model inconsistency may create uneven outcomes across use cases.
- Governance complexity could slow deployments.
- Vendor dependency may deepen even in a multi-model world.
- User overtrust remains a real operational risk.
There is also the broader market risk that AI agent hype outruns practical business value. McKinsey’s data shows adoption is widespread, but scaling and profit impact remain uneven. That means many organizations may experiment with agents without seeing immediate returns, which could lead to disappointment if vendors overpromise and underdeliver.
Looking Ahead
The next phase will likely be defined by how quickly Microsoft can turn agentic features into reliable enterprise defaults. If Copilot Cowork proves valuable in real workflows and if governance tools like Agent 365 reduce friction instead of adding it, Microsoft could strengthen its dominance in workplace AI. If not, the market may treat these announcements as sophisticated previews rather than transformative shifts.What should enterprises watch most closely? Not the marketing language, but the operational details. The winners in this phase will be the vendors that make AI execution feel boringly safe—predictable, observable, reversible, and easy to govern. In enterprise software, boring is often what scales.
- Whether Copilot Cowork expands beyond preview-style access.
- How Microsoft balances OpenAI and Anthropic inside the same workflow.
- Whether Agent 365 becomes a real control plane for large organizations.
- How quickly enterprise customers standardize agent governance.
- Whether rivals respond with similarly integrated agent platforms.
Source: DesignRush Microsoft’s Copilot Expansion Accelerates the Rise of Autonomous AI Agents, Experts Say
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