Putting aside the buzz around “your next coworker is an AI assistant” for a moment, we can better understand where various forms of automation software should fit by examining what each intelligence service does. By identifying which “desk” (i.e., specific function or role in enterprise operations) an agent or RPA robot occupies, we can begin to design the so-called office of the future and integrate these services into real-world production environments.

What Is Controlled Agency?

Taking inspiration from employment law and management terminology, the concept of “controlled agency” helps clarify who (or what) performs which tasks in the workplace.

In traditional contexts, controlled agency often applies to healthcare, construction, and IT contractors, defining their legal, operational, and functional responsibilities. It outlines what they do, how and where they perform it, and crucially, what they are not allowed to do.

In the realm of AI, controlled agency is becoming a key approach to distributing work across different automation and intelligence systems. There are software robots that handle form-filling and administrative duties (clerical roles), agentic services that support business decisions (like analysts or consultants), and advanced AI tools pushing the boundaries of innovation (akin to researchers). Importantly, while all these intelligence services may coexist in the same system, each deserves its own defined space and access permissions.

Applying this principle across UiPath’s technology stack is Raghu Malpani, chief technology officer at the agentic automation firm. Given the company’s background in robotic process automation, it is well-positioned to explore the convergence of RPA and agentic AI.

With Great Power, Comes Great Auditability

“At UiPath, controlled agency represents our method for delivering AI agents that operate with clarity, context, and compliance. Powerful AI alone isn’t enough—it must also be reliable, auditable, and aligned with enterprise goals,” explained Malpani. “We implement controlled agency by embedding AI agents within structured workflows, where deterministic tasks are managed by [software]

robots and only non-deterministic ones are assigned to agents. This ensures that agents are used exclusively when adaptive decision-making is necessary. These agents are designed to be single-minded, focusing on narrow, clearly defined objectives. Complex processes are made up of multiple such agents combined with deterministic automation, maintaining clarity and modularity.” To mitigate risk, Malpani emphasizes that agents must function within strict constraints. This includes limiting inputs and outputs, enforcing policies, monitoring activities in real time, and having clear escalation paths to human oversight when exceptions arise or judgment calls are needed. This framework ensures that every agent operates securely within enterprise boundaries—whether retrieving data, updating systems, or interacting with users. UiPath has built-in tools for orchestration, governance, and evaluation to support this model.

From Prototype To Production

“Furthermore, through context grounding, each agent accesses the appropriate knowledge at the right moment. With dynamic evaluations, agents are tested in practical settings to ensure performance, accuracy, and trustworthiness before deployment. A scoring and governance framework allows developers and administrators to assess readiness and enforce standards at scale,” Malpani shared during a recent press briefing. “This strategy transforms generative AI from a prototype into a fully deployable solution—one that boosts productivity, reduces risks, and integrates seamlessly with existing teams and systems.”

Malpani’s insights into managing automation intelligence for mission-critical applications align with recent platform updates from UiPath. The company aims to tackle what it describes as major obstacles to widespread adoption, since conversational AI and agent-based assistants have so far shown "isolated value" when deployed enterprise-wide. Key issues include security and compliance concerns, inconsistent reliability, stalled pilot programs, and vendor lock-in fears.

The Second Act For AI

“This [year’s] launch marks our entry into the second phase,” said Daniel Dines, UiPath’s founder and CEO. “We’ve created a platform that brings together AI, RPA, and human decision-making, enabling companies to build smarter, more resilient workflows without unnecessary complexity. As models and hardware become commoditized, the true value of AI moves upward to orchestration and intelligent coordination.”

UiPath Maestro serves as the orchestration layer that automates, models, and optimizes complex business processes from start to finish, featuring built-in process analytics and KPI tracking for ongoing optimization. Maestro offers centralized control to scale AI-powered agents across teams and systems. Developers can create agents using UiPath Agent Builder within UiPath Studio, with flexibility for customization when needed. This empowers both technically skilled professionals and seasoned developers to build adaptable automations for complex business needs. Additionally, UiPath IXP (intelligent [e]xtraction and processing) now supports multi-modal, AI-driven classification and extraction of unstructured data. Designed for high-complexity scenarios like claims adjudication, loan approvals, and electronic batch records, IXP delivers enterprise-level scalability for document handling.

Agentic Orchestration, Competitive Analysis

Possibly the next frontier in AI (and naturally, a space tech companies are eager to dominate), the path to agentic orchestration is already well-trodden, perhaps even paved with gold. With its strong foundation in automation, UiPath is among the early movers in this domain, though not the only one.

Automation Anywhere also offers agentic process automation controls, complemented by robust workflow analytics via process discovery and governance features. Microsoft Copilot Studio focuses on building and analyzing workflows, Blue Prism remains a solid player in enterprise-grade RPA with good compliance and scalability, Moveworks competes in this market space, SnapLogic provides agentic capabilities through its AgentCreator tool—though its focus leans toward data orchestration—and IBM’s watsonx Orchestrate continues IBM’s tradition of offering a version of every major tech trend.

As automation intelligence finds its place in modern workflows, understanding where each service fits and crafting precise virtual job descriptions will help organizations integrate these digital colleagues more effectively. While AI agents and robots don’t need much room in the cafeteria, they still need to know exactly where to return after lunch.

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