Agentic AI

• Agentic AI is a new form of AI that enables agents to act autonomously to pursue goals, performing complex, decision-intensive workflows that until recently were considered too dynamic, contextual, and consequential to be automated.  

• Agentic AI reaches enterprise scale through BOAT (Business Orchestration and Automation Technologies) the category that describes the integrated set of capabilities, including AI agents, robots, orchestration, and people, that work together to automate complex, end-to-end business workflows.  

• Agentic AI is unlocking new value and competitive advantage for leading adopters across many different business processes, use cases, and industries. Analysts predict it will unlock trillions of dollars in global economic value as adoption spreads.

• It requires new technologies, approaches, and capabilities. Critical capabilities for execution and scalability include orchestration frameworks and tools, contextual memory, dynamic tool integration, and interfaces leveraging natural language processing (NLP). Ensuring governance, observability, and security requires safe reasoning mechanisms, control-as-code, human-in-the-loop, control planes, and more.

Agentic AI gives autonomous agents the ability to plan, make decisions, and adapt as they pursue goals. But agentic potential only becomes reliable business execution when agents operate inside an orchestration layer that coordinates them with robots, APIs, documents, data, and people — elevating the work from task automation to process automation.

Agentic AI systems can:

• Understand goals and make decisions based on context and available data

• Break down complex tasks into manageable step-by-step plans

• Use tools, applications, and external systems to complete work

• Maintain memory of relevant information and ongoing situations

• Collaborate with people or other agents when needed

• Learn from results to improve performance over time

Together, these capabilities allow agents to operate independently across complex workflows—handling variability, adapting to new information, and coordinating work across systems to keep long-running processes moving toward the right outcome without ongoing human intervention.

An agentic AI system is a continuous loop of perception, planning, action, and learning.

Perception

An AI-powered agent collects information from documents, applications, data sources, APIs, sensors, or other systems. It interprets this information to understand the current context.

Reasoning and planning

Based on what it perceives, the agent evaluates options and determines the steps required to achieve a goal. These initiatives may involve selecting the best sequence of actions or identifying the right tools or systems to use.

Taking action

The agent performs tasks through applications, APIs, robots, or other agents. These actions can include updating records, generating content, retrieving information, initiating workflow steps, or coordinating activity across multiple systems.

Reflecting and learning

After each action, the agent evaluates the outcome. It uses feedback and short- or long-term memory to refine its approach, improve future decisions, and maintain continuity across longer workflows.

This loop runs inside an orchestration layer that manages long-running process and case state, keeps actions auditable, and maintains human-in-the-loop control where judgment is required so agents operate reliably, safely, durably, and at scale.

Agentic AI gives agents the ability to reason, plan, and act. But for those capabilities to operate reliably at enterprise scale, agents need to be coordinated with robots, APIs, documents, and people, with long-running process and case state managed and humans kept in charge where judgment matters. This is the work of BOAT (Business Orchestration and Automation Technologies) the category that describes the integrated set of technologies needed to scale agentic AI across the enterprise. Within this category, the UiPath Platform and Maestro specifically serves as the control plane that turns agentic potential into reliable business execution.

BOAT is the broader category that encompasses how AI agents, robots, integrations, and people come together to automate multistep tasks across systems. Within this category, the UiPath Platform serves as the execution layer primarily through Maestro turning an agent's decisions into real work. Agents interpret context and determine what needs to happen next. Robots perform consistent, high-fidelity actions across screens, APIs, and applications. People provide direction, approvals, and specialized judgment where oversight is required through human-in-the-loop controls. A defining characteristic of BOAT is its ability to operate across diverse enterprise systems legacy and modern, structured and unstructured.

Orchestration

Orchestration is the control plane that governs how agents, robots, APIs, data, documents, and people work together across long-running processes and cases. UiPath delivers this through the UiPath Platform and Maestro, with three lead capabilities — process orchestration, agentic case management, and multi-agent orchestration — that turn agentic potential into durable, governed business execution. It defines roles, permissions, sequencing, and handoff rules across a workflow. It ensures actions are observable, decisions are auditable, and behavior aligns with enterprise policies and governance requirements.

Through orchestration, organizations can manage multi-agent collaboration, long-running processes, cross-system interactions, and the points where human oversight is required ensuring that autonomous activity is safe, predictable, and aligned with business goals.

Orchestration is the coordination capability at the heart of BOAT, and the two work together to enable agentic AI to operate effectively across the enterprise.

• BOAT’s execution layer provides the ability to carry out work across systems through robots, integrations, and AI-driven actions.

• Orchestration coordinates those actions, ensuring they occur in the right sequence, with the right permissions, and with the visibility and guardrails needed for safe and compliant operation.

Together, they provide the structure and control that allow AI agents to plan, act, and deliver complex outcomes at scale.

Agentic AI operates within a broader landscape of AI models and techniques, automation tools, and coordination and control capabilities. Understanding this ecosystem clarifies how agentic AI works, what it depends on, and how it interacts with other enterprise technologies.

AI capabilities: the cognitive building blocks

Generative AI (GenAI) GenAI produces text, code, summaries, or other content. Agentic AI may use generative outputs as part of a larger plan but extends beyond generation to also include decision making and taking action.

Traditional AI, machine learning, and deep learning: Traditional AI, machine learning, and deep learning models support tasks such as classification, prediction, pattern recognition, and optimization. Deep learning models, in particular, excel at interpreting unstructured data like text, images, and audio. Agentic AI incorporates these capabilities within broader workflows that require context-sensitive planning, decision making, and action across multiple systems.

Reasoning models: Reasoning models support structured problem-solving and multistep thinking. Agentic AI uses these capabilities to evaluate options and determine appropriate next steps.

Natural language interfaces: Natural language interfaces make AI accessible by allowing people to express goals in everyday language. Agentic AI can interpret these inputs, although decision making is driven by its planning and control logic.

Operational entities and roles: the actors performing the work

AI agents: AI agents apply agentic AI and embedded control logic to perceive context, make decisions, and take action. Control logic may include explicit rules, constraints, and policies that shape behavior and provide predictable, auditable decision paths. Agents may work individually or in collaboration with other agents, robots, and people.

Specialized agents: Specialized agents are designed for focused roles or domain-specific tasks. They apply targeted logic or expertise to handle distinct steps in a workflow—such as extracting data, validating inputs, assessing risk, or coordinating handoffs.

Deep agents: Deep agents are a new class of agentic systems—multistep and goal driven—capable of planning, retaining context, recovering from errors, and coordinating subagents. Unlike shallow task-level agents, deep agents handle long-horizon tasks and adapt to real-world complexity, operating more like autonomous digital coworkers.

Multi-agent systems (MAS): Multi-agent systems involve multiple agents that share context and coordinate tasks within a workflow. MAS architectures support specialization, distributed problem solving, and resilience, allowing agents to hand off work or operate in parallel. Agentic AI supports these patterns through interoperable planning and communication capabilities.

Execution and automation technologies: tools and resources for taking action

Robotic process automation (RPA): RPA enables robots to perform structured, repeatable actions at speed and scale. Agents call on robots to execute reliable UI or system interactions and deterministic and structured processes, while agents take on steps that require reasoning or adaptation.

System integrations and API tools Agents use integration technologies such as APIs, connectors, and system actions to perform work inside enterprise applications. These capabilities allow agents to retrieve data, update records, and complete tasks across platforms.

Prebuilt agentic AI solutions: Prebuilt agentic AI solutions provide ready-made, domain-specific capabilities that teams can deploy quickly to accelerate adoption. These packaged agents and workflows offer tested logic, integrations, and guardrails, giving organizations a fast way to introduce complete agentic workflows without creating every component from the ground up.

Coordination and governance: enterprise controls that enable scale

Orchestration: Agentic orchestration governs how agents, robots, and people work together across workflows. It defines collaboration patterns, permissions, sequencing, and oversight to ensure safe and coordinated execution. It also supports multi-agent systems by managing shared context and aligning decisions with enterprise policies.

Guardrails and policy controls: Guardrails and policy controls provide the enterprise rules that guide agent behavior. They enforce access permissions, safety checks, decision boundaries, and escalation conditions—ensuring that agents operate within approved policies with minimal human intervention and adapting those rules as processes evolve.

Monitoring and oversight: Monitoring and oversight offer real-time visibility into agent operations through analytics dashboards, audit trails, and performance checks. These capabilities help teams validate system behavior, detect drift or unexpected outcomes, and maintain reliable, compliant operation across workflows.

By powering next-generation AI agents to perform a wider array of complex tasks than ever before possible, agentic AI vastly expands what can be automated. For enterprises, this delivers a number of important benefits:

While both agentic AI and generative AI (GenAI) are pivotal technologies, their focuses differ. Each has its unique strengths and applications.

GenAI is built to create copy, images, code, and ideas. Its ability to support natural language processing makes it a powerful tool for content generation.

Agentic AI, by contrast, is built to act. It plans, decides, and executes to reach outcomes. Where GenAI stops at creation, agentic AI continues implementing actions, triggering workflows, and adapting to new circumstances.

Together, these technologies are complementary. For example, GenAI might draft marketing content, while agentic AI launches and iterates the campaign automatically based on real-time performance data.

In 2017, Google researchers introduced the Transformer architecture a step-change innovation in how machines process language. Unlike earlier models, Transformers used attention mechanisms to understand context more efficiently and at scale. This set the stage for modern AI.

Over the next few years, researchers built on this foundation by training Transformers on massive text datasets. The result: large language models (LLMs) that could generate text, answer questions, and even reason—just from natural language prompts.

By 2022, conversational AI reached a tipping point. Fine-tuning techniques made LLMs more aligned with human intent, safer to interact with, and easier to use. AI went from being a tool people operated to a partner they could talk with.

Now, we’re seeing the next leap: agentic AI. By adding planning, memory, and tool use to LLMs, these systems go beyond giving answers and actually plan and take action to meet specific goals. AI agents can follow multi-step instructions, call APIs, and complete goals autonomously. This marks a turning point in automation.

Agentic AI draws on multiple branches of artificial intelligence to enable thinking, acting, and adapting:

Agentic AI’s flexibility and autonomy introduce new considerations for governance, security, and reliability. Organizations must ensure that these systems behave safely, transparently, and within defined boundaries.

Autonomy and oversight Balancing autonomy with human oversight is essential. Clear rules, policies, and escalation paths help ensure that agents act within defined limits.

Transparency and reliability Agents may generate results that require validation or explanation. Audit trails, evaluation frameworks, and continuous monitoring improve traceability and reduce uncertainty.

Security and privacy Agents often access sensitive data. Strong identity controls, access policies, and monitoring are essential to prevent unauthorized use and maintain data integrity.

Coordination and alignment As multi-agent systems become more common, maintaining shared context and preventing divergent behavior is critical. Orchestration frameworks help maintain alignment.

Model and tool integrity Agents rely on many components, including models, APIs, and third-party tools. Regular testing, validation, and provenance tracking reduce the risk of incorrect or unsafe behavior.

Deploying agentic AI requires aligning design, orchestration, integration, and oversight so agents can operate consistently and safely across the enterprise.

Deploying agentic AI is not about replacing workflows but rather redesigning them to take advantage of autonomous intelligence. This checklist outlines a practical approach.

The massive leaps forward in AI have created a seismic technology shift. Today, agentic AI enables AI agents that can learn, predict, and take action. Agents can take on undefined tasks, manage complex processes, and make nuanced decisions that until recently could only be completed by people.

Agentic AI, in short, is enabling us to explore entirely new possibilities in designing work processes, expanding the role of intelligent orchestration and automation as we redefine the roles of people, robots, and machines in a myriad of processes across the organization.

While advances in agentic AI have already changed some of the ways that people and machines interact and collaborate, we’re just at the beginning of the revolution. There’s a massive and inevitable wave of work transformation on the horizon that has only just started to coalesce and gather momentum.

The way that the most modern and successful companies will operate in five years what their people do, what machines do, and the ways people, robots, and agents work together will bear little resemblance to how they operate today.

As agentic AI achieves scalability and AI use cases expand throughout every business process, enterprises everywhere will have the means to operate better, faster, and more efficiently. They’ll be differentiated by their outstanding customer engagement, nimbleness in responding to current and future changes, and their ability to attain new levels of employee productivity and engagement.

The possibilities are virtually endless, and the future of agentic AI is filled with promise. As this technology evolves, it is reshaping the world of work and the roles of humans and machines in the world.