By combining LLM-powered reasoning, robust context grounding, and seamless integration with RPA workflows, UiPath positions AI agents as the next evolution of intelligent automation.
Time to meet your new team member.
Agentic AI represents a significant leap in automation, transforming how organisations tackle complex tasks by enabling AI agents to act as intelligent digital workers. Unlike general-purpose chatbots or RPA workflows that execute predefined processes, Agentic AI combines the reasoning power of large language models (LLMs) with a structured, task-specific approach. Here, we’ll explore what Agentic AI looks like in practice, focusing on UiPath’s upcoming capabilities.
Agentic AI is designed to tackle specific tasks with intelligence. An Agent is essentially an AI Language Model (such as OpenAI’s GPT, Anthropic Claude, Meta’s Llama or Google Gemini) that is given a task to perform. Language Models have an encyclopaedic knowledge of different approaches to almost every possible situation; thus, it is essential to give your agents clear job descriptions, guidelines, and context to solve the problems that you ask of it. Think of it as a digital team member:
- It receives instructions written in plain English.
- It draws on a wealth of business knowledge to inform its decisions.
- It uses pre-built tools (like RPA workflows) to execute tasks.
Most importantly, it’s designed to adapt. If the situation changes, the agent adjusts its approach, staying focused on the end goal.
Adaptive Workflow Management
A hallmark of Agentic AI is adaptive workflow management, enabling agents to adjust processes in real time based on changing conditions. For instance:
- Imagine an ERP system outage during reconciliation. Instead of grinding to a halt, the AI agent adjusts. It might switch to a backup workflow, use local data sources, or notify key stakeholders about the delay.
- If a supplier disputes a charge, the agent pauses the reconciliation process and escalates the issue with all relevant documentation.
This adaptability ensures resilience and efficiency, even in unpredictable scenarios.
Guardrails for Integrity
While powerful, AI agents remain bounded by the tools and context provided to them. They cannot exceed their defined capabilities, ensuring the integrity of systems and processes. This controlled environment is achieved by:
- Tool Descriptions: Each tool is explicitly defined for the agent. For example, “You can use the RPA workflow ‘FetchTransactions’ to retrieve transaction records from the ERP system.”
- Role-Specific Knowledge: Context grounding ensures that the agent’s decision-making aligns with business rules and objectives.
This careful balance between autonomy and control makes Agentic AI both versatile and reliable.
A Real-World Problem: Supplier Statement Reconciliation
Let’s put this into a real-world context. One of the biggest headaches for finance teams is reconciling supplier statements. It’s repetitive, time-consuming, and prone to delays—especially when discrepancies arise. Mistakes are inevitable, and delays cost both time and goodwill.
Now imagine handing this problem over to an AI agent.
With UiPath’s Agentic AI, your digital worker starts with a clear directive:
“Your job is to reconcile supplier statements by cross-checking ERP data and resolving any discrepancies with minimal human intervention.”
Step 1: Giving the Agent Its “Job Description”
Every agent needs instructions—plain-English prompts that spell out its goals. This is where you define the scope of the task, leaving no room for ambiguity. In this case:
- Task: Reconcile supplier statements.
- Boundaries: Resolve issues within given workflows; escalate complex cases to a human.
No assumptions, no guesswork.
Step 2: Equipping the Agent with Knowledge
Instructions are only half the story. For the agent to succeed, it needs the right context—things like reconciliation policies, FAQs, and historical data. Think of it as onboarding a new team member, but much faster.
This is where context grounding comes in. Using UiPath’s advanced “Context Grounding” and “AI Trust Layer” capabilities, the agent safely draws on live data from your ERP, supplier guidelines, and even specific exceptions recorded in the past.
Now it doesn’t just know what to do—it knows how to do it.
Step 3: Tools in Action
An agent isn’t all talk; it needs “hands” to get the job done. In this case, its tools are RPA workflows:
- One workflow fetches supplier data from the ERP.
- Another generates discrepancy reports.
- A third sends follow-up emails using pre-approved templates.
These tools are like apps on a phone—ready to launch when the agent calls on them.
Step 4: Adaptive Workflow Management
Here’s where things get interesting. Let’s say the ERP system suddenly goes down mid-process. Traditional automation would crash, but an AI agent is smarter. It detects the issue and switches to a backup process, such as pulling data from a local file.
Or maybe a supplier disputes a charge. The agent pauses the workflow, drafts a detailed email to the supplier, and logs the case for escalation. It’s not just following instructions—it’s thinking on its feet.
The Results
By the end of the month, the agent has reconciled hundreds of statements with minimal human involvement. Your finance team has more time for strategic work, errors are down, and suppliers are happier.
Why Business Leaders Should Care
At the heart of Agentic AI is a simple idea: give AI the right tools, instructions, and context, and it will deliver. It won’t stray from its task or break your systems—it’s built to work within the boundaries you set.
Agentic AI isn’t just about doing tasks faster—it’s about doing them smarter. By combining reasoning, context, and tools, these digital workers go beyond the limits of traditional RPA. They adapt, learn, and collaborate, opening the door to entirely new possibilities in automation.
Over the coming months, AI agents will increasingly be handling customer inquiries, managing inventory or compliance workflows, and even coordinating project schedules. The potential applications are as diverse as your business needs. You’re not replacing people or risking chaos; you’re empowering your teams with a digital worker that’s focused, efficient, and always learning.
Let’s talk about where Agentic AI fits into your business. The possibilities might just surprise you. Reach out below.
Frequently asked questions
What is Agentic Automation, and how does it differ from traditional RPA?
Agentic automation uses AI-driven agents to perform tasks, plan workflows, and adapt to changes in real time. Unlike traditional RPA, which follows rigid, predefined scripts, agents leverage reasoning capabilities (powered by AI) to make decisions, coordinate tools, and handle exceptions autonomously. In fact, AI agents leverage RPA to perform specific tasks.
What makes AI agents ‘agentic’?
AI agents are described as “agentic” because they possess three key elements:
- Instructions: Clear job descriptions, written in plain English, define their goals and limits.
- Context: Knowledge such as operating procedures, FAQs, or live data helps them make informed decisions.
- Tools: RPA workflows, APIs, or human escalation mechanisms give agents the ability to act.
This enables agents to reason, plan, and execute tasks autonomously while staying within their defined scope.
What types of tasks are ideal for Agentic Automation?
Agentic automation is particularly well-suited for tasks that require:
- Decision-making under varying conditions (e.g., resolving discrepancies).
- Coordination across systems, workflows, or teams (e.g., reconciling supplier statements or handling exceptions).
- Managing dynamic workflows where real-time adjustments are needed (e.g., supply chain disruptions, IT incident management).
Tasks that blend structured processes with some variability benefit the most.
Can Agentic Automation replace human workers?
Not really. Agentic automation isn’t designed to replace people but to augment teams by handling repetitive or complex processes that would otherwise consume significant time. Human oversight remains crucial for high-stakes decisions, training agents, and addressing escalations when tasks exceed the agent’s scope.
For instance: An AI agent might reconcile 90% of supplier statements but pass tricky disputes to a human expert.
How secure and reliable are AI agents in a business environment?
Agentic automation is highly controlled and secure because:
- Agents are explicitly constrained to the tools and instructions they are given.
- Workflows, systems, and actions must be described in detail, preventing unintended behaviour.
- Agents can only operate within defined limits, ensuring they respect system integrity and compliance.
Additionally, any unexpected conditions or failures are logged, escalated, or handled dynamically based on business rules.
How does Agentic Automation handle unexpected changes or failures in workflows?
Agentic automation is designed to adapt when workflows encounter unexpected changes or failures. AI agents can:
- Identify issues in real time, such as a system outage or missing data.
- Adjust workflows dynamically by switching to alternative processes, using backup tools, or notifying relevant stakeholders.
- Escalate complex problems to human teams when they fall outside the agent’s defined scope.
For example: If an ERP system fails during reconciliation, the agent might access a backup file, pause the task, or notify IT while logging the failure for review. This flexibility ensures processes continue smoothly without grinding to a halt.
Agentic AI in the Real World
