Agentic AI

 Introduction to Agentic AI

What is agentic automation?

Agentic automation emerged as a response to the growing need for more flexible, self-directed, and intelligent automation solutions—especially as organizations handle increasingly complex workflows.

Traditional, rule-based automation often struggles when scenarios change, exceptions appear, or new integrations are introduced.

Agentic automation addresses these challenges by equipping agents with the autonomy to learn, adapt, and make decisions on the fly. Here are some of the key challenges it set out to solve:

1. Limited adaptability of traditional RPA: Early robotic process automation (RPA) solutions typically followed strict, predefined rules. Any process change—like a new user interface layout, an extra approval step, or a change in business logic—would break the existing automation. Agentic automation aims to handle these dynamic elements more gracefully by making robots capable of decision-making and real-time adaptation.

2. High maintenance costs and complexity: Frequent updates and constant oversight often inflate the cost of legacy automation solutions. By combining agents with robots to holistically understand processes and dynamically adapt their actions, agentic automation reduces the maintenance burden.

3. Need for end-to-end automation: Organizations often have multiple disconnected processes that require human intervention to link them together. Agentic automation moves closer to seamless end-to-end workflows, with automated agents able to collaborate, hand off tasks, and handle variations. This reduces bottlenecks and manual handoffs.

4. Rapid scaling and faster time-to-value: As organizations grow and processes evolve, scaling traditional automation can become complicated. Agentic automation through agentic orchestration allows for more plug-and-play capability. Companies can expand or modify automated operations without needing to rebuild entire workflows from scratch.

By addressing these pain points, agentic automation helps organizations avoid the pitfalls of rigid automation setups and empowers them to manage more complex, changing, and intelligent workflows. We’ll discuss in the following lessons examples of use cases that agentic automation can solve.   

How agents differ from Robots?

           

So, what is an agent?

Now, let’s talk about agents, which come into play because our world is full of uncertainty and constant change. Applications, processes, technology, information, people, and business objectives can shift at any moment. Trying to create or update an entire automation each time something changes just isn’t scalable.

That’s why we leverage AI. Yes—an agent is still a robot in the UiPath ecosystem, just an advanced one. It’s a piece of software capable of more than following a static script—it can make decisions, plan, and learn as conditions change. However, humans remain an integral part of the process.

They provide validation, define objectives, and handle exceptions or complex decisions that require human judgment.

Here are the four defining traits of an agent:

• Context grounding (Knowledge Base) - Just like a new employee needs access to policies, reports, and past decisions, an automation agent needs to "learn" from available data before making decisions.

• Prompting (Decision making) - Similar to how employees rely on guidelines and past experience to make the right decisions, an agent follows instruction guidelines to ensure accuracy.

• Tools (Task execution) - Just as employees use software and automation tools to complete tasks, an agent executes its actions seamlessly with use of robots.

• Escalations (Human validation) - When agent is unsure of the next actions, it will ask for help or specific instructions from a human for deciding on next actions.

Agentic AI is the autonomous artificial intelligence systems that can perceive, reason, and act independently to achieve specific goals. These AI-driven agents are self-sufficient decision-makers capable of adapting to real-time changes, learning from interactions, and making informed choices without predefined rules.

Agentic Automation is the application of Agentic AI within automation workflows to enhance operational efficiency.

Let’s examine a process from two perspectives: how it functions automated with RPA and specialized AI, and how it evolves with an agentic approach.

Processing vendor invoices (With RPA and Specialized AI)

Processing vendor invoices (With agentic automation)

What is agentic orchestration?

As seen in the example above, agents were integrated into the existing workflow rather than replacing the entire process.

This is achieved through Agentic Orchestration, which, enables rapid scaling. Instead of rebuilding entire processes from scratch, this approach focuses on identifying key steps that can benefit from agentic automation, making the transition more efficient and less time-consuming.

Let's look at two more examples:

Purchase-to-pay process (With agentic automation)

• The process starts with a purchase requisition, where a robot pulls data and a human reviews it. Once approved, the robot converts it into a purchase order and sends it to the supplier after a final check.

• For receiving goods, an agent tracks deliveries and resolves standard discrepancies, escalating only complex cases to a human.

• During invoice processing, the agent performs a three-way match, flags discrepancies for review, and schedules payments. A robot then executes payments, escalating unresolved issues as needed.

Purchase-to-pay process (With RPA and Specialized AI)

• In this scenario, anything that requires judgment, context, or flexible decision-making would usually fall to a person. Specifically, in the purchase-to-pay flow shown: 

  • Monitoring deliveries – Robots can’t track delays or handle supplier emails, so humans follow up on missing items.

  • Resolving discrepancies – Three-way matching is automated, but humans step in for mismatches like partial shipments or incorrect pricing.

  • Scheduling payments – Robots handle fixed terms, but humans decide on discounts or adjust for cash flow.

  • Handling exceptions – Unclear policies or unconventional supplier requests require human intervention.

Order-to-cash process (With agentic automation)

• The order-to-cash process starts with customer onboarding, where a robot creates records, and an agent runs credit checks, escalating issues to a human when needed.

• In order management, a robot verifies orders, while an agent checks credit terms, involving a human only for special cases.

• During fulfillment, humans handle logistics, a robot updates inventory, and an agent manages exceptions like backorders or customer inquiries.

• For invoicing, a robot generates invoices, and an agent validates them, flagging discrepancies for finance review.

• In payment collection, a robot tracks payments, and an agent follows up on delays, escalating complex disputes to a human.

• Finally, in accounts receivable, a robot compiles reports, and an agent analyzes trends, notifying finance managers of recurring issues.

Order-to-cash process (With RPA and Specialized AI)

• Without agents, robots handle rules-based tasks, but humans must manage anything requiring judgment, policy interpretation, or flexible decisions:

• Without agentic automation, human intervention remains high for nuanced tasks.

  • Credit checks & approvals – Robots fetch credit scores, but humans assess borderline cases.

  • Complex orders – Robots verify orders, but humans approve exceptions or special pricing.

  • Fulfillment exceptions – Robots update inventory, but humans handle backorders or special shipping requests.

  • Invoice discrepancies – Robots generate invoices, but humans correct pricing or stock mismatches.

  • Payment issues – Robots process payments, but humans handle disputes and negotiations.

  • Accounts receivable – Robots generate reports, but humans analyze trends and adjust collection strategies.

Without agentic automation, human intervention remains high for nuanced tasks.

Although agentic automation is still an emerging technology, it has broad application across industries, departments, and processes across all sectors. The following use cases illustrate the successful implementation of agentic automation. However, more applications are expected to emerge rapidly in the future.

1. Talent acquisition & recruitment

Challenges: Recruiters spend a significant amount of time manually reviewing resumes, conducting initial screenings, and ensuring consistency in candidate evaluations. This process is time-consuming, prone to bias, and inefficient, often leading to hiring delays and mismatches.

How agents can help: Agents review job applications and resumes, screen candidates based on key criteria such as credentials, job changes, location, and industry experience, and generate tailored interview questions based on application details and industry requirements. They support the interview process by recording and transcribing conversations, evaluate candidates using predefined criteria and transcripts, and generate detailed evaluation reports with recommendations for recruiters.

Business impact: Agents significantly enhance the efficiency and accuracy of candidate screening. They reduce the time and effort required for initial evaluations, ensure consistent assessment criteria, and help recruiters to quickly identify the most suitable candidates

2. IT operations

Challenges: IT teams spend a lot of time manually monitoring logs, detecting issues, and searching for fixes. Delayed responses can lead to system downtime and business disruptions. Creating detailed tickets is also time-consuming, making it harder to resolve problems quickly.

How agents can help:  The agent continuously monitors and downloads log files from application servers, uses an LLM (Large Language Model) to analyze log files for critical issues, searches resources (web/documentation) for possible fixes, and automatically creates tickets with error details and potential solutions. It also notifies IT staff of issues before they escalate.

Business impact: The agent prevents downtime and reduces IT support costs, allowing IT teams to focus on strategic tasks rather than reactive problem-solving. This leads to improved system reliability, reduced business disruptions, and enhanced overall IT performance. 

3. Procurement

Challenges: Procurement teams often struggle with manually reviewing and comparing quotations from different vendors. This process is time-consuming, prone to human errors, and lacks real-time price validation. Keeping up with internal rules and market standards makes things even more difficult. This can cause problems and cost overruns.

How agents can help:  Agents automate the entire price validation process by extracting item details from quotations, researching market prices through APIs, and checking compliance with internal policies. They ensure accuracy by cross-verifying prices against external sources and internal benchmarks. The results that are confirmed are then given to the procurement officer. This lets them make faster and better decisions while reducing the work they have to do manually.

Business impact: The agent speeds up quotation reviews, ensuring accurate price validation and policy compliance. It reduces processing time, minimizes errors, and helps secure cost-effective purchases, leading to optimized procurement and significant savings.

4. Handling public complaints

Challenges: Handling public complaints efficiently is a critical yet time-consuming task for the public sector organizations. Complaints come from multiple channels, requiring manual triaging, investigation, and resolution. Ensuring policy compliance, tracking case history, and maintaining transparency add further complexity. Delays in responding and fixing problems can make people unhappy and lose trust.

How agents can help:  The robot extracts complaint details from multiple channels, categorizes them, and sends acknowledgments. The agent investigates, gathers evidence, and refers the policies. Agent summarizes case history for reopened complaints. It then presents findings to the human for validation. The robot notifies the customer of the decision and updates records.

Business impact: The agent enhances efficiency by reducing manual effort, speeding up response times, and ensuring regulatory compliance. It minimizes rework, helps meet strict SLAs, and improves overall accountability. This leads to faster resolutions, increased customer satisfaction, and a more effective complaint management process.

5. Financial fraud detection

Challenges: Detecting financial fraud manually is time-consuming and prone to human error. Large volumes of transactions must be analyzed to identify suspicious patterns, making it difficult for financial institutions to keep up with evolving fraud tactics. Delayed detection can lead to financial losses, compliance violations, and reputational risks.

How agents can help:  The agent automates fraud detection by analyzing transactions against pre-defined fraud schemas. It flags potential fraudulent activity for human review, ensuring faster and more accurate identification of suspicious transactions. The agent gathers relevant information for reporting to authorities and monitors flagged accounts for further investigation. This reduces manual workload, enhances compliance, and improves fraud prevention efficiency.

Business impact: Agents enhance compliance, reduce financial losses, and build trust in the financial system. Their adaptive approach improves fraud detection, strengthening prevention while lowering operational costs. 

6. Processing insurance claims

Challenges: Processing insurance claims manually is time-consuming and prone to errors. Validating documents, checking reimbursement history, and ensuring compliance with policy rules require significant human effort. This can lead to delays, inconsistencies, and increased workload for employees, impacting overall efficiency and customer satisfaction.

How agents can help:  The agent automates claim processing by classifying documents, extracting key information, and validating requests against policy rules. It leverages the knowledge base to check reimbursement history and match requirements. The system updates records and notifies customers with approval or rejection, allowing human agents to focus only on complex cases, improving speed, accuracy, and efficiency.

Business impact: The agent reduces operational costs and decreases claim handling time significantly. It achieves high data extraction accuracy, improving customer satisfaction and employee retention.

The common concerns around agentic automation

Who's responsible if an agent makes a bad call?

The simple answer is that developers program the rules for when and how agents should involve human supervision. Agents have taken over parts of the process that used to need human input, but critical decisions still land with people. That safeguard remains in place to ensure accountability.

How safe is our data once we hand over control to a machine?

With UiPath, your data is just as secure as it’s always been. Agents handle information with the same protections and standards that govern traditional RPA.

What if those decisions are biased, or downright wrong?

Enter context grounding, a key concept for agentic automation. Context grounding means giving AI the right frame of reference—think policy documents, standard operating procedures, and other internal guidelines—so it can make decisions that align with your business’s specific needs and values.

Business leaders and process owners make sure these documents are available to “train” the AI, reducing the risk of biased or incorrect decisions.