I like how UiPath Agentic Automation combines AI, automation, and orchestration into a single platform that handles both simple and complex business processes effectively. It strikes a good balance between traditional RPA and intelligent decision-making, making the automation more adaptive and scalable. The platform is valuable because it allows us to automate processes that aren't fully predictable or purely rule-based. The initial setup was relatively smooth, especially for teams already experienced with the UiPath ecosystem and enterprise automation tools. Review collected by and hosted on G2.com.

In some complex enterprise environments, debugging and troubleshooting AI-assisted workflows can also take additional effort because decision-making paths may vary depending on context and input data. Better transparency and simplified diagnostics for AI-driven decisions would further improve maintainability & supportability. Review collected by and hosted on G2.com.

I like the agentic workflow of UiPath Agentic Automation, which allows the AI agent to think, decide, and act autonomously to complete tasks, rather than just following fixed steps. I appreciate how it integrates AI to analyze and decide dynamically, particularly for handling complex decisions where traditional bots might fail with changing inputs or exceptions. The RPA workflow automation with UiPath and business process capabilities are appealing. Also, the initial setup of UiPath Agentic Automation was really amazing. Review collected by and hosted on G2.com.

Agentic Automation is powerful, but it’s not universally reliable yet. There are clear categories of business processes where it struggles or needs improvement: Deterministic, rule-critical processes like payroll processing, tax calculations, and financial postings require 100% accuracy and consistency every time, which agentic AI may struggle with due to its probabilistic nature. AI may produce slightly different outputs for the same input, presenting a business risk. Also, compliance-heavy or audit-driven processes such as KYC verification, audit trails, and regulatory reporting reveal similar challenges. Review collected by and hosted on G2.com.

I like the technology provided by UiPath Agentic Automation, especially the context grounding feature, which allows me to compare data effectively. I appreciate having the option to add tools to run predefined steps and the flexibility to incorporate human verification when needed. It's also helpful to keep standard templates in UiPath storage and create indexes at UiPath AI Center. These features help significantly in identifying template scenarios that may fail in the system. The platform has saved a lot of time for users who would otherwise be doing tedious manual work. The initial setup was okay, and the UiPath Academy was helpful in getting started. I highly recommend it, giving it a 10 out of 10. Review collected by and hosted on G2.com.

Sometimes it gives different outputs for the same inputs, so we should have proper prompts. I had to keep coming up with better system prompts. It occurred a few times. Review collected by and hosted on G2.com.

What stands out most to me about UiPath Agentic Automation is the way it combines traditional RPA with AI agents instead of trying to replace existing automation completely.

A few things are especially impressive:

Autonomous decision-making

Unlike classic bots that only follow fixed rules, agentic automation can analyze context, reason through tasks, and decide next actions dynamically. That makes it useful for messy real-world workflows where every case is slightly different.

Human + AI collaboration

UiPath’s approach doesn’t assume AI should operate alone. It supports “human-in-the-loop” workflows where agents escalate uncertain situations to people. That balance is practical for enterprise use.

End-to-end orchestration

The integration of AI agents, RPA bots, APIs, and workflow orchestration in one platform is powerful. Traditional RPA handled repetitive steps well, but agentic automation can coordinate across unstructured tasks, documents, emails, decisions, and enterprise systems.

Context awareness

UiPath emphasizes “context grounding,” where agents use company policies, historical data, and business rules before acting. That’s a major step beyond simple chatbot-style AI.

Enterprise governance and security

One of the biggest challenges with AI agents is control and auditability. UiPath is focusing heavily on governance, compliance, and secure deployment — especially for regulated industries and public sector environments.

Hybrid automation model

The most realistic aspect is that UiPath still values deterministic automation. Stable processes continue using standard RPA, while AI agents handle exceptions and ambiguity. That hybrid model is more reliable than forcing everything into AI-driven agents.

A simple way to think about it:

RPA bots = follow instructions exactly

AI agents = understand goals and adapt

Agentic Automation = combines both under one orchestrated workflow

That combination is why many companies see it as the next evolution of enterprise automation rather than just another AI trend. Review collected by and hosted on G2.com.

A few things about UiPath Agentic Automation look genuinely promising, but there are also some important limitations and risks that stand out.

Here’s what I’d be cautious about:

High licensing and implementation cost

One of the most common complaints from users is pricing. For smaller teams or mid-sized businesses, the platform can feel expensive compared to alternatives like Microsoft Power Automate or newer AI-native tools.

Complexity is increasing

Traditional RPA was already complex in enterprise environments. Adding AI agents, orchestration, governance layers, memory systems, and human-in-the-loop reviews can make architectures much harder to debug and maintain.

“Agentic” hype can exceed practical ROI

Some automation professionals are concerned companies are adopting agents because the term is trendy rather than because the workflow truly needs it. In some cases, businesses now accept lower automation coverage and weaker ROI simply to say they are “using AI agents.”

Reliability still isn’t enterprise-perfect

AI agents are adaptive, but they’re also less deterministic. Research shows current agentic systems can struggle with consistency, especially in long, multi-step workflows. Performance often drops sharply in complex enterprise tasks.

Governance and security challenges

Agentic systems introduce a much larger attack surface than classic RPA because agents can reason, access tools, and take actions autonomously. Enterprises need strict controls, permissions, monitoring, and auditability — and many organizations are still immature in those areas.

Steep learning curve

Many users mention that advanced AI-agent features require deeper technical knowledge than expected. Teams often need expertise in APIs, orchestration, AI prompting, governance, and exception handling — not just drag-and-drop automation.

Potential “AI-washing” concern

Some analysts argue parts of the industry are rebranding existing automation as “agentic AI” without fundamentally changing the architecture. UiPath sometimes faces criticism that it is layering AI onto legacy RPA foundations rather than rebuilding around AI-native workflows.

Monitoring and debugging can become difficult

With deterministic bots, failures are usually traceable. With AI agents, behavior can vary from run to run. That makes troubleshooting, compliance validation, and root-cause analysis more difficult.

The biggest strategic question for UiPath is probably this:

> Can enterprise AI agents become reliable enough for mission-critical workflows without becoming too expensive or too difficult to govern?

UiPath’s answer so far seems to be a hybrid model — combining deterministic RPA with controlled AI agents — which is more realistic than “fully autonomous AI workers.” But the technology is still evolving, and many enterprises are still in experimentation rather than large-scale production deployment. Review collected by and hosted on G2.com.

The platform offers all services in a single place, making it easy for users to access and use them efficiently. The documentation for each service is well-organized, clean, and provides the necessary explanations clearly. Review collected by and hosted on G2.com.

It is unclear how the platform units are being consumed, as the dashboard does not provide sufficient information regarding this. Review collected by and hosted on G2.com.

I think UiPath Agentic Automation is a fantastic product. I really appreciate its capability to build and orchestrate agents seamlessly using Maestro. It's also impressive how the platform lets us pick different Large Language Models (LLMs). The use of various LLMs, external tools, and Multi-Cloud Platform (MCP) servers enhances the developer experience significantly. We've managed to create over 50 agents, which shows how reliable and effective it is. Additionally, importing custom agents is a feature I find particularly useful. The support from the UiPath product team was really good, which made the setup not that difficult. Review collected by and hosted on G2.com.

If they can provide updates on LLMs versions quickly it would be helpful. Review collected by and hosted on G2.com.

I appreciate UiPath Agentic Automation for its ability to enable intelligent decision-making beyond rule-based automation. I like the seamless integration of RPA with AI and human-in-the-loop processes, which speeds up the handling of complex and unstructured data while improving accuracy by reducing manual errors. The platform provides better process visibility, monitoring, and governance while offering scalability for high-volume business operations. I value its strong orchestration and exception management capabilities, along with enhanced compliance and audit tracking. I also find the fast deployment of enterprise automation solutions and the support for end-to-end digital transformation initiatives highly beneficial. Review collected by and hosted on G2.com.

1. Initial setup and configuration can be complex for large-scale enterprise implementations. 2. Learning curve is high for advanced Agentic Automation features, especially for new users. 3. Licensing and pricing can be expensive for small and mid-sized organizations. 4. AI model training and fine-tuning sometimes require additional effort for better accuracy. 5. Debugging complex agent workflows can be challenging compared to traditional RPA processes. 6. Performance optimization is needed for high-volume processes with multiple AI decision points. 7. Documentation for newer Agentic Automation capabilities could be more detailed and practical. 8. Integration with some third-party legacy systems may still require custom handling. 9. Real-time monitoring and deeper analytics for agent decisions can be improved further. 10. Faster support response for complex production issues would enhance the overall experience. Review collected by and hosted on G2.com.

I use UiPath Agentic Automation to streamline and automate business processes, which improves operational efficiency and reduces manual effort. I love the overall developer experience offered by its intuitive and user-friendly builder interface, which makes designing and managing automation workflows much easier. The evaluation features are very useful for testing, validating, and improving agent performance. Additionally, the ease of development allows my team to quickly build and deploy solutions without a steep learning curve. The descriptive interface clearly presents workflows, configurations, and agent behaviors, making development faster and reducing the effort needed to understand complex processes. The evaluation features provide better visibility into agent responses and performance, making it easier to identify gaps, refine logic, and continuously improve the quality and reliability of automation. Review collected by and hosted on G2.com.

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I like UiPath Agentic Automation for its ability to combine AI agents, automation, and enterprise workflows in a scalable way, making complex business processes easier to automate. I appreciate its flexibility for both low-code and developer-focused implementations. The integration capabilities help me connect APIs, databases, cloud platforms, and enterprise applications quickly, making automation faster and more reliable. The workflow orchestration feature is particularly valuable as it helps coordinate AI agents, backend services, and automated tasks efficiently. The platform provides strong AI-driven automation capabilities, good enterprise integration support, and is valuable for scaling intelligent automation across teams. I also found the initial setup to be smooth for standard workflows and integrations, with its available templates and orchestration features. Review collected by and hosted on G2.com.

One area that could be improved is the learning curve for advanced orchestration and AI-driven workflows, especially for developers new to the platform. Some complex integrations and debugging scenarios can also take time to troubleshoot in large enterprise environments. I would also like to see even more flexibility for code-first developers, deeper AI customization options, and improved monitoring and observability features for large-scale automation systems. Review collected by and hosted on G2.com.

I like UiPath Agentic Automation's ability to combine traditional RPA with AI-driven intelligence to create more adaptive and human-like automation solutions. The seamless integration of AI Agents, Document Understanding, GenAI activities, and orchestration capabilities makes it easier to automate complex end-to-end business processes efficiently. I appreciate the low-code development experience, scalability, and the flexibility to rapidly build innovative POCs and enterprise-grade solutions. It significantly enhances automation capabilities beyond conventional rule-based workflows. Review collected by and hosted on G2.com.

While UiPath Agentic Automation offers powerful AI-driven capabilities, there are still areas that could be improved. Some advanced agentic and GenAI features have a learning curve and require deeper understanding for effective implementation in enterprise-grade solutions. In certain scenarios, AI responses and decision-making can be inconsistent, which requires additional validations and human oversight for critical business processes. There is also room for improvement in documentation, debugging visibility, and cost optimization for AI-intensive workflows, especially when scaling solutions across larger environments. Review collected by and hosted on G2.com.