UiPath Platform

What I like most about UiPath is its ability to simplify and accelerate automation development through an intuitive and well-structured environment. The platform offers a rich library of pre-built activities, connectors, and templates that significantly reduce development time and allow us to focus on solving business challenges rather than building everything from scratch. I also value its strong orchestration and monitoring capabilities. UiPath Orchestrator provides full visibility of our automation ecosystem, making it easy to schedule, manage, and scale robots as our needs grow. This has been essential for maintaining reliability and operational continuity to automated processes. Additionally, the constant evolution of the platform, especially with AI-powered features, document understanding, and improved integrations, has enabled us to expand use cases and deliver more impactful automation initiatives across the organization.

We had a positive experience with UiPath, particularly in the context of automating invoice processing. Utilizing this tool allowed me to automate the extraction of data, validate it and post it to our SAP system efficiently. This automation has substantially reduced the manual efforts involved in processing invoices, which has been a game changer for any organisation.

UiPath Agentic Automation provides unified orchestration across bots, AI agents and human touchpoints, allowing organizations to design much more dynamic and adaptive workflows. The platform offers strong governance and compliance features such as role-based access contrtol and full audit trails, which ensure trust and transparency when deploying autonomous agents. Its low-code and pro-code environment makes it flexible, enabling business users to prototype quickly while allowing developers to refine and extend capabilities. Existing RPA assets and bots can be reused seamlessly within the new agentic framework, protecting previous investments and reducing migration effort.

Some of its components, especially those related to advanced automation, AI features and document understanding, still require considerable configuration effort and can feel complex for new or non-technical users. While the platform is useful, achieving optimal performance sometimes involves navigating multiple tools, packages, or dependencies that are not always straightforward.

Issues we've been experiencing sometimes with this product's ability to handle large data sheets. Specifically, we have encountered problems when working with excel sheets containing upwards of hundred or thousand rows, as the software frequently crashes under this load.

The platform's performance and scalability can be challenged when many agents or complex decision loops are involved, which may lead to latency or higher resource consumption. The transparency of AI-driven decision logic is still limited, making it harder to fully understand, debug or audit certain agentic workflows. Licensing and cost structures can become expensive as agent complexity or usage volume increases, which requires careful cost management. The learning curve is steeper compared to traditional RPA, as designing agentic flows requires a different mindset and new skills. Some features, such as scheduling and agent feedback mechanisms, feel less mature and could be further improved as the platform evolves.