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UiPath

This course, available on Coursera with content from UiPath, takes you through the complete journey of building, configuring, and evaluating intelligent agents in UiPath Studio Web, with a focus on Generative AI (Gen AI) activities and scalable evaluation strategies.

You’ll begin with Build your first agent with Studio Web, where you’ll create intelligent agents using no-code tools and Autopilot. Then, in Agentic prompt engineering, you’ll learn to design effective prompts that help AI agents deliver accurate, structured, and useful outputs.

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This course, available on Coursera with content from UiPath, takes you through the complete journey of building, configuring, and evaluating intelligent agents in UiPath Studio Web, with a focus on Generative AI (Gen AI) activities and scalable evaluation strategies.

You’ll begin with Build your first agent with Studio Web, where you’ll create intelligent agents using no-code tools and Autopilot. Then, in Agentic prompt engineering, you’ll learn to design effective prompts that help AI agents deliver accurate, structured, and useful outputs.

With Configure context and escalations for agents, you’ll make your agents enterprise-ready by grounding their responses in business context and creating escalation flows for human-in-the-loop scenarios. Finally, in Configure evaluations for agents, you’ll explore structured evaluation sets and scoring methods like LLM-as-a-Judge, Exact Match, and JSON Similarity to test and refine your agents.

By the end of this course, you’ll be equipped to build and deploy Studio Web agents that are reliable, context-aware, and evaluation-driven.

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Learners who complete UiPath Agentic Automation Associate will develop knowledge and skills that may be useful to these careers:
Prompt Engineer
A Prompt Engineer specializes in crafting, refining, and optimizing prompts to guide large language models and AI agents to produce accurate, structured, and useful outputs. This course is exceptionally tailored for aspiring Prompt Engineers, dedicating an entire module to agentic prompt engineering. Learners will delve into techniques such as zero-shot, few-shot, and chain-of-thought prompting, understanding how to apply system versus user prompts for autonomous agents. The curriculum also covers prompt health scoring and optimization, which are crucial for ensuring the reliability and performance of AI agents in real-world applications. By mastering these specific skills, you will be well-equipped to design effective prompts that drive the intelligence and utility of Generative AI agents, a core competency for this career.
AI Automation Engineer
An AI Automation Engineer is responsible for designing, developing, and deploying intelligent systems that automate complex business processes using artificial intelligence. This course provides a robust foundation for becoming an effective AI Automation Engineer by guiding learners through the complete journey of building, configuring, and evaluating intelligent agents in UiPath Studio Web. You will gain hands-on experience creating agents using no-code tools and Autopilot, focusing on Generative AI activities. The detailed modules on agentic prompt engineering, configuring context, and designing escalation flows for human-in-the-loop scenarios are essential for building reliable, enterprise-ready solutions. Furthermore, the emphasis on structured evaluation sets and scoring methods like LLM-as-a-Judge helps ensure the quality and effectiveness of deployed automation, a critical skill for this role.
AI Quality Assurance Engineer
An AI Quality Assurance Engineer is dedicated to testing, validating, and ensuring the reliability, accuracy, and performance of artificial intelligence systems, including intelligent agents. This course provides an exceptional fit for an AI Quality Assurance Engineer, particularly through its dedicated module on configuring evaluations for agents. You will learn to create structured evaluation sets and utilize powerful scoring methods such as LLM-as-a-Judge, Exact Match, and JSON Similarity. These techniques are fundamental for systematically testing and refining agents. Furthermore, the course teaches how to track agent health and ensure enterprise readiness, directly aligning with the core responsibilities of a QA professional in the AI domain. This robust foundation in evaluation empowers you to deliver high-quality, dependable AI solutions.
Generative AI Developer
A Generative AI Developer creates and implements applications that harness the power of generative artificial intelligence to produce new content, solve problems, or automate tasks. This course is highly relevant for a Generative AI Developer, as it focuses extensively on Generative AI activities within the context of intelligent agents. You will learn to build and deploy Studio Web agents, which are inherently driven by Gen AI, utilizing no-code tools and Autopilot. The curriculum's emphasis on designing effective prompts to generate accurate outputs and configuring robust evaluation strategies (including LLM-as-a-Judge) is vital. This foundational knowledge ensures you can confidently develop agents that are reliable, context-aware, and evaluation-driven, preparing you for the practical demands of creating cutting-edge Gen AI solutions.
Machine Learning Operations Engineer
A Machine Learning Operations Engineer builds and maintains the infrastructure and processes for deploying, monitoring, and managing machine learning models and AI systems in production environments. This course is highly relevant for a Machine Learning Operations Engineer, as it covers the practical aspects of deploying Studio Web agents and ensuring their enterprise readiness. The curriculum emphasizes scalable evaluation strategies, which are crucial for monitoring agent performance post-deployment. Learning to configure context, track agent health, and design human-in-the-loop escalation flows provides critical skills for managing agents effectively throughout their lifecycle. This knowledge ensures that deployed agents remain reliable, context-aware, and perform optimally in operational settings, aligning perfectly with MLOps principles.
Robotic Process Automation Developer
A Robotic Process Automation Developer designs, builds, and deploys software robots to automate repetitive tasks, enhancing efficiency and accuracy. While traditional RPA often focuses on structured tasks, this course expands those capabilities by introducing intelligent agents in UiPath Studio Web. As a Robotic Process Automation Developer, you will learn to configure agents, integrate Generative AI activities, and build advanced automation solutions that can handle more complex, cognitive tasks. The modules on configuring context, creating escalation flows for human-in-the-loop scenarios using Action Center, and designing multi-agent flows are particularly beneficial for extending RPA into agentic automation, making processes more adaptable and robust in enterprise environments.
Automation Engineer
An Automation Engineer designs, implements, and maintains systems and processes that operate automatically, often integrating various technologies to streamline operations. This course enhances the skill set of an Automation Engineer by focusing on intelligent agentic automation. You will learn the complete journey of building, configuring, and evaluating intelligent agents in UiPath Studio Web, leveraging Generative AI activities. The ability to create agents using no-code tools, design effective prompts, and configure context and escalation flows is directly applicable to developing robust and adaptable automation solutions. Furthermore, the emphasis on structured evaluation strategies helps ensure the reliability and continuous improvement of automated processes, making you adept at deploying modern, intelligent automation systems.
AI Solutions Architect
An AI Solutions Architect designs and oversees the implementation of enterprise-level artificial intelligence solutions, ensuring they meet business needs and integrate seamlessly into existing IT infrastructures. This course helps an aspiring AI Solutions Architect by providing an in-depth understanding of building, configuring, and evaluating intelligent agents, which are key components in many modern AI architectures. You will learn to make agents enterprise-ready by grounding their responses in business context and designing escalation flows that combine AI with human decisions. This knowledge is crucial for architecting reliable, context-aware, and scalable agent deployments. The focus on evaluation strategies also ensures that architects can design systems with measurable performance and health tracking, vital for successful enterprise adoption. This role typically requires an advanced degree in a related technical field, along with significant professional experience.
AI Integration Specialist
An AI Integration Specialist focuses on seamlessly embedding AI solutions, such as intelligent agents, into existing enterprise systems, applications, and workflows. This course provides a strong foundation for an AI Integration Specialist by covering the configuration of intelligent agents and their operational aspects. You will learn to make agents enterprise-ready by grounding their responses in specific business contexts using storage buckets, ensuring data continuity. Crucially, the course teaches how to design escalation apps using Action Center and build multi-agent flows that combine AI with human decisions. This knowledge is vital for creating robust interfaces and orchestrating complex interactions between AI agents and other system components, facilitating smooth and effective deployments within diverse IT landscapes.
Data Analyst Automation Performance
A Data Analyst Automation Performance collects, processes, and analyzes data to measure the efficiency, effectiveness, and impact of automated processes and AI solutions. This course provides highly relevant skills for a Data Analyst Automation Performance, particularly through its comprehensive module on configuring evaluations for agents. You will learn agent scoring and evaluation basics, how to create structured evaluation sets, and use powerful scoring methods like LLM-as-a-Judge, Exact Match, and JSON Similarity. This knowledge is fundamental for collecting meaningful data on agent performance and output quality. By mastering these techniques, you will be equipped to track agent health and derive actionable insights, which are crucial for optimizing automation performance and demonstrating business value.
Business Process Automation Consultant
A Business Process Automation Consultant advises organizations on optimizing workflows and implementing automation technologies to improve efficiency and productivity. This course prepares consultants to leverage advanced agentic automation. As a Business Process Automation Consultant, you will gain a deep understanding of building intelligent agents using no-code tools and Autopilot in UiPath Studio Web. The ability to configure agents to be enterprise-ready by grounding responses in business context and designing escalation flows for human intervention is critical for delivering comprehensive solutions. Understanding scalable evaluation strategies also allows you to quantify the impact and reliability of proposed automations, thereby strengthening your recommendations and ensuring successful client implementations in evolving business landscapes.
Continuous Improvement Specialist Automation
A Continuous Improvement Specialist Automation identifies opportunities to enhance processes and systems through automation, constantly seeking efficiencies and optimizing performance. This course is well-suited for a Continuous Improvement Specialist Automation, providing the tools to build and refine intelligent agents. The curriculum's strong focus on configuring evaluations for agents, using structured evaluation sets, and robust scoring methods like LLM-as-a-Judge, Exact Match, and JSON Similarity, directly supports the continuous improvement cycle. You will learn to track agent health and ensure enterprise readiness, which enables systematic monitoring and iterative refinement of automated processes. This capability is essential for sustaining high performance and adapting automation solutions to evolving business requirements.
AI Product Manager
An AI Product Manager guides the strategy, development, and launch of artificial intelligence features and products, bridging technical capabilities with market needs. This course may be helpful for an AI Product Manager by providing a foundational understanding of how intelligent agents are built, configured, and evaluated. Understanding core agent components, the specifics of prompt engineering for desired outputs, and how to ground agents in business context are insights that can inform product vision and feature prioritization. While the role primarily focuses on market and user needs, knowing the practical aspects of building enterprise-ready agents and their evaluation strategies can lead to more informed product decisions and realistic development roadmaps for AI-powered solutions.
Technical Support Specialist AI Automation
A Technical Support Specialist AI Automation provides expert assistance and resolves issues for users deploying or maintaining AI-powered automation solutions and intelligent agents. This course may be useful for a Technical Support Specialist AI Automation, as it provides a comprehensive understanding of how UiPath Studio Web agents are built, configured, and evaluated. Knowledge of core agent components, prompt engineering, context configuration, and escalation flows for human-in-the-loop scenarios is invaluable for diagnosing and troubleshooting common problems. By understanding the underlying architecture and operational principles of these agents, you will be better equipped to provide effective support and guidance, ensuring smooth operation for end-users and facilitating quicker problem resolution in complex automation environments.
AI Ethics and Governance Specialist
An AI Ethics and Governance Specialist works to ensure that AI systems are developed, deployed, and used responsibly, adhering to ethical principles, regulations, and societal values. This course may be helpful for an AI Ethics and Governance Specialist by providing a detailed understanding of how AI agents are configured and evaluated, which is crucial for identifying and mitigating risks. Gaining insight into grounding responses in business context helps address potential biases or misinterpretations. The course's evaluation methods, including LLM-as-a-Judge, offer practical means to assess agent behavior for fairness and accuracy, informing ethical oversight. Understanding human-in-the-loop scenarios is also key to designing transparent and accountable AI systems. This role often requires an advanced degree in a relevant field.

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in UiPath Agentic Automation Associate.
This comprehensive textbook provides a broad overview of artificial intelligence, including chapters on intelligent agents, natural language processing, machine learning, and computer vision. It is written in a clear and accessible style, and is suitable for both undergraduate and graduate students.
This practical guide covers the entire process of implementing an enterprise AI platform, from assessing strategic needs to building a sustainable ecosystem.
Provides a comprehensive guide to prompt engineering, covering techniques for crafting effective inputs to generative AI models. It's particularly useful for understanding how to obtain reliable and predictable results, which is crucial for both beginners and those looking to deepen their practical skills. This book is valuable as a current reference for anyone working with generative AI.
This comprehensive handbook covers the entire field of robotics and automation, from the basics to the latest developments. It is an essential resource for anyone who wants to learn about this rapidly growing field.
Provides a comprehensive overview of the algorithmic, game-theoretic, and logical foundations of multi-agent systems. It is written in a clear and accessible style, and is suitable for both undergraduate and graduate students.
This practical book covers a wide range of AI techniques, including machine learning, deep learning, and natural language processing, with hands-on exercises.
Provides a practical guide to using generative AI, covering the different techniques and tools available. It is written by two leading experts in the field, Josh Patterson and Adam Gibson.
Provides a hands-on approach to prompt engineering with a focus on using the OpenAI API. It's particularly relevant for undergraduate students, graduate students, and working professionals who want to programmatically interact with LLMs. It serves as a practical guide for implementing prompt engineering techniques in applications.
Offers a practical, hands-on approach to prompt engineering specifically with ChatGPT. It's an excellent resource for high school and undergraduate students getting started, providing clear examples and exercises. It serves as a useful introductory guide and additional reading to complement foundational AI courses.
Focuses on the creative aspects of prompt engineering and generating diverse language outputs. It's a good fit for students and professionals looking to go beyond basic prompting and explore more advanced techniques for creative content generation. It adds breadth by covering applications in areas like creative writing and podcasting.
Provides a comprehensive overview of machine learning concepts and their applications in business, with real-world examples and case studies.
While not solely focused on No-Code development, this book explores the principles of Lean software development and DevOps, which are highly relevant to the No-Code approach. It provides valuable insights into agile methodologies, continuous delivery, and building high-performing technology teams.
This guide aims to make prompt engineering accessible with a step-by-step approach. It is well-suited for beginners and those new to the field, including high school students and those in introductory undergraduate programs. It provides practical tips and is useful for gaining a broad understanding of how to formulate effective AI prompts.
This practical book introduces deep learning concepts and techniques using the Fastai library, making it accessible to developers without a background in machine learning.
A comprehensive guide aimed at making prompt engineering accessible to a wide audience. It covers various techniques and applications for leveraging AI language models effectively. is useful for high school students through working professionals seeking a thorough introduction and practical guidance.
This guide offers quick tips and tricks for prompt engineering across different generative AI models, including text and image generation models. It's a practical resource for users who want to improve their immediate results with various AI tools. It's suitable for all levels looking for actionable advice.
This report by Forrester provides an in-depth analysis of the No-Code/Low-Code market, including its key trends, challenges, and vendor landscape. It offers valuable insights for business leaders and IT professionals considering adopting No-Code solutions.
Provides a comprehensive overview of reinforcement learning, a type of machine learning that allows agents to learn how to behave in an environment by interacting with it. It is written in a clear and accessible style, and is suitable for both undergraduate and graduate students.
Provides a comprehensive overview of intelligent robotics, including the design, implementation, and evaluation of intelligent agents. It is written by two leading researchers in the field and is considered one of the best textbooks on intelligent robotics.
Provides a comprehensive overview of probabilistic robotics, a type of robotics that uses probability theory to model the world. It is written by three leading researchers in the field and is considered one of the best textbooks on probabilistic robotics.
Provides a comprehensive overview of deep learning, a type of machine learning that is used to train intelligent agents. It is written by three leading researchers in the field and is considered one of the best books on deep learning.
Provides a business-oriented perspective on generative AI, discussing its potential impact on industries and how companies can use it to gain a competitive advantage. It is written by three leading experts in the field, Thomas Davenport, Rajeev Ronanki, and Nitin Mittal.
Provides a thought-provoking exploration of the future of generative AI, discussing its potential benefits and risks. It is written by Gary Marcus, a leading researcher in the field.
While not solely focused on prompt engineering, this book provides a strong foundation in understanding how LLMs work, which is essential for effective prompting. It's suitable for undergraduate and graduate students, offering technical insights into language understanding and generation. It serves as valuable background reading for those wanting to understand the underlying mechanisms of the models they are prompting. Expected publication in September 2024.
Explores prompt engineering within the broader context of generative AI and touches upon ethical considerations. It's relevant for all levels, providing a balanced view of the technical aspects and the societal impact of generative AI. It's useful for gaining a broader understanding and considering the responsible use of AI.
This handbook is geared towards engineers working with LLMs, covering the process from concept to production. It provides a deeper technical understanding of integrating and managing LLMs, which is relevant for prompt engineers working in a development context. It is best suited for graduate students and working professionals.
This practical book covers the end-to-end process of building machine learning pipelines, including data engineering, model training, and deployment.
Covers the broader field of AI engineering with a focus on foundation models, which include the LLMs used in prompt engineering. It's relevant for professionals and graduate students interested in the engineering aspects of building AI applications. It provides context on how prompt engineering fits into larger AI systems.
Focuses on the practical aspects of building NLP systems. While prompt engineering specific technique, understanding the entire NLP pipeline is beneficial for professionals. It's a good reference for those looking to implement prompt engineering within larger NLP applications.
Provides a comprehensive overview of machine learning, a subfield of artificial intelligence that allows computers to learn from data. It is written in a clear and accessible style, and is suitable for both undergraduate and graduate students.
This beginner-friendly guide introduces the basics of predictive analytics and its use in various industries.
This classic and widely-referenced textbook in the field of NLP. While it predates the latest advancements in LLMs and prompt engineering, it provides a foundational understanding of language processing, which is crucial for anyone serious about the field. It's highly recommended for undergraduate and graduate students as a comprehensive reference for core NLP concepts.
For those who want to understand the mechanics of LLMs deeply, this book guides you through building one from scratch. This is highly technical and suitable for advanced undergraduate students, graduate students, and researchers. A deep understanding of LLM architecture is beneficial for advanced prompt engineering techniques.
Explores the relationship between generative AI and the creative process, discussing how generative AI can be used to enhance creativity. It is written by Margaret Boden, a leading researcher in the field.
Widely used introduction to NLP using the NLTK library in Python. It's excellent for beginners and undergraduate students to gain practical skills in processing and analyzing text data, which fundamental prerequisite for prompt engineering. It serves as a hands-on guide for learning the basics of NLP.
Provides a comprehensive overview of data mining, including its applications to intelligent agents. It is written by four leading researchers in the field and is considered one of the best books on data mining.
Is specifically aimed at educators but offers valuable insights into crafting prompts for educational purposes. It's relevant for anyone interested in using prompt engineering in a learning context. It provides practical strategies and examples for a specific application domain.
Uses illustrations and analogies to explain deep learning concepts in a clear and intuitive way.
Provides a comprehensive overview of computer vision, including its applications to intelligent agents. It is written by two leading researchers in the field and is considered one of the best textbooks on computer vision.
Teaches you how to use Python to automate tasks in a variety of domains, such as web scraping, data analysis, and software testing.
A foundational text in statistical NLP, this book provides the theoretical background necessary for understanding many of the techniques used in modern LLMs. It's a valuable resource for graduate students and researchers looking to deepen their understanding of the statistical underpinnings of language models. It is more theoretical and serves as a strong reference for advanced learners.
A beginner-friendly guide to using ChatGPT. is ideal for high school students and those new to generative AI who want a straightforward introduction to interacting with models. It focuses on practical usage and good starting point before diving into more complex prompt engineering concepts.
Provides a comprehensive overview of distributed artificial intelligence, a subfield of artificial intelligence that allows multiple agents to work together to solve problems. It is written in a clear and accessible style, and is suitable for both undergraduate and graduate students.
Explores the potential applications of generative AI in healthcare, discussing how it could be used to improve patient care and accelerate drug discovery. It is written by Eric Topol, a leading researcher in the field.

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