{"id":238495,"date":"2026-04-08T15:04:27","date_gmt":"2026-04-08T13:04:27","guid":{"rendered":"https:\/\/www.itta.net\/?post_type=formations&#038;p=238495"},"modified":"2026-04-12T18:59:03","modified_gmt":"2026-04-12T16:59:03","slug":"creating-deploying-ai-agents-enterprise","status":"publish","type":"formations","link":"https:\/\/www.itta.net\/en\/trainings\/artificial-intelligence\/ai-agents-and-automation\/copilot-studio-and-microsoft-agents\/creating-and-deploying-ai-agents-in-the-enterprise\/","title":{"rendered":"Creating and Deploying AI Agents in the Enterprise"},"content":{"rendered":"\r\n<p class=\"wp-block-paragraph\">The Creating and Deploying AI Agents in the Enterprise training helps participants discover what an AI agent truly is, how it differs from a standard conversational assistant, and how it can be used to automate interactions, execute tasks or orchestrate processes within a business context. It provides a structured view of the steps required to move from an agent concept to a deployable solution.<\/p>\r\n\r\n\r\n\r\n<p><strong>An advanced training course for tackling more sophisticated use cases<\/strong><\/p>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\">During this training course, participants learn the design principles of an AI agent, explore viable use cases, understand essential scoping requirements, and address security, governance and integration considerations, as well as the conditions for successful enterprise deployment. This course serves as a natural bridge between basic AI usage and more advanced environments such as Copilot Studio, business automation or service orchestration. Participants leave with a clear roadmap for their first agent project.<\/p>\r\n","protected":false},"featured_media":0,"parent":0,"menu_order":0,"template":"","editeurs":[5037,4257,1488],"domaine":[4248,2982,4269,4241,2980],"class_list":["post-238495","formations","type-formations","status-publish","hentry","editeurs-copilot-studio","editeurs-itta-artificial-intelligence","editeurs-microsoft","domaine-ai-agent-creation-and-automation","domaine-artificial-intelligence","domaine-copilot-studio-and-microsoft-agents","domaine-creation-dagents-ia-et-automatisation","domaine-intelligence-artificielle"],"acf":{"reference":"AI-03-05","duree_nombre":1,"duree_unite":"jour","prix_virtuel":750,"prix_presentiel":800,"prix_blended":"","prix_elearning":"","views":93,"niveau":"a:1:{i:0;s:6:\"Avance\";}","role":"","certifiant":"","pdus":"","lieux":["Gen\u00e8ve","Lausanne"],"formats_dapprentissage":["presentiel","virtuel"],"examen_inclus":"Non certifiant","garanti":"","financement":["Temptraining"],"description_temptraining":"","description_caf":"","description_title":"Creating and Deploying AI Agents in the Enterprise Training","objectifs":"<ul>\r\n \t<li>Understand what an AI agent is and where it fits<\/li>\r\n \t<li>Distinguish AI agents from conversational assistants and traditional automation<\/li>\r\n \t<li>Identify enterprise use cases for AI agents<\/li>\r\n \t<li>Structure the scoping phases of an AI agent project<\/li>\r\n \t<li>Understand deployment, governance and control challenges<\/li>\r\n \t<li>Prepare a realistic and practical plan for AI agents in the organization<\/li>\r\n<\/ul>","connaissances":"<ul>\r\n \t<li>A solid understanding of general AI use cases is required<\/li>\r\n \t<li>Prior exposure to automation topics is recommended<\/li>\r\n<\/ul>","profils_participants":"<ul>\r\n \t<li>Project managers<\/li>\r\n \t<li>Innovation managers<\/li>\r\n \t<li>Managers<\/li>\r\n \t<li>Process owners<\/li>\r\n \t<li>Consultants<\/li>\r\n \t<li>Professionals involved in transformation or automation initiatives<\/li>\r\n \t<li>Anyone looking to understand how to implement AI agents in the enterprise<\/li>\r\n<\/ul>","examen":"","documentation":"<ul>\n \t<li>Support de cours num\u00e9rique inclus<\/li>\n<\/ul>","lab__exercices":"<ul>\n \t<li>This course includes workshops on mapping use cases, scoping AI agents, defining interaction flows and analyzing deployment conditions for AI agents in a professional context.<\/li>\n<\/ul>","contenu_cours":"<strong>Module 1: Understanding What an AI Agent Is<\/strong>\r\n<ul>\r\n \t<li>Definition of an AI agent<\/li>\r\n \t<li>Difference between an agent, a chatbot, a conversational assistant and an automated workflow<\/li>\r\n \t<li>Overview of use cases<\/li>\r\n \t<li>Positioning within the enterprise AI ecosystem<\/li>\r\n<\/ul>\r\n<strong>Module 2: Identifying Relevant Use Cases<\/strong>\r\n<ul>\r\n \t<li>Support agents<\/li>\r\n \t<li>Qualification agents<\/li>\r\n \t<li>Document processing agents<\/li>\r\n \t<li>Coordination agents<\/li>\r\n \t<li>Business assistance agents<\/li>\r\n \t<li>Determining where an agent creates real value<\/li>\r\n<\/ul>\r\n<strong>Module 3: Designing a Useful AI Agent<\/strong>\r\n<ul>\r\n \t<li>Defining the agent's mission<\/li>\r\n \t<li>Scoping inputs and outputs<\/li>\r\n \t<li>Identifying useful data<\/li>\r\n \t<li>Specifying business rules<\/li>\r\n \t<li>Planning for edge cases<\/li>\r\n \t<li>Determining the role of human oversight<\/li>\r\n<\/ul>\r\n<strong>Module 4: Integrating an Agent into an Enterprise Environment<\/strong>\r\n<ul>\r\n \t<li>Connecting to tools and processes<\/li>\r\n \t<li>Managing rights and access<\/li>\r\n \t<li>Usage governance<\/li>\r\n \t<li>Supervision logic<\/li>\r\n \t<li>Preparing progressive deployment<\/li>\r\n<\/ul>\r\n<strong>Module 5: Governance, Security and Quality<\/strong>\r\n<ul>\r\n \t<li>Response reliability<\/li>\r\n \t<li>Error handling<\/li>\r\n \t<li>Sensitive data<\/li>\r\n \t<li>Traceability<\/li>\r\n \t<li>Responsibilities<\/li>\r\n \t<li>AI agent governance best practices<\/li>\r\n<\/ul>\r\n<strong>Module 6: Preparing a Deployment Project<\/strong>\r\n<ul>\r\n \t<li>Choosing a pilot use case<\/li>\r\n \t<li>Defining success criteria<\/li>\r\n \t<li>Measuring created value<\/li>\r\n \t<li>Supporting users<\/li>\r\n \t<li>Evolving the agent over time<\/li>\r\n<\/ul>","cours_recommandes":[238512,238492,238507,238506,238511],"infos_additionnelles":"<h3>AI Agents: The New Frontier of Automation<\/h3>\r\n<p>AI agents represent a major evolution beyond simple chatbots or conversational assistants. An AI agent can execute tasks autonomously, make contextual decisions, interact with external systems and coordinate complex sequences of actions.<\/p>\r\n<p>For businesses, AI agents open automation possibilities that were inaccessible with traditional approaches. They can manage complete processes from initial trigger to final result delivery.<\/p>\r\n\r\n<h3>How an AI Agent Works<\/h3>\r\n<p>An AI agent combines a language model with tools and data sources. The language model provides reasoning and comprehension capabilities. Tools enable interaction with the outside world, such as sending emails, querying databases or calling APIs. Data sources provide the context needed for decision-making.<\/p>\r\n<p>The agent can break down a complex task into subtasks, choose the right tools for each step and adjust its strategy based on intermediate results. This autonomy fundamentally distinguishes it from a simple conversational assistant.<\/p>\r\n\r\n<h3>Enterprise Use Cases for AI Agents<\/h3>\r\n<p>AI agents find applications in many domains. A customer support agent can resolve requests end to end by consulting the knowledge base, checking order status and proposing a personalized solution. An analysis agent can collect data from multiple sources, analyze it and produce a structured report.<\/p>\r\n<p>Monitoring agents continuously watch relevant information sources and alert decision-makers when significant changes occur. Document management agents classify, summarize and route incoming documents autonomously.<\/p>\r\n\r\n<h3>Designing an Effective and Safe AI Agent<\/h3>\r\n<p>Designing an effective AI agent requires careful thought about the scope of action, guardrails and supervision mechanisms. An overly autonomous agent may take unwanted actions, while an overly constrained agent loses its value. The right balance depends on context and acceptable risk level.<\/p>\r\n<p>Human-in-the-loop validation, detailed action logs and scope limitations are essential to ensure safe operation aligned with organizational expectations.<\/p>\r\n\r\n<h3>Deploying AI Agents in Your Organization<\/h3>\r\n<p>AI agent deployment follows a progressive approach from prototype to production. The testing phase is critical for identifying edge cases, reasoning errors and supervision needs. Production monitoring enables continuous improvement of agent performance.<\/p>\r\n<p>ITTA supports companies in French-speaking Switzerland in designing and deploying AI agents with practical training delivered in Geneva and Lausanne, covering technical, organizational and governance aspects.<\/p>\r\n\r\n\r\n<h3>Automation and Process Transformation in French-Speaking Switzerland<\/h3>\r\n<p>Companies in French-speaking Switzerland face specific automation challenges. Labor costs, talent shortages in certain fields and growing productivity demands create a favorable context for adopting AI-powered automation. Organizations that invest in these technologies see a rapid and lasting return on their investment.<\/p>\r\n<p>The Swiss technology ecosystem provides a conducive environment for intelligent automation. Locally available cloud infrastructure, clear data protection regulatory frameworks and the digital maturity of businesses facilitate the deployment of automation solutions. Team training is the cornerstone of successful transformation, ensuring that internal skills keep pace with technological evolution.<\/p>\r\n\r\n<p><strong>What is the difference between a chatbot and an AI agent?<\/strong><\/p>\r\n<p>A chatbot answers questions within a conversational framework. An AI agent can execute actions, interact with external systems and make decisions autonomously. The agent is capable of managing a process from start to finish.<\/p>\r\n\r\n<p><strong>Do you need to know how to code to create an AI agent?<\/strong><\/p>\r\n<p>No-code platforms allow you to create simple agents without programming. For more complex agents with custom integrations, Python skills are an asset. The training covers both approaches.<\/p>\r\n\r\n<p><strong>Are AI agents reliable for critical tasks?<\/strong><\/p>\r\n<p>Reliability depends on design and supervision level. For critical tasks, human-in-the-loop validation is recommended. Agents are particularly reliable for repetitive, well-defined tasks.<\/p>\r\n\r\n<p><strong>What does an AI agent cost in production?<\/strong><\/p>\r\n<p>The main cost relates to API calls to language models. It varies depending on request volume and the model used. For most use cases, the cost is significantly lower than equivalent manual processing.<\/p>\r\n\r\n<p><strong>How do you supervise an AI agent in production?<\/strong><\/p>\r\n<p>Supervision involves detailed action logs, performance metrics, alerts for abnormal behavior and regular reviews of produced results. The training teaches best practices for monitoring and governance.<\/p>","inclus_dans_ce_cours_virtuel":"<ul>\n \t<li>Training provided by a domain expert<\/li>\n \t<li>Digital documentation and support materials<\/li>\n \t<li>Achievement badge<\/li>\n<\/ul>","inclus_dans_ce_cours_presentiel":"<ul>\n \t<li>Training provided by a domain expert<\/li>\n \t<li>Digital documentation and support materials<\/li>\n \t<li>Achievement badge<\/li>\n<\/ul>","inclus_dans_ce_cours_blended":"","inclus_dans_ce_cours_elearning":"","inclus_dans_ce_cours_surmesure":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>AI Agents Enterprise Deployment Training<\/title>\n<meta name=\"description\" content=\"AI agent training in Geneva and Lausanne. 1-day course to design, scope and deploy practical AI agents in the enterprise. Expert trainers. 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