{"id":2242,"date":"2025-07-22T19:02:30","date_gmt":"2025-07-22T19:02:30","guid":{"rendered":"https:\/\/solutionsreview.com\/thought-leaders\/?p=2242"},"modified":"2025-07-22T19:03:43","modified_gmt":"2025-07-22T19:03:43","slug":"the-risks-and-governance-requirements-of-agentic-ai","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/thought-leaders\/the-risks-and-governance-requirements-of-agentic-ai\/","title":{"rendered":"The Risks and Governance Requirements of Agentic AI"},"content":{"rendered":"<p style=\"text-align: justify;\"><strong><em>This article was written by Kevin Petrie, VP of Research at\u00a0<\/em><\/strong><a href=\"https:\/\/barc.com\/\" target=\"_blank\" rel=\"noopener nofollow\" class=\"external\"><strong><em>BARC<\/em><\/strong><\/a><strong><em>, a global research and consulting firm focused on data and analytics.\u00a0<\/em><\/strong><\/p>\n<p style=\"text-align: justify;\">Picture a chatbot that gives erroneous tax advice, insults a customer, or refuses to issue a justified refund, and you start to appreciate the risks of agentic AI.<\/p>\n<p style=\"text-align: justify;\">This blog, the first in a three-part series, explores why and how organizations must implement new governance controls to address the distinct requirements of AI models and the agents that use them. The second blog will define the must-have characteristics of an agentic AI governance program, and the third blog will recommend criteria to evaluate tools and platforms in this space.<\/p>\n<h3><strong>Agentic AI, Defined<\/strong><\/h3>\n<p style=\"text-align: justify;\"><a href=\"https:\/\/www.dataiku.com\/stories\/detail\/ai-agents\/\" target=\"_blank\" rel=\"noopener nofollow\" class=\"external\">Agentic AI<\/a>\u00a0refers to an application (also known as an agent) that uses AI models to make decisions and take actions with little or no human involvement. Agents assess various inputs, then plan and execute sequences of tasks to complete specific objectives. They often delegate tasks to tools, models, or other agents. More sophisticated agents transact with one another, reflect on their work, and iterate to improve outcomes.<\/p>\n<p style=\"text-align: justify;\">Agentic AI represents a compelling new opportunity for digital transformation. Most business, data, and AI leaders now view agents as the ideal vehicle for integrating AI\u00a0<a href=\"https:\/\/blog.dataiku.com\/ai-agents-turning-business-teams-from-ai-consumers-to-ai-creators\" target=\"_blank\" rel=\"noopener nofollow\" class=\"external\">into their business processes<\/a>. One-third of organizations already have agents in production, according to BARC research, as part of an ambitious push to improve efficiency, enrich user interactions, and gain competitive advantage.<\/p>\n<h3><strong>What Could Go Wrong?<\/strong><\/h3>\n<p>However, agentic AI poses considerable downside if not governed properly, and the downside only worsens as adopters consider more sophisticated and autonomous use cases. Let\u2019s consider the three primary domains of risk \u2014 data, models, and agents \u2014 then define the new governance controls that models and agents require.<\/p>\n<h3><strong>Governance Risks<\/strong><\/h3>\n<figure><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter\" src=\"https:\/\/blog.dataiku.com\/hs-fs\/hubfs\/image1-Jul-14-2025-07-57-01-4777-PM.png?width=800&amp;height=351&amp;name=image1-Jul-14-2025-07-57-01-4777-PM.png\" sizes=\"(max-width: 800px) 100vw, 800px\" srcset=\"https:\/\/blog.dataiku.com\/hs-fs\/hubfs\/image1-Jul-14-2025-07-57-01-4777-PM.png?width=400&amp;height=176&amp;name=image1-Jul-14-2025-07-57-01-4777-PM.png 400w, https:\/\/blog.dataiku.com\/hs-fs\/hubfs\/image1-Jul-14-2025-07-57-01-4777-PM.png?width=800&amp;height=351&amp;name=image1-Jul-14-2025-07-57-01-4777-PM.png 800w, https:\/\/blog.dataiku.com\/hs-fs\/hubfs\/image1-Jul-14-2025-07-57-01-4777-PM.png?width=1200&amp;height=527&amp;name=image1-Jul-14-2025-07-57-01-4777-PM.png 1200w, https:\/\/blog.dataiku.com\/hs-fs\/hubfs\/image1-Jul-14-2025-07-57-01-4777-PM.png?width=1600&amp;height=702&amp;name=image1-Jul-14-2025-07-57-01-4777-PM.png 1600w, https:\/\/blog.dataiku.com\/hs-fs\/hubfs\/image1-Jul-14-2025-07-57-01-4777-PM.png?width=2000&amp;height=878&amp;name=image1-Jul-14-2025-07-57-01-4777-PM.png 2000w, https:\/\/blog.dataiku.com\/hs-fs\/hubfs\/image1-Jul-14-2025-07-57-01-4777-PM.png?width=2400&amp;height=1053&amp;name=image1-Jul-14-2025-07-57-01-4777-PM.png 2400w\" alt=\"governance risks \" width=\"800\" height=\"351\" \/><\/figure>\n<ul>\n<li aria-level=\"1\"><strong>Data<\/strong>\u00a0governance risks relate to accuracy, privacy, bias, intellectual property protection, and compliance. While organizations still struggle to control these risks, they can rely on established tools and techniques.<\/li>\n<li aria-level=\"1\"><strong>Models<\/strong>\u00a0add the risks of toxic (i.e., inappropriate or malicious) content, and black-box logic that cannot be explained to stakeholders, such as customers and auditors. These risks \u2014 coupled with underlying data issues \u2014 help explain why 41% of practitioners do not trust the outputs of their AI\/ML models.<\/li>\n<li aria-level=\"1\"><strong>Agents<\/strong>\u00a0add the risks of misguided decisions, damaging actions, and even subversive intentions thanks to the possibility that\u00a0GenAI reasoning models will deceive humans and hide their actions.<\/li>\n<\/ul>\n<p style=\"text-align: justify;\">These rising, multiplying risks create an uncertain outlook for data, AI, and business leaders as they weave agents into their business processes. Complexity compounds the problem \u2014 each new dataset, model, and agent introduces new interdependencies and points of failure, creating an unwieldy web of things that can go wrong.<\/p>\n<h3><strong>New Governance Requirements<\/strong><\/h3>\n<p style=\"text-align: justify;\">While most organizations still struggle with data governance, they at least understand the nature of this longstanding problem. In this blog, we\u2019ll explore the new governance requirements of AI\/ML models and agents.<\/p>\n<h4><strong>Models<\/strong><\/h4>\n<p>Business, data, and AI stakeholders must address the model risks of toxicity and black-box logic:<\/p>\n<p><strong>1. Toxicity<\/strong><\/p>\n<p style=\"text-align: justify;\">Business owners must help data scientists and engineers select appropriate tables, documents, images, and so on that feed into retrieval-augmented generation (RAG) workflows for GenAI. Data scientists then test those models by prompting them with various scenarios and evaluating the resulting outputs. They collaborate with developers to implement rule-based checks (e.g., based on keywords) and evaluator models (e.g., based on sentiment), then alerts or filters that stop impermissible interactions. All these controls need continuous monitoring.<\/p>\n<p><strong>2. Black-Box Logic<\/strong><\/p>\n<p style=\"text-align: justify;\">Data scientists and engineers can make ML more explainable by implementing interpretable models such as decision trees or linear regressions. They also can use techniques such as SHAP to calculate the impact of features on ML model outputs, or LIME to approximate the relationships of inputs and outputs. Explainability is much more difficult with GenAI models due to the complexity of their underlying neural networks. As a partial solution, data scientists and engineers can satisfy basic customer or auditor concerns by ensuring GenAI workflows cite the sources of their content summaries.<\/p>\n<h4><strong>Agents<\/strong><\/h4>\n<p>These stakeholders also must address the risks that agents make misguided decisions, take damaging actions, and even develop subversive intentions.<\/p>\n<ol>\n<li><strong>Misguided Decisions:<\/strong>\u00a0Data scientists and ML engineers must closely monitor and inspect the decisions that agents make. Did the service chatbot advise a customer to disregard product installation instructions? Perhaps a shopping agent decides to make purchases that exceed its budget. To minimize decisions like these, data scientists and ML engineers must configure alerts and kill switches that activate when agents pass certain thresholds. And those controls should trigger a root-cause analysis so that data scientists and business owners can adjust model logic to avoid similar decisions in the future.<\/li>\n<li><strong>Damaging Actions:<\/strong>\u00a0If governance controls fail to flag or stop a bad decision, the agent might take the additional step of executing tasks or sequences of tasks based on that decision. Data scientists and developers must implement the appropriate controls to predict, identify, prevent, and remediate such actions. This might mean escalating a service ticket to an expert human or blocking a credit-card transaction for that ill-advised customer purchase. It also might mean notifying business owners of a problem, automatically de-activating an agent or tracing the lineage of an error to identify the root cause.<\/li>\n<li><strong>Subversive Intentions:<\/strong>\u00a0As GenAI-driven agents become more sophisticated, they might devise workflows that subvert human intentions or overstep ethical boundaries. While unlikely, this risk poses the greatest challenge because the signals are subtle, the problem is not well understood, and models might even respond to oversight by\u00a0<a href=\"https:\/\/openai.com\/index\/chain-of-thought-monitoring\/\" target=\"_blank\" rel=\"noopener nofollow\" class=\"external\">hiding their behavior<\/a>.\u00a0Data scientists can mitigate this risk in two ways. First, they can research the latest findings from OpenAI and other vendors about their models\u2019 known limitations and tendencies. Second, they must conduct rigorous tests by probing agents\u2019 reactions to various ethical or legal scenarios. Whatever the method, they must prevent suspicious behavior from making it to production.<\/li>\n<\/ol>\n<h3><strong>\u201cWisely and Slow\u201d<\/strong><\/h3>\n<p style=\"text-align: justify;\">As we learned from Romeo and Juliet, \u201cThey stumble that run fast.\u201d Agentic AI is no different. As organizations adopt agentic AI to boost efficiency and innovate, they must also manage new governance risks, from toxic outputs and opaque logic to misguided or subversive agent behavior. Reducing these risks requires cross-functional controls at every level, including safe data selection, explainability techniques, and safeguards like alerts, thresholds, and kill switches. The second blog in our series tackles the next question this raises: How does a modern governance program rise to this challenge?<\/p>\n<h4 class=\"hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_inline_text\" style=\"text-align: justify;\" data-hs-cos-general-type=\"widget\" data-hs-cos-type=\"inline_text\" data-hs-cos-field=\"section_heading\">Discover the blueprint to agentic AI with interviews from Siemens, Deloitte, EY, and more: <a href=\"https:\/\/pages.dataiku.com\/economist-impact-agentic-ai-advantage?_gl=1*ph4c5g*_gcl_au*MTUzNjEyOTA5Ny4xNzUzMTkxODU4*_ga*NDYzMTk3NDAuMTc1MzE5MTg1OA..*_ga_B3YXRYMY48*czE3NTMxOTE4NTgkbzEkZzAkdDE3NTMxOTIwODckajU5JGwwJGgw\" target=\"_blank\" rel=\"noopener nofollow\" class=\"external\">Get the exclusive paper.<\/a><\/h4>\n<blockquote>\n<div data-hs-cos-general-type=\"widget\" data-hs-cos-type=\"inline_text\" data-hs-cos-field=\"section_heading\">This article was originally published on the <a href=\"https:\/\/blog.dataiku.com\/\" target=\"_blank\" rel=\"noopener nofollow\" class=\"external\">Dataiku blog<\/a>.<\/div>\n<\/blockquote>\n<div data-hs-cos-general-type=\"widget\" data-hs-cos-type=\"inline_text\" data-hs-cos-field=\"section_heading\"><\/div>\n","protected":false},"excerpt":{"rendered":"<p>This article was written by Kevin Petrie, VP of Research at\u00a0BARC, a global research and consulting firm focused on data and analytics.\u00a0 Picture a chatbot that gives erroneous tax advice, insults a customer, or refuses to issue a justified refund, and you start to appreciate the risks of agentic AI. This blog, the first in [&hellip;]<\/p>\n","protected":false},"author":1334,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[96],"tags":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>The Risks and Governance Requirements of Agentic AI<\/title>\n<meta name=\"description\" content=\"This article was written by Kevin Petrie, VP of Research at\u00a0BARC, a global research and consulting firm focused on data and analytics.\u00a0\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Risks and Governance Requirements of Agentic AI\" \/>\n<meta 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