{"id":4936,"date":"2025-04-08T16:17:49","date_gmt":"2025-04-08T16:17:49","guid":{"rendered":"https:\/\/palmer-consulting.com\/agentic-ai-towards-a-new-era-of-autonomous-artificial-intelligence\/"},"modified":"2025-04-08T16:17:49","modified_gmt":"2025-04-08T16:17:49","slug":"agentic-ai-towards-a-new-era-of-autonomous-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/palmer-consulting.com\/en\/agentic-ai-towards-a-new-era-of-autonomous-artificial-intelligence\/","title":{"rendered":"Agentic AI: towards a new era of autonomous Artificial Intelligence?"},"content":{"rendered":"

Agentic AI: towards a new era of autonomous artificial intelligence? <\/strong><\/h2>\n

<\/strong><\/p>\n

Artificial intelligence is undergoing a major strategic shift with the emergence of agentic AIs, as presented in our previous article (ref. Generative AI, RPA, Analytic and Agentic AI: what are the differences and how to choose the right approach? | Palmer)<\/a>. Unlike traditional or generative AI, these new agents are capable of acting autonomously to achieve a goal, without continuous human intervention. For companies, this represents unprecedented potential for transformation, particularly in support functions, operations and information systems. <\/p>\n

But what are we actually talking about? And what are the implications for organizations in 2025? <\/p>\n

AIs capable of thinking and acting<\/strong><\/h3>\n

Agentic AI is based on a different architecture. It no longer simply responds to an order: it takes the initiative, draws up an action plan, mobilizes the necessary tools (e-mail, APIs, databases, etc.), carries out tasks sequentially, and then evaluates itself. Agentic AI relies on three key cognitive functions: perception (context and data analysis), reasoning (sequential planning, simulations, decision-making), and action (autonomous execution via business systems). We are thus moving from assisted AI to executing AI. <\/p>\n

Companies such as OpenAI (with its autonomous GPT agents) and Adept (with Action Transformer) are developing solutions capable of surfing the web, filling in forms, generating summary documents or orchestrating complex workflows in complete autonomy. At OpenAI, experiments have demonstrated the ability of autonomous agents to manage multi-step tasks, such as booking tickets, managing diaries, or automatically generating administrative documents from incoming e-mails. These agents interact with web interfaces, make context-sensitive decisions, and can reformulate their plans in the event of failure.
> At Adept, the Action Transformer model has been designed to interpret human actions on conventional software interfaces (Excel, CRM, browser) and reproduce them autonomously, enabling the agent to perform repetitive, context-sensitive tasks, without complex API integration.<\/p>\n

In a business environment, this could mean entrusting AI with the complete management of HR onboarding, the automatic recognition and prioritization of IT tickets, or the consolidation of heterogeneous data to produce summaries or dashboards without human intervention.<\/p>\n

High-impact use cases in business functions \ufe0f<\/strong><\/h3>\n

The first deployments of agentic AI are already being seen in key corporate functions, with measurable gains in efficiency. In customer support, these agents are capable of handling complex requests autonomously, navigating internal tools, querying databases and interacting directly with users. <\/p>\n

In finance and accounting, they play a proactive role in automating monthly closings, checking compliance and generating reports, with a heightened level of accuracy. This is the case, for example, at Orange, where the ClariFibre agent enables technical teams to reduce the time taken to analyze support tickets relating to connection problems by 75%, by automatically synthesizing the data and suggesting possible resolutions. <\/p>\n

In the banking sector, too, some players are experimenting with agents capable of autonomously managing a credit file from start to finish: collection of documents, automatic verification of supporting documents, creditworthiness analysis according to risk rules, generation of underwriting documents, and pre-validation submitted to an account manager.
>This type of agent reduces processing times, standardizes decisions in line with internal policies, and increases capacity to handle requests without increasing staffing levels.<\/p>\n

On the purchasing and supply chain side, agentic AIs can trigger orders, adjust stock levels and even recommend the best suppliers based on real-time data. Finally, in IT teams, these autonomous intelligences enable incident detection, alert activation and automated documentation of actions, while integrating with existing information systems. These uses, once considered futuristic, are now becoming concrete levers for improving performance. <\/p>\n

 <\/p>\n

Technological, human and ethical challenges<\/strong><\/h3>\n

But this progress is not without its challenges. The first is technological: getting these agents to interact with the existing application ecosystem (ERP, CRM, business APIs, etc.) requires open, well-governed architectures. This implies the ability to standardize interfaces, document processes, and set up logging and control mechanisms for actions carried out by agents. Technical teams also need to provide error handling scenarios, fallback plans in the event of an incident, and monitoring systems capable of tracking agent behavior in real time. This infrastructure must also guarantee scalability and evolutivity, so that agents can be multiplied without increasing management complexity. <\/p>\n

The second is human: these agents do not replace employees, but shift their role to higher value-added tasks. It is therefore essential to support this transition with training and work on acculturation to these new tools. Adopting agentic AI requires upstream thinking about the evolution of skills, jobs and daily routines. This requires awareness-raising initiatives, modular training courses, and the integration of AI into talent development plans. It is essential to promote the appropriation of agents by business teams, through periods of cohabitation, controlled testing phases, and user feedback loops. <\/p>\n

AI under control: safety, ethics and governance at the heart of projects<\/h3>\n

Last but not least, questions of security, explicability of agent decisions and supervision remain fundamental. It’s not a question of delegating control to a black box, but of establishing effective collaboration between humans and agents. Organizations must equip themselves with mechanisms for real-time supervision, traceability of actions, and a posteriori human control. The explicability of decisions is essential to guarantee compliance (RGPD, sector-specific regulations) and to be able to justify the actions taken by AI in the event of litigation. This requires the implementation of execution logs, confidence scores, alert thresholds, and manual validation mechanisms when risks are high. Finally, AI governance committees must be involved right from the project scoping stage, to assess ethical, social and organizational impacts. <\/p>\n

<\/strong><\/p>\n

2025: the pivotal year for agentic AIs<\/strong><\/h3>\n

More and more business departments are launching POCs around agentic AI, particularly in HR, legal, operations, IT and finance functions, with cases such as automated handling of support requests, litigation management, or multi-entity accounting consolidation. The ramp-up of these projects is expected as early as 2025, driven by the growing maturity of technologies, the availability of data, and pressure on operational efficiency. <\/p>\n

The challenge is no longer merely technological, but organizational, cultural and even strategic: how to rethink the distribution of roles, process management and governance models, in an environment now enriched by these new intelligent assistants. These autonomous agents are redrawing the contours of work, modifying interactions between teams and systems, and inviting us to revisit the logics of coordination and responsibility. <\/p>\n

At PALMER, we support our customers in this transformation: analysis of use cases, scoping of opportunities, choice of architectures, change management. Agentic AI is no longer a vision of the future: it’s a reality that’s taking hold, and redefining the boundary between automation and autonomy. \ud83d\ude80<\/p>\n

Want to explore the potential of agentic AI for your businesses? Get in touch with our teams of experts<\/a>. <\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

Agentic AI: towards a new era of autonomous artificial intelligence? Artificial intelligence is undergoing a major strategic shift with the emergence of agentic AIs, as presented in our previous article (ref. Generative AI, RPA, Analytic and Agentic AI: what are the differences and how to choose the right approach? | Palmer). Unlike traditional or generative […]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4936","post","type-post","status-publish","format-standard","hentry","category-non-classe"],"acf":[],"yoast_head":"\nAgentic AI: towards a new era of autonomous Artificial Intelligence? | Palmer<\/title>\n<meta name=\"description\" content=\"Agentic AI marks a turning point in artificial intelligence: autonomous agents, intelligent automation, operational gains...\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/palmer-consulting.com\/en\/agentic-ai-towards-a-new-era-of-autonomous-artificial-intelligence\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Agentic AI: towards a new era of autonomous Artificial Intelligence? 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