{"id":34572,"date":"2025-04-23T11:04:36","date_gmt":"2025-04-23T10:04:36","guid":{"rendered":"https:\/\/www.dataiq.global\/?post_type=article&p=34572"},"modified":"2025-04-23T11:25:40","modified_gmt":"2025-04-23T10:25:40","slug":"best-practices-implementing-agentic-ai","status":"publish","type":"article","link":"https:\/\/www.dataiq.global\/devstage\/articles\/best-practices-implementing-agentic-ai\/","title":{"rendered":"Best practices for implementing agentic AI"},"content":{"rendered":"

WNS Analytics explains: \u201cAs the opportunity for analytics and AI interventions grow for organisations, the interdependencies of AI and human expertise is taking a dramatic leap. With hyperspecialised AI agents and execution ability of AI systems, the dynamics of AI and human collaboration is changing. Agentic AI will enable organisations to derive value out of complex processes because it understands the business goal and an ensemble of hyperspecialised AI agents are orchestrated by a supervisor agent to solve the business problem. <\/span>\u00a0<\/span><\/p>\n

At <\/span>WNS Analytics<\/span><\/a>, we focus on driving value for our customers keeping AI + HI at the core. Agentic AI solutions are further enriching the AI + HI handshake and can drive business value for our clients in enhanced ways.\u201d<\/span>\u00a0<\/span><\/p>\n

What are some of the key best practices data leaders and their teams need to follow to ensure success with a new agentic AI operation?<\/span>\u00a0<\/span><\/p>\n

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High impact use cases<\/span><\/b>\u00a0<\/span><\/h4>\n

Proving the ROI for agentic AI means data leaders need to have some quick, high-stake use cases in mind to demonstrate success. Some examples can include:<\/span>\u00a0<\/span><\/p>\n

    \n
  • Customer service automation:<\/span><\/b> Autonomous agents that can handle complex customer queries and adapt to new customer scenarios.<\/span>\u00a0<\/span><\/li>\n
  • Supply chain optimisation: <\/span><\/b>Real-time decision-making to improve logistics and inventory management.<\/span>\u00a0<\/span><\/li>\n
  • Fraud detection and prevention:<\/span><\/b> Identifying and acting on suspicious patterns autonomously with a high degree of accuracy.<\/span>\u00a0<\/span><\/li>\n
  • Financial trading: <\/span><\/b>AI-driven trading strategies that adjust to market conditions instantly.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n

     <\/p>\n

    These agentic AI systems thrive in dynamic environments requiring adaptive, goal-driven behaviour led by data teams.\u00a0<\/span>\u00a0<\/span><\/p>\n

    For data leaders, agentic AI unlocks strategic value by automating complex, high-stakes decisions at scale. Agentic AI can rapidly adapt to changing environments and learn from outcomes to ensure that an organisation stays competitive in fast-moving markets and can manoeuvre into new opportunities seamlessly.\u00a0<\/span>\u00a0<\/span><\/p>\n

    \u00a0<\/span><\/p>\n

    Aligning business goals <\/span><\/b>\u00a0<\/span><\/h4>\n

    Data leaders must ensure that AI agents are properly aligned with business goals and relevant stakeholders, governed effectively, and continuously improved to maximise ROI and minimise risk.<\/span>\u00a0<\/span><\/p>\n

    By engaging stakeholders early in the process, perhaps through demonstrations of areas where autonomy and real-time decision-making will drive value, data leaders can ensure the solutions they are working on will achieve the intended goals. For example, data leaders and their business leaders should collaborate to define measurable and easily understood business outcomes, such as cost reduction and improved customer satisfaction.<\/span>\u00a0<\/span><\/p>\n

    Alignment with the business objectives ensures that the direction of development is narrowed and clear for different departments. This will also bolster support for other data-driven projects and improve data culture from the top down.\u00a0<\/span>\u00a0<\/span><\/p>\n

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    Goal-oriented designs<\/span><\/b>\u00a0<\/span><\/h4>\n

    Goal-oriented design ensures AI agents act with purpose and adaptability, driving business value. Building AI systems that are explicitly designed to pursue defined business objectives autonomously is the key to goal-oriented designs for agentic AI. This can be achieved by aligning goals with measurable business outcomes, as well as designing the AI to evaluate multiple paths toward achieving the desired goal.<\/span>\u00a0<\/span><\/p>\n

    It is imperative that the AI system can self-correct based on performance data and has a set of constraints and boundaries to limit its scope and avoid unintended consequences. This can be checked by having data leaders continuously assess how well the AI meets business targets and being adjusted accordingly.<\/span>\u00a0<\/span><\/p>\n

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    Integration with existing systems<\/span><\/b>\u00a0<\/span><\/h4>\n

    It is difficult to integrate new agentic AI solutions with existing systems, but it can be done. To achieve this, data leaders should evaluate existing data pipelines, APIs, and infrastructure to ensure alignment with AI agent requirements. This can then be utilised with modular agentic AI components that can plug into existing systems, avoiding major overhauls and costs.\u00a0<\/span>\u00a0<\/span><\/p>\n

    Middleware or integration layers can be used to connect AI agents with legacy systems without causing too much disruption, but they require strict evaluation to ensure that they do not hinder further development once agentic AI has been established.\u00a0<\/span>\u00a0<\/span><\/p>\n

    The introduction of agentic AI should happen incrementally to minimise disruption and validate performance. Data leaders can set up real-time data exchange between AI and existing platforms to help monitor this progression and demonstrate the incremental improvements in each step to enhance existing operations.<\/span>\u00a0<\/span><\/p>\n

    \u00a0<\/span><\/p>\n

    Monitoring outcomes<\/span><\/b>\u00a0<\/span><\/h4>\n

    To track and improve agentic AI performance, data leaders and their teams should establish a structured feedback loop:<\/span>\u00a0<\/span><\/p>\n

      \n
    1. Define KPIs:<\/span><\/b> Set business-aligned metrics (cost savings, accuracy, customer satisfaction).<\/span>\u00a0<\/span><\/li>\n
    2. Real-time monitoring:<\/span><\/b> Create and mandate the use of dashboards and automated alerts to detect deviations or underperformance.<\/span>\u00a0<\/span><\/li>\n
    3. Data-driven diagnostics:<\/span><\/b> Keep a human-in-the-loop approach to analysing errors and decision patterns to identify root causes, particularly in the early stages of implementation. Ensure there is a human review for critical decisions and edge cases.<\/span>\u00a0<\/span><\/li>\n
    4. Adaptive learning:<\/span><\/b> Implement reinforcement learning and manual tuning to refine agent behaviour.<\/span>\u00a0<\/span><\/li>\n
    5. Iterative improvement: <\/span><\/b>Regularly and systematically retrain and update models based on feedback and changing business conditions.<\/span>\u00a0<\/span><\/li>\n<\/ol>\n

      \u00a0<\/span><\/p>\n

      Maintaining suitable human oversight<\/span><\/b>\u00a0<\/span><\/h4>\n

      To maintain suitable human oversight when implementing agentic AI, data leaders should:<\/span>\u00a0<\/span><\/p>\n

        \n
      1. Establish clear escalation protocols:<\/span><\/b> Data leaders must define when and how AI-driven decisions should be escalated to humans, particularly for high-stakes outcomes.<\/span>\u00a0<\/span><\/li>\n
      2. Implement real-time monitoring:<\/span><\/b> Mandate the use of dashboards to provide visibility into agent actions and decisions.<\/span>\u00a0<\/span><\/li>\n
      3. Introduce human-in-the-loop systems:<\/span><\/b> Ensure that sensitive or complex decisions require human approval or review. This can be expanded to cover more basic decisions when implementation is beginning.\u00a0<\/span>\u00a0<\/span><\/li>\n
      4. Audit and test regularly:<\/span><\/b> Conduct regular audits to identify biases, inconsistencies, and unintended behaviours.<\/span>\u00a0<\/span><\/li>\n
      5. Define accountability: <\/span><\/b>Assign responsibility for reviewing agent performance and addressing failures.<\/span>\u00a0<\/span><\/li>\n
      6. Feedback integration:<\/span><\/b> Capture human feedback to refine agent decision-making and improve alignment over time.<\/span>\u00a0<\/span><\/li>\n<\/ol>\n

         <\/p>\n

        Balancing automation and human oversight ensures that AI remains aligned with business objectives with minimal risk and optimal review.<\/span>\u00a0<\/span><\/p>\n

        With these steps, data leaders will be able to successfully and swiftly integrate agentic AI into their organisations with ease.\u00a0<\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

        Agentic AI is a leap forward\u202fin organisational AI adoption. Here we discuss the best practices to harness its full potential.<\/p>\n","protected":false},"author":19,"featured_media":34575,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_searchwp_excluded":"","footnotes":""},"categories":[129,398],"tags":[1369,1370,1051,218,917,1371,224,1123,1122],"pillar":[197],"class_list":["post-34572","article","type-article","status-publish","format-standard","has-post-thumbnail","hentry","category-editorial","category-public","tag-agentic","tag-agentic-ai","tag-ai-technology","tag-artificial-intelligence","tag-integration","tag-outcomes","tag-technology-and-tools","tag-wns","tag-wns-analytics","pillar-technology"],"acf":[],"publishpress_future_action":{"enabled":false,"date":"2026-05-20 23:39:47","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category","extraData":[]},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/34572","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/types\/article"}],"author":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/comments?post=34572"}],"version-history":[{"count":2,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/34572\/revisions"}],"predecessor-version":[{"id":34574,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/34572\/revisions\/34574"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media\/34575"}],"wp:attachment":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media?parent=34572"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/categories?post=34572"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/tags?post=34572"},{"taxonomy":"pillar","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/pillar?post=34572"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}