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The Agentic AI Framework: Mastering Autonomy for Enterprise Operations

  • Writer: Sibylle Moller-Sherwood
    Sibylle Moller-Sherwood
  • Oct 28
  • 2 min read
A conceptual digital overlay of the term "Agentic AI" projected onto a dark, deep blue background, likely representing a secure cloud environment or a data centre. The foreground is blurred, showing a person's hands typing on a laptop keyboard, establishing a human-in-the-loop context. The central projection features a stylised graphic of a robot or chatbot head with antennae, labelled "Agentic AI", connected to a human head profile containing complex circuit board imagery, symbolising the fusion of autonomous intelligence with human expertise and guidance.

When planning agentic Artificial Intelligence initiatives, it is wise to establish a framework. This structure is essential for assessing where AI agents are most likely to bring improvements, how to evaluate AI readiness across the organisation, how to manage and orchestrate the actions of AI agents, and, crucially, how to implement governance for responsible oversight.

The principles of agentic AI are already transforming three core enterprise functions: IT Operations and Cybersecurity, Data based Recommendations, and Software Development.


Key Applications of an Agentic AI Framework


1. IT Operations and Cybersecurity


AI agents offer a step change in IT automation, moving beyond simple scripting to full autonomous action based on policy.

  • Automating best practices in IT operations and cybersecurity.

  • Automatically remediating issues and enabling further automation with automated workflows.

  • Automating compliance checks with compliance as code recommendations, reducing audit risk and regulatory exposure, and creating audit ready artefacts.

  • Generating zero configuration dashboards, alerting, troubleshooting, and remediation.

  • Giving applications the precise resources they need when they need them, including optimising GPU workloads without sacrificing performance.

  • Identifying resource congestion and cost overruns across hybrid cloud environments.


2. Data Based Recommendations


Agentic AI transforms data analysis from a passive alerting system into an active, preventative measure by correlating complex signals and recommending action.

  • Scoring and correlating data to identify issues and implement actionable recommendations.

  • Consolidating scattered data so it can provide insights and generate actions to prevent problems, not just provide alerts.

  • Providing actionable recommendations, thresholds, and remediation plans using agentic AI.

  • Displaying evidence in visualisations and natural language interactions so that users can validate findings, explore context, and move to remediation.


3. Software Development


From continuous integration to deployment and monitoring, agents provide full stack visibility and automation.

  • Scanning for and detecting problematic code that can result in security or resilience issues.

  • Automating full stack application visibility across the entire monitoring life cycle, including real time change detection, mapping, tracing, and profiling.

  • Enabling automatic and continuous discovery, deployment, configuration, and dependency mapping.


The Agentic AI Advantage: Eliminating Context Switching


Traditionally, these tasks would require hopping from screen to screen to review Central Processing Unit, memory, and disk metrics, checking log files to gather more data, and drilling down in the search for the root cause. Instead, agents can automate all of this and correlate actions between different agents to recommend actions, or if they are fully trusted, take specific actions based on preset policy.


Core Benefits of Agentic AI


  • Increased performance.

  • Significant cost savings.

  • Implementation of Enterprise rules at scale.

  • Enhanced compliance and reduced audit risk.

  • Adjustable human in or out the loop based on the level of comfort and trust within the organisation.


How To Start Small, Scale Responsibly


There is an ongoing race to use AI to achieve enterprise glory. Enterprises rushing headlong into AI may end up making mistakes that reduce confidence in agentic AI across the board. The smart approach is to start small, preferably in IT operations use cases, and scale responsibly within IT, before unleashing automated agents across the wider business. This establishes trust and refined governance models before expanding to more mission critical or customer facing tasks.

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