Agentic AI: Transforming Enterprise Automation and Digital Resilience

How Agentic AI transforms enterprise automation

Agentic AI enables secure, adaptive automation within enterprise constraints There's a lot of noise in enterprise AI right now. Under mounting pressure to deliver faster, safer digital services, businesses are turning to the next evolution in automation: Agentic AI. No, this isn't bolting on a chatbot and calling it digital transformation. AI agents are built to understand your organization, operating within your domain constraints with real autonomy. These agents operate inside your business, using your data to automate decisions, adapt to real-world problems in milliseconds, and embed themselves directly into operational workflows. They blend the general reasoning power of today's large language models with domain- specific intelligence grounded in company data. That might be clinical records, compliance frameworks, or engineering logs - whatever your business runs on. The result? Systems that take action: surfacing insights, automating tasks, and adapting based on your company policies and workflows. Demand for automation is growing, as are expectations around compliance, transparency, and data governance, especially in Europe. Agentic AI offers a response to both: scalable intelligence, designed to work inside complex regulatory frameworks. That matters in sectors like healthcare, manufacturing, and financial services, where data security, explainability, and reliability aren't negotiable. These aren't markets where "good enough" is acceptable. Customers simply can not tolerate hallucinated responses or unreliable systems where their data hits the public domain. Agentic AI is safer. Not because it's slower or more cautious, but because it's built for the environment it's deployed into. Agentic systems rely on a layered approach, with different types of agents operating across an organization: Key to all of this is the use of custom vector databases. Vector databases enable AI agents to fetch relevant, security-controlled context from sensitive data without actually exposing that data in its original form to the agent. This is a game-changer for regulated industries. Rather than relying on generic training data from the public internet, this draws directly from the institutional knowledge inside your firewalls. That means better accuracy, stronger compliance, and fewer surprises. It also means outputs that reflect your standards, rather than what's statistically likely. Agentic systems are already transforming highly regulated sectors in Europe. In healthcare, they reduce administrative overheads, improve triage, and accelerate innovation while protecting patient privacy. In manufacturing, they're powering predictive maintenance, supply chain optimization, and real-time field service. Within finance, these agents enhance fraud detection, refine compliance, and provide hyper- personalized services. Agentic AI adoption is particularly strong in regions with tighter data controls - namely France, Germany, and the Nordics - because these systems respect the boundaries enterprises are required to operate within. These systems increasingly rely on serverless inference, which allows businesses to scale their AI infrastructure without wedding themselves to their maximum theoretical usage. That's critical in Europe, where innovation budgets are often tight, and sovereign infrastructure matters. Agentic AI is being built to meet those regulatory requirements from day one. Yes, Europe's regulatory environment slows things down. But that friction forces better thinking. It pushes enterprises to build with trust, accountability, and explainability. Creating market conditions where sustainable AI can thrive. GDPR, the EU AI Act, NIS2 and other regulatory frameworks define the standards by which responsible AI can scale. As US start-ups chase MVPs and launch before the proper guardrails are in place, European enterprises may end up with AI that's more compliant and generally more effective in the long term. Agentic AI marks a turning point in how businesses interact with their data and workflows. It moves beyond static automation to deliver systems that act, learn, and improve within the constraints enterprises define. This is not a plug-and-play future. It's a future that demands thoughtful design, domain- specific strategy, and an unflinching focus on outcomes. The rewards will be sustainable and significant for the organizations that build smart and scale responsibly. The hype in off-the-shelf, plug-and-play solutions will fade. Agentic AI infrastructure is built for the latest ways of working. Enterprises that invest now and build with intent will lead in the next stage for what's next. We've featured the best AI writer.

Agentic AI and the future state of enterprise security and observability

With its ability to reason, adapt, and take action autonomously at machine speed, agentic AI has the power and potential to dramatically change how enterprises maintain their digital resilience. It also redefines how they secure and deliver reliable performance for their digital ecosystems, where data pattern recognition and decision-making need to happen in real time and at machine speed. With agentic AI, companies get the benefits of a conversational analysis experience from LLM reasoning and adaptation plus the automation of task execution from the agentic framework. Together these shift IT teams from reactive fire-fighting mode to proactive planning mode. Here's how. The promise of agentic AI for digital resilience 1. Pinpoint root-cause (almost) instantly Agentic AI can cross siloed application boundaries to bring data insights together for more complete visibility. For example, agentic AI can use LLMs to analyze logs, metrics, events, and trace data; call upon different monitoring systems in your ecosystem; apply reasoning to the data; and recommend or take actions to remediate. In minutes, the agentic AI can complete what used to take a site reliability engineer hours to pinpoint and troubleshoot potential issues. For security threats, agentic AI can analyze data streams to identify threats in real-time, including zero-day exploits or insider threats; automate multi-step investigation workflows from multiple security applications; and execute appropriate remediation responses to contain the threat and prevent lateral movements. Investigations that took the SOC analyst hours can now be done in minutes. 2. Preempt disruptions and downtime The power of agentic AI can prevent incidents and disruptions in more proactive ways. By studying historical data and current trends, agentic AI can forecast vulnerabilities -- such as unpatched software or weak encryption -- before they are exploited. It can detect subtle user behavior anomalies and flag suspicious activity before damage occurs. It can also analyze real-time data streams -- such as logs, metrics, and traces from multiple sources -- to provide a comprehensive view of system health and detect issues such as resource bottlenecks or latency spikes before they escalate. In short, the speed and scale at which root cause analysis can be done by agentic AI means more alerts can be analyzed -- and resolved -- before they become bigger issues. 3. Make better decisions with contextual, real-time insights Agentic AI has the ability to process new information in its environment and adapt its reasoning and course of action in real time. Contextual data refers to the rich, multidimensional information about users, devices, applications, and environments -- such as user behavior patterns, device states, network conditions, and data flows. Agentic AI can process contextual data and patterns to make rapid, informed decisions to detect and remediate incidents and optimize operational performance. 4. Upskill and optimize the workforce With agentic AI, you get both a natural language interface and automated task execution through the agency framework. Workers at all levels can use it to upskill their knowledge across domains, whether identifying security threat vectors or navigating complex application stacks in observability. Humans are ultimately responsible for managing AI agents. As more AI agents augment the work of analysts and managers, organizations will need technical analysts to learn new skills to manage agents and incorporate them into enterprise workflows (human-on-the-loop). Automating the full detection-investigation-response workflow is appealing -- but as workflows grow more complex, with multiple agents and steps, so does the risk of compounding errors and hallucinations. Inserting humans at critical points in the automated analysis workflow (human-in-the-loop) enables you to ensure the agent(s) is on the right track, provide real-time feedback and use reinforcement learning to improve model performance. 2. Avoid hallucinations with domain-specific, specialized agents There's a real cost to model hallucinations. This McKinsey AI Report estimates $67.4B was lost globally due to hallucinated AI output. OpenAI's o3 and o4-mini were shown to hallucinate between 51% and 79% of the time on reasoning tasks. Narrowing the agent's purpose -- combined with fine-tuning and augmenting the model with RAG using domain-specific data -- improves output accuracy. Specialized agents for areas like security and observability and even more targeted ones for detection, investigation, and response will deliver greater precision. These agents will also benefit from lower inference compute costs and latency compared to larger general-purpose LLMs. 3. Ensure seamless integration and compatibility in agentic ecosystems Integrating agentic AI into your IT environment requires rethinking of data flows, processes, and security protocols, and adapting user interaction models to maintain system integrity while harnessing AI's potential. Three emerging protocols will help accelerate this: 4. Agent access control and data privacy governance The volume and speed for agent access management will far exceed the traditional human access management. It's critical to define clear access levels for autonomous agents that maintain compliance, and establish a plan of record for audits and governance. The goal: boost operational efficiency without introducing risk so AI acts as a secure, augmentative force within the IT ecosystem. Splunk AI for digital resilience Splunk, a Cisco company, is redefining enterprise security and observability with AI at its core to accelerate insights, automate critical workflows, and boost analyst productivity. Building on a long history of machine learning capabilities, Splunk is embedding generative and agentic AI across its industry-leading security and observability solutions. With a unified data platform for operational data, Splunk is building an AI-ready platform to turbocharge enterprise security and observability outcomes. Visit www.splunk.com/ai to learn more. Cory Minton is Field CTO - AI at Splunk. Sancha Norris is Product Marketing Leader at Splunk AI.