Presented by SplunkEvery day, organizations learn things their AI systems never get to use.A security analyst corrects an AI-generated investigation. A network engineer identifies the root cause of a recurring outage. An observability team discovers that a pattern of latency, logs and infrastructure changes predicts service degradation. A customer operations team learns which signals indicate an escalation is likely.Each moment contains valuable organizational knowledge. But in most enterprises, that knowledge disappears into tickets, dashboards, chat threads, post-incident reviews and the minds of individual experts. It may help solve the immediate problem, but it rarely becomes part of a reusable system that improves future AI-driven decisions.That is the next challenge for the agentic enterprise.The future will not be defined simply by who has the most capable model or the most autonomous agents. Many organizations will have access to similar frontier models. Many will deploy agents across security, IT, engineering, customer service, and business operations.The real differentiator will be whether those agents can learn from the organization around them.Not by constantly retraining the underlying model, but by capturing operational experience, converting it into institutional knowledge and making that knowledge available to future agents, workflows, and decisions.The agentic enterprise is not just an enterprise that uses AI. It is an enterprise that learns through AI.Agentic enterprises allow AI systems to learn from themThe AI conversation has been dominated by model capability: larger context windows, better reasoning, faster inference, stronger tool use, and more sophisticated agentic behavior.Those advances matter. But in the enterprise, a model i …
