Presented by Splunk
Every day, organizations learn things that their AI systems could never use.
A security analyst corrects an AI-generated investigation. A network engineer identifies the root cause of recurring outages. An observational team discovers that a pattern of latency, logs, and infrastructure changes predicts a degradation in service. A customer operations team learns what signals indicate growth is likely.
Every moment contains valuable organizational knowledge. But in most enterprises, that knowledge disappears in tickets, dashboards, chat threads, post-event reviews, and in the minds of individual experts. It may help solve the immediate problem, but it rarely becomes part of a reusable system that improves AI-powered decisions in the future.
This is the next challenge for the agentic enterprise.
The future will not be defined solely by who has the most capable models or the most autonomous agents. Many organizations will have access to the same Frontier model. Many will deploy agents in 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.
Agents allow enterprise AI systems to learn from them
The AI conversation has been dominated by model capability: larger context windows, better reasoning, faster inference, stronger tool use, and more sophisticated agentic behavior.
Those progress matter. But in the enterprise, a model is only part of the system.
A model does not automatically know how a specific organization operates. It naturally doesn’t know which corrective step resolved last month’s outage, which analyst fix improved threat detection, which network signal preceded the service disruption, or which internal policy should override an otherwise laudable recommendation.
That knowledge belongs to the enterprise.
To improve agentic systems, organizations need a way to capture that knowledge and make it reusable. In many cases, this does not require replacing the model. This requires changing the ecosystem around the model: the knowledge base, retrieval layer, prompts, policies, guardrails, routing logic, and workflows that shape the behavior of agents.
The model may remain the same. The learning system around it becomes smart.
Feedback loops turn every outcome into a teachable moment for agents
Each agentic workflow creates signals.
An agent receives a request. It retrieves context, reasons through possible actions, calls tools and generates answers. The human being accepts, rejects or modifies that answer. Downstream systems know whether the action worked or not.
That whole series is valuable.
AI observability gives organizations visibility into what happened: instantiation, response, logic paths, tool calls, data sources, intermediate steps, failure modes, and outcomes. Without that visibility, organizations can’t understand why an agent behaved the way it did, let alone improve it.
But observation alone is not enough.
The bigger opportunity is to transform observed behavior into institutional knowledge. A trace should not only help a developer and operators debug an agent. This should help the enterprise understand what the agent learned, what improvements the human made, what the outcome was, and what should be changed before the next similar incident.
This is a shift from monitoring AI to teaching AI.
In the agentic enterprise, feedback loops link action to outcome, outcome to knowledge, and knowledge to future action.
A learning system in practice on security, observation and networks
Consider a service experiencing intermittent degradation.
An observation agent detects abnormal latency and error rates. A network agent identifies packet loss on a specific path. A security agent notices that the same time window contains suspicious authentication behavior and unusual traffic from a previously unseen source.
Individually, each agent has only a partial view. Together, they create a rich operational picture.
The first time this phenomenon occurs, human experts may need to intervene. A network engineer confirmed that the packet loss was caused by a incorrectly configured routing change. A security analyst determined that the suspicious traffic was not an attack, but a side effect of a misrouted internal service. An SRE links network events to application degradation.
That resolution includes knowledge that the organization must not relearn.
A mature agentic learning system will capture traces, human corrections, topology context, safety findings, observation signals, and final remediation steps. This will preserve the relationship between those signals: latency patterns, network paths, detection behavior, routing changes and treatments.
The next time a similar pattern appears, the agents will not start from scratch. They can retrieve the past case, compare the current conditions, recommend proven diagnostic paths and move forward with better context.
There was no need to retrain the underlying marginal model.
Enterprise learned.
Architecture of the Learning Agentic Enterprise
A learning-oriented agentic enterprise needs more than a model or chatbot. It requires an architecture that can capture experience, transform it into usable knowledge, connect that knowledge to the operational context, and control how it changes future agent behavior.
Memory Preserves what happened: what the agent saw, what it did, where humans intervened, and what the consequences were.
knowledge base Turn that experience into reusable guidance including playbooks, examples, policies, procedures, and evidence.
A data fabric Connects the operating environment. Signal agents must be live across logs, metrics, traces, tickets, identity systems, security tools, network telemetry, collaboration platforms, and business applications. A data fabric makes those signals discoverable, correlated, controlled, and usable in context.
AI observability It describes how agents behave by capturing signals, tool calls, intermediate steps, reactions, feedback, and outcomes. That visibility helps organizations understand where agents succeed, where they fail, and what needs to be improved.
control plane It controls how learning becomes change: what knowledge is promoted, what signs or policies are updated, which agents can use new information, what approvals are required, and how changes are audited.
Together, these capabilities allow AI systems to improve over time in a controlled, reliable manner that allows the enterprise to learn from its own operations.
Organizations that learn fastest will win
The next era of AI will not be won by models alone. It will be won by organizations that can capture what they learn from each workflow, expert improvement, incident, investigation and outcome.
The most advanced agentic enterprises won’t just deploy more agents. They will create systems that allow each agent to benefit from the collective knowledge of the organization.
This means connecting operational data through the data fabric. This means observing the agent’s behavior in depth to understand it. This means preserving experience in memory and institutionalizing it in knowledge bases. This means using a control plane to control how learning changes the agent’s behavior.
The future of AI is not one of an autonomous agent working alone. It is an ecosystem of agents, humans, data, and controls that learns over time.
Organizations building that ecosystem will create AI systems that get better with every interaction. Not because the model is constantly changing, but because the enterprise itself is becoming more intelligent.
Learn more about how Cisco Data Fabric powered by Splunk Platform Accelerating agentive work.
Hao Yang is vice president of AI at Splunk, a Cisco company.
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