Presented by Splunk
Every day, organizations learn things that their AI systems never 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 client operations team learns what signals indicate an escalation is likely.
Every moment contains valuable organizational knowledge. But in most companies, that knowledge disappears in tickets, dashboards, chat threads, post-incident 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 future AI-driven decisions.
That is the next challenge for the agency company.
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 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 agent company is not just a company that uses AI. It is a company that learns through AI.
Agent companies allow AI systems to learn from them
The conversation about AI has been dominated by model capability: larger context windows, better reasoning, faster inference, more robust use of tools, and more sophisticated agent behavior.
Those advances matter. But in business, a model is only part of the system.
A model does not automatically know how a specific organization operates. You don’t inherently know what remediation step resolved last month’s outage, what analyst fix improved a threat investigation, what network signal preceded a service outage, or what internal policy should override an otherwise plausible recommendation.
That knowledge belongs to the company.
For agent systems to improve, organizations need a way to capture that knowledge and make it reusable. In many cases, that doesn’t require changing the model itself. It requires changing the ecosystem around the model: the knowledge base, the recovery layer, the prompts, the policies, the guardrails, the routing logic, and the workflows that shape agent behavior.
The model can remain the same. The learning system around you becomes smarter.
Feedback loops turn every result into a teachable moment for agents
Each agent workflow creates signals.
An agent receives a request. Retrieves context, reasons through possible actions, calls tools and generates responses. A human accepts, rejects or modifies that response. Subsequent systems reveal whether the action worked.
That whole chain is valuable.
AI observability gives organizations visibility into what happened: the warning, the response, the reasoning path, tool calls, data sources, intermediate steps, failure modes, and results. Without that visibility, organizations can’t understand why an agent behaved the way they did, much less improve it.
But observability alone is not enough.
The greatest opportunity is to convert observed behavior into institutional knowledge. A trace should not only help the developer and operators debug an agent. It should help the business understand what the agent learned, what the human corrected, what outcome followed, and what should change before the next similar event.
That’s the shift from monitoring AI to teaching it.
In the agentic enterprise, feedback loops connect action to outcome, outcome to knowledge, and knowledge to future action.
A learning-by-doing system about security, observability, and the network.
Consider a service that experiences intermittent degradation.
An observability agent detects unusual error rates and latency. A network agent identifies packet loss on a specific path. A security agent notices that the same time window includes suspicious authentication behavior and unusual traffic from a previously unseen source.
Individually, each agent has only a partial view. Together, they create a richer operational landscape.
The first time this incident occurs, human experts may need to intervene. A network engineer confirms that the packet loss was due to a misconfigured route change. A security analyst determines that the suspicious traffic was not an attack, but a side effect of a misrouted internal service. An SRE connects the network event to the application degradation.
That resolution contains knowledge that the organization should not have to relearn.
A mature agent learning system would capture traces, human corrections, topological context, security findings, observability signals, and final remediation steps. It would preserve the relationship between those signals: latency pattern, network path, identity behavior, path change, and correctness.
The next time a similar pattern appears, agents won’t start from scratch. They could retrieve the previous case, compare current conditions, recommend the tested diagnostic path, and escalate with better context.
There was no need to retrain the underlying boundary model.
The company learned.
The architecture of the learning agent enterprise.
A learning-oriented agency company needs more than a model or a chatbot. You need an architecture that can capture experience, turn it into usable knowledge, connect that knowledge to operational context, and govern how future agent behavior changes.
Memory preserves what happened: what the agent saw, what he did, where humans intervened, and what results followed.
Knowledge bases turn that experience into a reusable guide, including guides, examples, policies, procedures and evidence.
TO data fabric connects the operating environment. The signals agents need are conveyed through logs, metrics, traces, tickets, identity systems, security tools, network telemetry, collaboration platforms, and business applications. A data structure makes those signals detectable, correlated, governed, and usable in context.
AI Observability Explains how agents behave when capturing prompts, tool calls, intermediate steps, responses, comments, and results. That visibility helps organizations understand where agents succeed, where they fail, and what should improve.
He control plane It governs how learning becomes change: what knowledge is promoted, what guidance or policies are updated, what 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 and reliable way that allows the company to learn from its own operations.
Organizations that learn the fastest will win
The next era of AI will not be won by models alone. Organizations that can capture what they learn from each workflow, expert remediation, incident, investigation, and outcome will win.
The most advanced agent companies will not simply deploy more agents. They will build systems that allow each agent to benefit from the collective knowledge of the organization.
That means connecting operational data through a data structure. It means observing agent behavior deeply enough to understand it. It means preserving experience in memory and institutionalizing it in knowledge bases. It means using a control plane to govern how learning changes the behavior of agents.
The future of AI is not an autonomous agent acting alone. It is an ecosystem of agents, humans, data and controls that learns over time.
Organizations that build that ecosystem will create artificial intelligence systems that improve with every interaction. Not because the model is constantly changing, but because the company itself is getting smarter.
Learn more about how Cisco Data Fabric powered by the Splunk platform is accelerating agent operations.
Hao Yang is vice president of AI at Splunk, a Cisco company.
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