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Every day, organizations learn things that AI systems could never use.
A security analyst edits an AI-generated investigation. A network engineer determines the root cause of the recurring outage. The monitoring team finds that a pattern of latency, logging and infrastructure changes predicts service degradation. The customer operations team learns which signals indicate the possibility of escalation.
Each moment contains valuable organizational knowledge. But in most businesses, this knowledge is lost in tickets, dashboards, conversation threads, post-incident reviews, and in the minds of individual experts. It can help solve an immediate problem, but rarely becomes part of a reusable system that improves AI-based decisions in the future.
This is the next challenge for the agent enterprise.
The future will not simply be defined by who has the most capable model or the most autonomous agents. Many organizations will have access to similar boundary models. Many will deploy agents across security, IT, engineering, customer service and business operations.
The real differentiator will be the ability for those agents to learn from the organization around them.
Not by constantly reengineering the underlying model, but by capturing operational experience, turning it into institutional knowledge, and making that knowledge available to future agents, workflows, and decisions.
An agent enterprise is not just an enterprise using artificial intelligence. It is an enterprise that learns through AI.
Agent entities allow AI systems to learn from them
Modeling capabilities dominated conversation with AI: larger context windows, better reasoning, faster inference, more powerful tool usage, and more complex agent behavior.
These advances are important. But in the enterprise, the model is only part of the system.
The model does not automatically know how a particular organization works. He essentially doesn’t know which remediation step resolved last month’s outage, which analyst fix improved threat investigation, which network signal preceded the outage, or which internal policy overrode an otherwise reasonable recommendation.
That knowledge belongs to the enterprise.
To improve agent systems, organizations need a way to capture and reuse that knowledge. In many cases, this does not require changing the model itself. This requires changing the ecosystem around the model: the database, search layer, directives, policies, guards, routing logic, and workflows that shape the behavior of agents.
The model may remain the same. The learning system around it becomes smarter.
Feedback loops turn every result into a teachable moment for agents
Each agent workflow generates alerts.
An agent receives a request. It captures context, causes, invokes tools, and generates responses through possible actions. A person accepts, rejects or changes this answer. Downstream systems indicate that the action is working.
This entire chain is valuable.
AI observability enables organizations to see what is happening: operational, response, reasoning path, tool calls, data sources, intermediate steps, failure modes and outcomes. Without this visibility, organizations cannot understand why an agent behaves the way it does, let alone improve it.
But observation alone is not enough.
A greater opportunity is to translate observed behavior into institutional knowledge. The trace should not only help the developer and operators to fix the agent. It should help the enterprise understand what the agent learned, what the person corrected, what the outcome was, and what needs to change before the next similar incident.
This is a transition from monitoring AI to teaching AI.
In the agent enterprise, feedback loops link action to outcome, outcome to knowledge, and knowledge to future action.
A learning system in practice on security, observability and networking
Consider a service subject to intermittent degradation.
An observable agent detects unusual latency and error rates. A network agent detects packet loss on a particular path. The security agent notes 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.
When this happens for the first time, human experts may need to intervene. The network engineer confirms that the packet loss was caused by a misconfigured route change. A security analyst determines that the suspicious traffic is not an attack, but a side effect of a misdirected internal service. SRE associates a network event with application degradation.
That resolution contains knowledge that the organization does not need to learn again.
A mature agent learning system will capture traces, human corrections, topology context, security findings, observable signals, and final remediation steps. This will preserve the relationship between these signals: delay pattern, network path, identity behavior, rerouting and correction.
Agents will not start from scratch the next time a similar pattern appears. They can retrieve previous work, compare existing conditions, recommend a proven diagnostic pathway, and elevate it with better context.
There was no need to redevelop the basic boundary model.
The enterprise learned.
Learning agent enterprise architecture
A learning-oriented agent is much more than a model or chatbot for the enterprise. What is needed is an architecture that can collect experience, transform it into usable knowledge, connect that knowledge to an operational context, and manage how it will change future agent behavior.
Memory stores what happened: what the agent saw, what he did, where people intervened, and with what consequences.
Basics of knowledge turn this experience into reusable guidance including playbooks, examples, policies, procedures, and evidence.
A information texture integrates the operating environment. Signals to agents must be live across logs, metrics, traces, tickets, identification systems, security tools, network telemetry, collaboration platforms, and business applications. Data structure makes these signals discoverable, relational, manageable and contextually usable.
AI observation describes how agents behave by capturing instructions, tool calls, intermediate steps, responses, feedback, and results. This visibility helps organizations understand where agents are succeeding, where they are failing, and what needs to be improved.
The control plane governs how learning is translated into change: what knowledge is promoted, what guidelines or policies are updated, what agents can use new information, what approvals are required, and how changes are verified.
Together, these capabilities enable AI systems to be improved over time in a controlled, reliable manner that allows the enterprise to learn from its operations.
Organizations that learn the fastest will win
The next era of artificial intelligence will not only be won by models. Organizations that can capture learnings from every workflow, expert fix, incident, investigation and outcome will win.
The most advanced agent businesses will not simply deploy more agents. They will build systems that allow each agent to benefit from the organization’s collective knowledge.
This means linking transaction data through a database. This means observing the agent’s behavior deeply enough to understand it. This means storing experience in memory and institutionalizing it in knowledge bases. This means using the control plane to control how learning changes agent behavior.
The future of artificial intelligence is not a single autonomous agent operating on its own. It is an ecosystem of agents, people, data and controls that learn over time.
Organizations that build this 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 the Splunk Platform accelerates agent operations.
Hao Yang is vice president of AI at Cisco’s Splunk.
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