Presented by Splunk
Agentic AI is making IT and security teams dramatically more efficient. But it’s also removing the apprenticeship that has long produced experienced operators.
As organizations automate more of the work once performed by junior analysts and engineers, they’re confronting a challenge that’s as much about workforce design as architecture design: how to build the next generation of experts when AI handles the work that once trained them.
What the junior workforce has been doing
For two decades, the path to becoming a world-class SecOps analyst, SRE, or NetOps engineer ran through repetition.
Triaging false positives. Hunting through dashboards for context. Reading logs at 2 a.m. that turned out to be benign. The industry treated this work as drudgery, and in many ways it was.
But it also served as the apprenticeship.
The thousands of hours an analyst spent staring at traffic patterns built the intuition that made them invaluable when a real attack arrived. That intuition was not taught in a single course or captured in a runbook. It was accumulated through exposure, pattern recognition, failure, and escalation. Over time, this is how people earn deep analytical experience.
However, agentic AI is now beginning to automate the very tasks that once served as the training ground for that expertise. That is not a reason to slow down. The drudgery was costly. The burnout was real. Organizations should use agents to reduce toil wherever they can.
At the same time, as we remove that apprenticeship loop, we need to provide operators something better in its place. How organizations approach this issue today will determine the winners for the future.
Organizations that approach this deliberately will produce the operators skilled to succeed in the next decade. Organizations that punt on this may find themselves with faster systems today, but with fewer people who understand them deeply enough to govern them tomorrow.
When automation hollows out accountability
There is also a second dimension to this conversation that gets less attention than it should.
In regulated environments, the drudgery of apprenticeship is part of the accountability layer. Frameworks from SOX to PCI DSS to HIPAA to NIS2 assume there is a chain of human judgments behind a control decision.
Auditors do not interview models. They interview people who can explain why a system did what it did, why the decision was sound, and whether the right controls were in place.
When the population of professionals who can explain that chain begins to thin, the risk may not appear immediately. The control may still pass. The workflow may still be executed. The dashboard may still look green.
But the underlying organizational memory begins to hollow out.
This is not simply a tooling problem. It is also a workforce skill and design problem. And for organizations moving quickly on agentic adoption, the risk is closer than many think.
Building human expertise to govern AI
When we lose part of the accountability layer to agents, humans will step into a different type of governance role. Governing an agentic system means implementing automated guardrails that adapt to non-deterministic agent behavior and ensures agents behave appropriately under conditions no one fully anticipated. It means designing escalation criteria that catch the right anomalies without overwhelming humans with the wrong ones. It means implementing dynamic tools, alerts, and processes to review machine decisions to detect drift, bias, and reasoning failures that no individual case would reveal.
The ability to evaluate and respond to these exceptions requires judgment built over years of experience, learning pattern recognition that the old apprenticeship model used to produce.
That is why the workforce question and the architecture question are now the same question. If we expect humans to govern increasingly autonomous systems, we need intentional pathways that help people manage the scale and speed of AI systems while building the intuition and judgment in human operators required to do that work.
In the AI era, the most valuable platforms will not simply automate the most tasks. They will help people become more capable, more credible, and more essential as the systems around them become faster and more intelligent.
That means organizations need to invest in the full ecosystem of expertise for operators: communities that spread shared practices, certifications or other proofs that make expertise visible, and human-oriented explanations and verifications in the AI along with learning paths that build capability. Empowerment is an architecture design choice
Human empowerment is a critical part of the conversation around the practical use of AI. However, without an intentional strategy to back this up, it risks becoming the kind of phrase that means nothing because it can mean anything.
Empowerment for agentic systems cannot just be a conceptual requirement. It has to be a set of design choices baked into how systems behave. An agentic system that empowers its human operators and grows their professional skillset does four things:
1. Exposes reasoning, with the data lineage behind it
Every recommendation an agent makes should be traceable to the data it considered, the logic it applied, and the provenance of the inputs it used. Operators who can see reasoning develop judgment about when to trust it. Operators handed only conclusions do not.
2. Tiers authority by confidence and impact
Familiar, low-risk patterns can be handled autonomously. Novel situations or actions with meaningful blast radius should escalate by default. The boundary should be explicit and configurable by the teams that own the consequences.
3. Treats disagreements as a correction signal
When an experienced engineer overrides an agent, they are doing more than disagreeing. They are correcting the system with judgment the model did not have: a fragile dependency, a quirk in the environment, a constraint the data never saw. A system that registers the override but ignores the reasoning behind it learns nothing from the one moment a human knew better.
4. Captures resolutions as cross-domain knowledge
How an incident gets resolved is a lesson that rarely stays in one lane. A SecOps incident may expose an ITOps weakness. A network issue may trace back to business impact. When that connection lives only inside a closed ticket, the next team to hit it starts from zero. Resolutions should travel across domains, not die where they were filed.
These are not aspirational qualities. They are testable product capabilities. Leaders evaluating agentic systems should be able to identify where these capabilities live, what happens when they fail, and whether operator skill improves after deployment.
The next advantage is when human and AI scale together
For AI systems to be practical, trusted, and work at scale, the critical design point is for the AI to work deeply alongside and empower human operators.
As such, the agentic era is not a story about replacing humans. It is a story about redesigning the systems humans operate so that these operations can happen at machine speed and scale, while human expertise grows at the same time. Together, rather than at each other’s expense.
That outcome is not a given. It will happen only where leaders treat operator development as a priority, not an afterthought. To achieve this, agentic systems have to be intentionally designed to expose reasoning, capture learning, and route work back to humans in ways that build skill and career rather than erode both.
The agents will keep getting smarter and faster. The ability of operators who work alongside them to learn and grow in lockstep, will determine whether the next decade of digital resilience is something organizations truly own, or something they rent from a shrinking pool of expertise.
Learn more about how Cisco Data Fabric powered by the Splunk Platform is helping teams accelerate agentic operations.
Kamal Hathi is SVP and GM of Splunk, a Cisco Company.
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