In Part 6, we addressed the security, privacy, and governance guardrails that make autonomous operations safe to deploy at scale - Zero Trust principles applied to agents, role-based access control, a structured guardrail framework, and data minimisation practices for protecting customer PII. We closed with the argument that trust is now an operational objective, one that needs to be measured and continuously improved.
That raises a question that has run quietly through the entire series: if the system is increasingly autonomous, where exactly does the human fit? This final part tries to answer this
The Human Thread Through This Series
Look back at every part of this series and the human expert is always present. In Part 3, it was the on-call engineer who reviewed the structured evidence and approved the rollback of the aggregation router - not because the system could not execute autonomously, but because a human decision at that moment carried accountability that no agent can assume. In Part 4, the Operations Guardrail paused agent action when confidence fell below threshold and notified an engineer. In Part 5, the Human Override Rate was defined as a key SLO - a signal that the agent workforce needs retraining when humans consistently disagree with its conclusions. In Part 6, mandatory human review applied to any decision affecting Tier-0 or Tier-1 services regardless of agent confidence.
The human is not an afterthought in autonomous operations. The human is at the centre.
This final part addresses what that actually means in practice - how the role of the network engineer transforms, what new skills it demands, and why the operators who navigate this transition well will define what autonomous network operations looks like for the next decade.
From Troubleshooter to Oversight Engineer
The traditional network engineer role is defined by depth in a specific domain - RAN, Core, Transport, or Cloud - and by the ability to navigate complex, fragmented management systems to find and fix problems. That depth remains valuable. But the primary activity changes fundamentally.
In an autonomous operations environment, the agent workforce handles the data gathering, correlation, and initial diagnosis. The engineer is no longer the first responder to every alert. Instead, the engineer's focus shifts to three responsibilities that only humans can own:
Intent Definition: Establishing the high-level service priorities that govern how agents make trade-offs. For example: during periods of network congestion, should critical healthcare network slices be prioritised over general consumer traffic? Should an agent proceed with a planned change if it detects elevated latency on a dependent service path, or pause and escalate? These are policy decisions that reflect business commitments, regulatory obligations, and ethical priorities. They cannot be delegated to an agent - they must be defined by humans and encoded as intent that the agentic workforce executes.
Trust Calibration: Setting and continuously tuning the confidence thresholds that determine when agents act autonomously and when they pause for human approval. This is not a one-time configuration - it evolves as the agent workforce matures, as the network changes, and as operational experience accumulates. An engineer who understands both the network and the agent reasoning process is uniquely positioned to calibrate these thresholds in ways that neither a pure network expert nor a pure AI specialist could do alone.
Exception Management: Reviewing situations where agent reasoning is ambiguous, where a recommended action falls outside established policy boundaries, or where the confidence score triggers a mandatory pause. The engineer is not reviewing every decision - only the ones that genuinely require human judgment. This is a fundamentally different cognitive task from traditional troubleshooting: the problem has already been identified, the evidence has already been assembled. The engineer is evaluating a conclusion, not searching for one.
The Skills Shift: From Command Line to Policy Engineering
This transformation in responsibility requires a corresponding shift in skills. The engineer who spent a career navigating CLI interfaces across vendor management systems will find that capability less central than it once was. What becomes more valuable is a different set of competencies:
Policy Engineering: The ability to translate complex business requirements and operational priorities into machine-readable intent that the agentic workforce can execute consistently. This is closer to systems thinking than traditional network engineering - understanding how policy decisions propagate through an autonomous system and what second-order effects they might produce.
Reasoning Auditing: The ability to read and evaluate an agent's reasoning trace - the NKG path traversed, the TKG events consulted, the confidence score and what drove it - and make a sound judgment about whether the conclusion is trustworthy. This requires understanding both the network domain and the logic of how agents reason over it.
Cross-Domain Fluency: By offloading primary data correlation to the agent workforce, engineers are freed from the constraint of deep specialisation in a single domain. An engineer previously focused on RAN monitoring can now apply their expertise across the full service chain - understanding how transport performance affects RAN throughput, how core network function behaviour propagates to access layer KPIs, and how to provide targeted guidance to agents operating across domain boundaries. The domain expert becomes a cross-domain practitioner.
Human-in-the-Loop: The Approval Gateway
Even in a highly autonomous system, the human remains the final authority for high-impact decisions. This was established in Part 3 with the midnight rollback scenario and reinforced throughout the series. It is worth being precise about what this looks like in practice.
When an agent workflow reaches a decision that requires human approval, the system presents a structured evidence package - not a raw data dump, not a vague alert, but a composed, traceable artifact that contains:
- A clear explanation of the identified issue and its likely cause, derived from the TKG causal chain
- A visualisation of the affected service path through the NKG, showing exactly which elements, services, and customer segments are in scope
- The proposed remediation action and the reasoning behind it
The engineer's task is to evaluate this evidence and provide authorisation - or to challenge it. The shift is significant: the engineer is no longer spending cognitive energy finding the problem. That energy is redirected entirely toward evaluating the solution. For experienced engineers, this is a more effective use of expertise than the current model demands.
A Note on the Transition
It would be dishonest to present this transformation as straightforward. The shift from active troubleshooter to oversight engineer requires engineers to develop genuine comfort with a system that acts on their behalf - and that requires trust in the system that is built incrementally, not assumed from day one.
The phased adoption approach described in Part 5 is relevant here too. Engineers who begin by observing agent reasoning before autonomous action is enabled, who participate in calibrating confidence thresholds, and who audit early agent decisions against their own judgment, develop the operational intuition needed to govern the system effectively. The transition works best when it is collaborative rather than imposed.
The operators who invest in this transition - building the skills, the processes, and the cultural readiness alongside the technical infrastructure - are the ones who will lead in the autonomous era. Those who treat it purely as a technology deployment will find that the technology alone is not enough.
Conclusion: A Human-Machine Partnership
This series started with a straightforward problem: configuration errors cause most major telecom outages, and as networks grow more complex, the gap between what human teams can monitor and what needs to be monitored keeps widening.
The answer we have explored across seven parts is not to remove humans from the loop. It is to change where in the loop humans operate - upstream, at the level of policy and intent, with the agent workforce handling the speed and scale of real-time operations that no human team can match.
The Knowledge Graphs provide the shared memory. The agents provide the reasoning. The Agentic OS and Agentic Ops provide the infrastructure and discipline to scale. The security and governance frameworks provide the trust. And the human expert provides the judgment, the accountability, and the intent that the entire system serves.
The operators who successfully integrate the speed of AI with human strategic judgment will not just manage the transition to autonomous networks - they will define what it looks like.
What is in Part 7
In this final part, we have covered:
- How the human thread has run through every part of this series as a design requirement, not an afterthought
- The three core responsibilities of the Oversight Engineer: intent definition, trust calibration, and exception management
- The skills shift from command-line proficiency to policy engineering, reasoning auditing, and cross-domain fluency
- What the Human-in-the-Loop approval gateway looks like in practice
- An honest note on the cultural and organisational transition that the technical transformation requires
This series has been Co-Authored by Balakrishnan K and Ravi Emani. Each part combines conceptual frameworks with practical implementation considerations for telecom operators moving toward TM Forum Autonomous Networks Level 4/5.
