What does new technology have to prove before you trust it with your network?
Scott Robohn joins Andy Lapteff for a conversation about earning trust, from Ethernet interoperability and vendor bake-offs to network automation and AI. Recorded at Networking Field Day 41, they explore how explicit testing, clear permissions, error handling, and human judgment help engineers decide when a system is ready for more responsibility.
Scott revisits a four-vendor evaluation at Bell Atlantic and describes lessons from Lumen’s approach to automating workflows. Andy shares a less comfortable example: software he built with AI that helped him get work done while failures went unnoticed.
The conversation also explores AI adoption pressure, the value of critical thinking, and why a read-only troubleshooting workflow can be a useful first step toward building confidence in a new tool.
Listen for a practical discussion of how trust develops in network technology, and what AI still needs to earn.

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AI can write code, troubleshoot systems, build applications, analyze telemetry, and increasingly take actions on our behalf.

So what happens to humans?

In this episode of The Art of Network Engineering, Andy sits down with Ryan Booth to explore what becomes more valuable as AI gets better at doing the work humans do.

The conversation starts with systems thinking. Everyone from technology leaders to engineers is talking about the need for systems thinkers, but what does that mean? Are network engineers better positioned for it than they realize?

Ryan explains why working with increasingly capable AI systems pushed him away from thinking like an individual engineer and toward thinking more like a manager, architect, and orchestrator. Instead of specifying every task, the job increasingly becomes defining outcomes, understanding dependencies, evaluating results, and deciding how much autonomy to give the system.

Andy and Ryan discuss human-in-the-loop automation, AI agents, building trust through progressively greater responsibility, the limits of today's systems, and why replacing people may be the wrong way to think about AI.

They also get into the fear surrounding AI, the enormous economic incentives driving its development, parallels with previous technology revolutions, and what engineers should do right now to prepare.

The takeaway isn't to outrun AI.

It's to learn how to use it, understand the systems around it, and become more valuable at the level where judgment, integration, leadership, and context matter.

Topics include:

• Systems thinking for network engineers
• AI agents and autonomous systems
• Human-in-the-loop automation
• How to build trust in AI systems
• AI-assisted network troubleshooting
• The changing role of engineers
• Learning AI through personal projects
• Automation, orchestration, and architecture
• AI risk, hype, and uncertainty
• What technical professionals should learn next

Ryan Booth: https://abstractryan.ai

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Everyone says network engineers need to automate.

Learn Python. Learn Ansible. Build a source of truth. Learn Git. Test your changes. Stop doing everything manually.

There’s just one problem:

When?

At AutoCon 5 in Munich, Andy Lapteff sits down with Dutch network automation consultants Bart Dorlandt and Bart Smeding for a conversation that starts with Python and eventually lands somewhere much more interesting: time.

Automation can turn three hours of work into three minutes. But getting there requires engineers to invest time they often don’t have, or don’t feel permitted to protect.

Andy knows that firsthand. At one point in his career, his manager gave him an entire day every week to learn automation. Instead, Andy kept using that day to catch up on operational work.

Why?

That question opens a much bigger conversation about engineering culture, burnout, professional identity, European and American attitudes toward work, change freezes, technical debt, and the endless tension between keeping today’s network running and building a better way to operate it tomorrow.

This isn’t another lecture telling network engineers to “learn Python.”

It’s a conversation about why that advice is so much easier to give than to follow, and what has to change if we want network automation to become common.

Bart Dorlandt:
https://www.linkedin.com/in/bartdorlandt/

Bart Smeding:
https://www.linkedin.com/in/bartsmeding/

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