As network automation tools proliferate, the risk grows of creating more complexity rather than less. Rautanen's approach suggests that successful automation requires thinking beyond tool integration toward service modeling and abstraction design.

Antich's semantic correlation approach suggests a path toward systems that can reason about network events without explicit programming for every scenario. Whether this specific approach succeeds or not, the principles—autonomous interpretation, graph-based reasoning, semantic understanding—likely represent necessary capabilities for future network operations.

NetBrain's approach suggests that operational automation requires different tools and thinking than deployment automation—focusing on service visibility, proactive monitoring, and institutional knowledge capture rather than just configuration management.

Harper's story illustrates how network automation can become a powerful security tool even when that wasn't the original intent. The lesson? Sometimes the best security improvements come not from security-focused projects, but from operational excellence initiatives that inherently improve risk posture.

For organizations considering Terraform for network automation, Pozo's epic provides a realistic roadmap: expect challenges, plan for scale, build teams, and focus on the indirect business value that makes the journey worthwhile. Sometimes the hardest journeys yield the most valuable destinations.

Gluware's presentation reflects a maturing understanding of enterprise automation challenges. Rather than promoting tool replacement, they're advocating for integration and enhancement of existing automation investments.

What makes Temporal compelling for network automation isn't just technical capabilities—it's the recognition that complex network operations are inherently distributed systems problems.

This presentation demonstrates that automation doesn't have to conform to popular tool limitations. When requirements exceed tool capabilities, the solution isn't necessarily to reduce requirements—it might be to find better tools.

Sprygada believes organizations that embrace AI-native network operations will see "significant increases in their ability to be agile, responsive, and build scalable infrastructure." This isn't just about efficiency gains—it's about capabilities that become possible only when AI is deeply integrated into operational frameworks. Think of it as the difference between using calculators to do math faster versus using computers to solve problems that were previously impossible.

What makes Luke's presentation valuable is its honesty about operational automation at scale. This isn't automation for efficiency gains—it's automation as the only viable way to manage a network of this size and complexity.

What makes their approach compelling isn't just the technical architecture—it's the honest acknowledgment that network automation is software development. Instead of pretending otherwise or trying to transform domain experts into something they're not, they embraced specialization.