Operations | Monitoring | ITSM | DevOps | Cloud

AI adoption is nearly universal. So why are most teams still struggling? In this session from GitKon, Nathen Harvey, head of DORA at Google Cloud, shares findings from the 2025 DORA State of AI-Assisted Software Development report, drawing on data from nearly 5,000 developers worldwide. The answer isn't more AI. It's what surrounds it.

LLMs have sucked all the oxygen out of the AI conversation — but AI is much more than just LLMs, and network engineers have been using AI techniques (machine learning, statistics, fuzzy logic, expert systems, neural networks) for decades. So what should LLMs be doing in network operations, what shouldn't they be doing, and how do agentic AI architectures fit in?

Autonomous operations has been the goal for years. But most “automation” never got us there—it just helped teams keep up. Now that’s changing. Agentic AI introduces a fundamentally different model:– Purpose-built agents, not static workflows– Real-time decisioning, not predefined rules– Collaboration across agents, not isolated tasks Instead of automating steps, agentic AI enables systems to **reason, adapt, and act**—at a speed and scale humans simply can’t match. That’s what turns autonomous operations from a long-standing ambition into something actually achievable.

Check out: https://www.implicator.ai/anthropic-shifts-enterprise-billing-to-per-token-pricing-the-flat-fee-era-is-over/

Or visit: https://speedscale.com/blog/local-llms-on-mac-cost-savings/

In a crowded MSP market, standing out takes more than great service. See how BigPanda customers are using AI-driven operations to differentiate their offerings, boost profitability, and maximize their ServiceNow investment.