Cisco ThousandEyes Innovation & Technology Culture

What’s your rule for releasing fast without chaos — and what KPI proves it?

To release quickly without chaos, it’s essential to establish automated testing and real-time alerting at the individual service level. As our services grow more complex, predicting every possible interaction in advance becomes unmanageable. Instead, each service should define clear health metrics — such as error rates, response times and availability — and continuously monitor these indicators. By injecting continuous baseline synthetic traffic (independent of real users), we can proactively detect degradation or failures before they impact customers, even for transitive dependencies. Fast, automated rollback mechanisms further reduce risk, enabling confident, rapid releases. The KPI I use to prove this out is mean time to recovery.

What standard or metric defines “quality” in your toolchain?

For me, “quality” in a toolchain is defined by architectural simplicity and the ability to reason about system behavior. I apply a concept similar to cyclomatic complexity — not just to code, but to system architecture as a whole. By modeling services and their interactions as a graph, I assess the impact of potential outages or degradations, not only for each service but also for downstream dependencies. Each connection in this graph represents more than just up/down status; it includes metrics like latency, retry behavior and resource saturation. For example, Bufferbloat illustrates how local optimizations (like buffer sizes) can have unexpected system-wide effects. High architectural complexity makes systems brittle and harder to maintain, while simplicity enhances reliability. Ultimately, quality is reflected in how easily we can understand, reason about and operate the system.

Share one recent adoption and its measurable impact.

Recently, I’ve expanded my use of AI beyond code assistance to non-coding tasks, particularly for technical document review. By leveraging AI to summarize and research referenced technologies, I significantly reduce the time spent on background research and ramp-up, allowing me to focus on the critical parts of the proposal. Additionally, I use AI to summarize chat discussions, as well as to serve as a first-pass editor for my own writing. Summarization and grading are strengths of current LLMs, so I’m finding it very effective to take on a collaborative approach to using AI in that respect. While integration across all tools is still developing, the measurable impact has been a noticeable increase in productivity and efficiency — often reducing document review and summarization time by 25 to 30 percent. I’m optimistic about further productivity gains as AI tools mature.

Innovation at Cisco ThousandEyes is tied to a broader mission: helping shape the future of digital work by solving complex problems, expanding visibility across enterprise environments and advancing AI-driven insights.

“If ThousandEyes can improve the quality of the internet, then we can improve the quality of life for people, and I can’t think of anything more important to get excited about each day.”

How did you begin using AI in your role? What specific tools and processes do you utilize? 

My primary use of AI involves refining text. Although I always draft the content myself, I rely on ChatGPT to enhance readability, correct grammar and polish the writing. This allows me to focus on the content itself while ensuring clarity and coherence. I find ChatGPT useful as a search engine, particularly when I need to grasp high-level concepts quickly, but for detailed and intricate information, its accuracy tends to diminish — I always cross-check ChatGPT results with reliable resources.

How has using AI simplified or streamlined your work? 

Although my coding tasks have reduced, I occasionally need to write simple scripts for basic analyses or specific queries. In such cases, ChatGPT assists in quickly outlining a basic structure, after which I fill in the details. It’s especially handy when I’m exploring different libraries or need sample code suggestions, but one must be careful never to input proprietary code.

What recommendations do you have for tech professionals interested in using AI tools in their daily work? 

While these tools can be valuable for streamlining workflows and providing insights, it’s important to remember their limitations. These tools can produce inaccuracies and errors, and there’s often a risk of exposing sensitive information. That’s why it’s best to use these tools as aids before overseeing and verifying the work they produce.