Date & time
17:00
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Teams are drowning in noisy observability stacks, incidents buried inside distributed systems and “unknown-unknown” production failures.
AI monitoring adoption jumped from 42% to 54% year-on-year as teams raced to understand its increasing complexity.
This session is designed to give engineering leaders a practical operational playbook they can take back to their teams immediately. Using real-world scenarios, we’ll explore why traditional monitoring and review practices are no longer enough for frontier-level software.
Alongside the technical practices, we’ll focus on the leadership that effective systems require, with a focus on reducing alert fatigue and cognitive debt and creating shared operational language.
Key takeaways:
- Replace static dashboards and reactive monitoring with approaches that surface production issues earlier
- Build deployment pipelines that preserve traceability, so you can cut debugging time by linking issues back to the change behind them
- Create review workflows that analyse system behaviour, lowering the cognitive load on engineers