The market for AI-agent observability is going to fill up with hosted dashboards. That makes sense. SaaS is the default delivery model for new operational tooling. It is fast to deploy, easy to demo, and easy to buy when the buyer is still experimenting. But AI coding agent governance
Most teams do not budget for AI coding agents in a way that survives contact with reality. They think in subscriptions, seats, or rough monthly caps. What they actually have is a new compute-consumption pattern running inside engineering delivery, and it behaves more like an operational workload than a
Most teams adopting AI coding agents still govern them as if they were autocomplete. That is the first mistake. Autocomplete suggests text. An agent runs tools. It reads files, edits files, executes shell commands, fetches remote content, retries failed operations, writes debug output, and keeps going when a human would
When running workloads in AWS, concept of shared responsibility assumes that AWS is responsible for the security of the cloud infrastructure, but you are responsible for securing environments running in the cloud. One of the important aspects of overall security posture is threat detection and compliance. Thankfully, AWS provides a
Security failures in modern systems are usually related to preventable issues: misconfigured infrastructure, leaked secrets, and vulnerable dependencies that slip through delivery. For that reason in today’s software engineering practice, security is no longer an isolated concern handled by a single team, it's continuous, organization-wide commitment.