Datadog’s Yrieix Garnier Discusses AI in Production: Trust, Costs, and Failure Modes
Product teams are increasingly challenged to integrate artificial intelligence into established enterprise architectures while grappling with observability, cost control, and potential failure modes. Yrieix Garnier, Vice President of Products at Datadog, sheds light on the essential technical signals that distinguish viable AI applications from those that might falter due to complexity, costs, or operational blind spots.
As teams assess AI use cases, clear observability throughout the lifecycle is crucial. Sustainable AI systems should present comprehensive telemetry, enabling teams to trace inputs, decisions, and outcomes effectively. Without this visibility, scalability is often hindered. Furthermore, consistent cost behavior is a significant indicator; successful implementations generate predictable transaction costs, which empowers teams to confidently scale operations. Conversely, any unpredictable cost spikes that lack attribution to value can halt adoption efforts.
Operational resilience in AI systems is characterized by graceful degradation. Effective fallback mechanisms, latency budgets, and error-handling protocols are vital for managing failures, model drift, or uncertain results. Ultimately, systems that seamlessly integrate into existing workflows, avoiding the creation of new silos, are more likely to be embraced as standard production services, a promising sign of scalability.
The landscape of observability is expanding beyond traditional metrics of latency and throughput. Product teams are increasingly connecting technical signals to business outcomes. For instance, latency spikes and transaction failures are now examined in relation to tangible impacts, such as declining conversion rates or abandoned transactions. By contextualizing telemetry with business metrics, teams can gauge the immediate revenue and customer experience repercussions of technical incidents.
Trust in AI systems is evolving into a quantifiable element of observability. It is no longer sufficient to assess performance solely based on speed and availability. Organizations must track anomalies in model behavior over time to identify any erosion of trust before it affects customers. Hence, observability serves as a critical link between technical metrics and high-level decision-making.
For industries like banking, aviation, and manufacturing—rich in legacy systems—the path to modernization often lies in incremental changes rather than complete overhauls. Successful organizations tend to enhance legacy systems with modern observability layers, using lightweight agents and API gateways to integrate telemetry into a unified observability framework. This careful bridging allows for tracing transactions from traditional core systems through to contemporary microservices, ensuring clarity across operations.
As AI systems progress toward autonomy and agent-driven architectures, new failure modes arise. One concerning pattern is cascading failures, where minor errors can escalate across systems before human intervention occurs. Additionally, silent failures present risks where systems appear operational but subtly produce incorrect outputs. Granular observability is essential to mitigate such risks, preventing unexpected budget overruns associated with autonomous agent activity.
In a landscape where security responsibilities are shared across development, site reliability engineering, and platform teams, unified observability must be prioritized. When security data exists in silos, it risks being overlooked. By integrating performance data with security insights within unified platforms, vulnerabilities and other security issues can be evaluated in context, highlighting their operational relevance. This alignment ensures that accountability is mapped to specific production services rather than diluted across functions, reinforcing the focus on prioritizing security amid operational imperatives.