The Importance of Context Engineering in the AI Era

The ability to detect, diagnose and remediate complex application, IT, and security issues has always been a crucial component of delivering technology-enabled customer experiences. As AI introduces a new layer of abstraction in how these experiences are built and delivered, the operational complexity behind them advances just as quickly.

For decades, observability practitioners and vendors have chased both predictive anomaly detection and root cause without a satisfying solution, ultimately settling for making the data more consumable by humans and counting on our own cognition and tribal knowledge to close the gap. Despite significant investments of time, money, and effort, progress has been only marginal. Fortunately, the same technology that enables this giant step forward in the productivity of building these services, also unlocks the long sought after capabilities to better manage and operate the systems as well.

Take, for example, an agent. An agent is far better equipped to identify both correlation and causation than a human manually scanning log files or tracing a degraded user experience to a backend database query. Even more so, it can anticipate the long-term impact of changes that begin to introduce anomalous behavior into the system.

A final challenge remains however, and it’s the same one that has plagued Observability and Monitoring since their inception: the sheer volume and complexity of the data. Foundational models provide all the logic we need to resolve these problems today, but the scale and intricacy of telemetry data expose the inefficiencies in how the models process information. The result is a breakdown in cost, speed, and accuracy for operational use cases.

Enter Context Engineering. The good news is that AI use cases are naturally evolving from Prompt Engineering approaches to Context Engineering strategies. Context Engineering is what makes AI truly operational for large dynamic data sets, enabling  models to process complex telemetry efficiently and effectively. It ensures that AI models not only process data but also comprehend the environment, intent, and nuances that shape meaningful decision-making.

Context engineering is how we drive scale and guarantee accuracy within the agentic operations era. As Shopify CEO Tobi Lütke stated, it is “the art of providing all of the context for a task to be plausibly solvable by an LLM.” This capability separates the promise of AI from its practical reality.