Technology Radar
Temporal fakes extend the idea of simulating real-world systems for development and testing, a practice long used in IoT and industrial platforms. With AI coding agents reducing the effort required to build such simulators, teams can now create high-fidelity replicas of external dependencies much more easily. Unlike traditional mocks that return static request–response pairs, temporal fakes maintain internal state machines and model the temporal evolution of real systems.
One of our teams used this technique while developing an observability stack for large GPU data centers, avoiding the need to procure physical hardware. Testing alert rules, dashboards and anomaly detection against real systems can be impractical — for example, intentionally overheating a GPU to validate a thermal throttle alert. Instead, the team built fakes for hardware domains such as NVIDIA DCGM and InfiniBand fabric using Go. These simulators enabled failure scenarios such as thermal throttling, XID error storms, link flaps and PSU failures with configurable intensity and duration, orchestrated via a process-compose stack.
A central registry defined valid failure scenarios, while an MCP server exposed scenario injection to the agent. The agent could trigger faults, for example, injecting a thermal throttle on a specific GPU and verify that metrics changed, alerts fired and dashboards updated as expected. This temporal fidelity makes the technique valuable for testing complex systems where failures cascade. However, teams must ensure the fakes remain faithful to real-world behaviour; otherwise, they risk creating false confidence in automated pipelines.
