The reporting problem
Performance teams often spend too much time moving numbers between interfaces and too little time interpreting what changed. The repeated work is predictable: fetch platform metrics, compare periods, calculate derived indicators, identify anomalies and write a summary.
A simple architecture
OpenClaw separates the workflow into four layers: data collection, normalization, interpretation and delivery. Platform APIs or structured exports provide the inputs. A consistent schema prevents Google and Meta metrics from being compared incorrectly. Rule-based calculations establish the facts before an AI layer summarizes the context.
Guardrails before generation
AI should not invent a cause for a movement that the data cannot explain. The prompt should distinguish observed facts, plausible hypotheses and missing context. Thresholds for spend changes, CPA movements or conversion drops should be calculated explicitly, and the output should preserve the source period.
Delivering the right amount of information
A Telegram alert is useful when it is brief enough to scan and specific enough to act on. The daily summary can highlight meaningful movements, while a linked dashboard retains detail for diagnosis. Not every fluctuation deserves a notification.
Where human judgment remains essential
The operator still decides whether a change is strategically important, whether creative fatigue is the likely explanation, and whether an optimization should be applied. Automation improves cadence and visibility; it does not own accountability.