This article discusses common pitfalls in building real-world automations, particularly the unreliability caused by naive event-driven triggers. It advocates for a "controlled activation model" to manage timing and data consistency, treating reports as deliberate system artifacts rather than immediate reactions to individual events. The piece also highlights the importance of structured AI output and effective delivery channels for operational trust.
Read original on Dev.to #systemdesignBuilding robust automations often reveals that reliability issues don't manifest as outright system failures, but as inconsistencies and unreliable reporting. This article details an experience integrating JobTread and CompanyCam for daily job reports, where the primary challenge wasn't data availability, but ensuring the reports were accurate and consistent despite imperfect timing of underlying events.
Many automation workflows are initially designed as a series of direct reactions: an event occurs (e.g., photo uploaded, description updated), and an automation runs. While this seems to offer "real-time" updates, it frequently leads to "reporting drift." This drift occurs because report content changes based on the exact moment of execution, leading to inconsistencies, duplicates, and a lack of trust from consumers. Late-arriving data or closely spaced events can perpetually alter the reported state.
To counter the unreliability of event reactions, the article proposes a significant design shift: treating reports as deliberate system artifacts rather than transient outputs. This involves implementing a "controlled activation model" where a report is generated once for a defined reporting window (e.g., one report per job per day).
System Design Principle: Deliberate Artifacts vs. Event Reactions
For critical reports or aggregated views in event-driven systems, consider designing explicit "materialized views" or "system artifacts" that are generated on a schedule or after a controlled consolidation period, rather than attempting real-time updates based on every granular event. This improves consistency and reliability at the expense of absolute real-time immediacy.
Beyond timing, the article emphasizes two other critical factors for automation success: structured output and effective delivery. Unstructured AI summaries can lead to inconsistent and less "decision-ready" information. By designing the AI layer as a "constrained renderer" with predefined sections (e.g., work completed, issues, next steps), operational clarity is maintained. Furthermore, delivering reports through existing, familiar channels ensures adoption; reports hidden behind new logins are often ignored, regardless of their content.