Microflow Debugging Checklist: 12 Telemetry Signals Every Playable Needs to Diagnose Drop‑offs

These signals are the minimal set that lets you triage the usual playable drop-offs: UI errors, auth friction, network failures/timeouts, and pricing/confusion. Capture each event with consistent names and minimal required properties (user_id/session_id, microflow_id, step_id, timestamp, error_code where applicable).

Prefer event names that read like steps in your funnel: demo_start, step_rendered, user_interaction, external_auth_initiated, external_auth_completed, api_call_sent, api_call_success, api_call_failed, pricing_shown, pricing_interacted, conversion_attempted, conversion_succeeded. Tracking both client-side and server-side variants for critical payments/auth events reduces blind spots.

General rule: capture UI lifecycle, render, and click-level events on the client; capture authoritative state changes (payments, account creation, invites) on the server. For key cross-cutting events (signup, conversion, billing), publish both a client-side event (for UX timing) and a server-side event (for truth). Use distinct names or a property like source:client/source:server to avoid double-counting.

Why duplicate? Client events show user-visible latency and rendering failures; server events show whether the backend accepted and persisted the action. When you have both, you can tell whether a drop-off came from a broken frontend handler (client event present, server absent) or a backend rejection (server error_code present). Industry guides from Amplitude and Mixpanel recommend this split for reliable funnels and to avoid event decay or identity mismatches.

Add lightweight Playwright checks that assert microflow progress and key telemetry emission. Each check should be short (5–10s) and target high-value failures: missing UI, auth redirect loops, or backend errors returned in the page. Use Playwright’s expect() so assertions wait and retry for stability.

Also emit a tiny console-driven telemetry assertion you can run in a headless job: open the playable, simulate a click path, and verify that the expected client events were emitted (mock the network or spy on analytics endpoint). For server-side checks, call the authoritative API endpoint after a simulated user action and assert a 200/202 and expected payload shape.

Use this decision tree: start with presence/absence of client vs server events for the same step. If client saw the action (client event present) and server did not (server event missing) — suspect network errors, CORS, or auth tokens. If server returned an error_code (api_call_failed) — suspect backend validation or rate-limits. If UI failed to render (step_rendered missing) — suspect JS exceptions, missing assets, or race conditions.

Match fixes to cause: UI bug → reproduce locally, add unit/e2e test, roll back faulty change. Auth friction → inspect auth logs, validate callback URLs, surface clearer UX messaging. Network timeouts → add retries/exponential backoff and surface a retry button in the playable. Pricing confusion → instrument pricing_shown and pricing_interacted and run a small A/B test with clearer CTAs or price anchors.

Below are the three fillable artifacts to copy into your repo or tracking plan immediately: (A) Event table (name, author, capture_location, required_props), (B) JSON schema snippet for each event showing required keys and types, (C) 30‑minute RCA template with sections: incident summary, signals observed, likely root cause, repro steps, immediate mitigation, permanent fix, and owner + ETA. Keep these artifacts in a plain MD file and include them in your CI checks that validate telemetry before deploy.

Practical tips: enforce a naming convention (verb_subject: e.g., demo_start, pricing_interacted), keep payloads small (GA4 16KB guidance), and test your tracking in the same Playwright smoke check you added earlier. If you use a CDP or event pipeline, wire the tracking plan through it so updates are versioned and run a weekly small audit for event decay.