Capabilities

Observability

Operational observability for distributed control: state, health, and diagnostics that connect UI, backend, and edge.

Design intent

Use this lens when adopting Observability: define success criteria, start narrow, and scale with safe rollouts and observability.

Unified timelines reduce time-to-diagnosis across teams
Stable IDs + rollout state make incidents debuggable
Alerts should detect degradation, not just total failure

What it is

A distributed automation system needs end-to-end visibility: runtime health, fleet status, and telemetry tied back to versions and deployments.

Design constraints

Unified timelines reduce time-to-diagnosis across teams
Stable IDs + rollout state make incidents debuggable
Alerts should detect degradation, not just total failure

Architecture at a glance

Edge emits health/events/telemetry with stable IDs (device/resource/app/deploy)
Store-and-forward buffers protect evidence during outages
Backend correlates timelines to snapshots so debugging becomes “find the diff”
This is a capability surface concern: visibility is required to scale safely

Typical workflow

Define the minimal signal set: deployment state + runtime health + adapter health + telemetry
Create dashboards that answer: what is running? is it healthy? is it correct?
Use correlated timelines (events + telemetry + rollout state) to debug
Codify alerts for restart bursts, buffer saturation, flapping, and drift

System boundary

Treat Observability as a capability boundary: define what success means, what is configurable per site, and how you will validate behavior under rollout.

Example artifact

Event timeline (conceptual)

t=00:00 deploy:start snapshot=snap_... device=edge-12
t=00:12 runtime:healthy resource=forte-1
t=00:20 adapter:connected protocol=modbus endpoint=pump-1
t=01:10 telemetry:ok points=128 gap=0s
t=03:40 alert:staleness point=pump_speed > 5s

What it enables

Faster incident response with clear traces
Rollout safety gates based on health
Confidence when changing logic and configuration

Engineering outcomes

Unified timelines reduce time-to-diagnosis across teams
Stable IDs + rollout state make incidents debuggable
Alerts should detect degradation, not just total failure

Quick acceptance checks

Define standard health signals for runtime, adapters, and deployments
Ensure timelines can join telemetry + events + deployments via stable IDs

Common failure modes

Telemetry gaps from buffer saturation or upstream auth/network issues
Clock skew and batching causing out-of-order timelines
“Everything is green” while behavior is wrong (mapping/data-quality issue)
Over-alerting: noisy signals that hide real regressions

Acceptance tests

Verify the deployed snapshot/version matches intent (no drift)
Run a canary validation: behavior, health, and telemetry align with expectations
Verify rollback works and restores known-good behavior

Deep dive

Practical next steps

How teams typically turn this capability into outcomes.

Key takeaways

Unified timelines reduce time-to-diagnosis across teams
Stable IDs + rollout state make incidents debuggable
Alerts should detect degradation, not just total failure

Checklist

Define standard health signals for runtime, adapters, and deployments
Ensure timelines can join telemetry + events + deployments via stable IDs
Create incident views: “what changed?”, “what failed first?”, “what recovered?”
Alert on restart bursts, drift, buffer saturation, and connectivity flaps

Next steps

Common questions

Quick answers that help align engineering and operations.

What’s the minimum observability needed to operate remotely?

Deployment status + runtime health + connectivity/adapter health + basic telemetry timelines tied to snapshot IDs.

Why do incidents feel hard without good observability?

Because you can’t correlate symptoms to changes. Version-correlation is what turns “mystery behavior” into a solvable diff.

How do we reduce time-to-diagnosis?

Use consistent IDs, record state transitions, and keep a single timeline view that joins deployments, health, and telemetry.