Observability
Collecting logs, metrics, and traces to understand how workflows run, where they fail, and how long they take.
Observability captures logs, metrics, and traces so you can see how automations behave end to end. It answers what happened, where, and how long it took.
Operators use it to debug failed runs, spot latency spikes, and find error hot spots across services and workflows. It replaces guesswork with clear signals.
In workflows, observability is embedded via instrumentation at each step, correlated by IDs. The impact: faster incident response, better capacity planning, and trust in automation performance.
Frequently Asked Questions
What should I instrument first?
Start with request IDs, step-level timing, success/failure counts, and key business metrics. Add structured logs for errors and retries.
How do I correlate logs, metrics, and traces?
Propagate a correlation/trace ID through all steps and include it in every log and metric. Use tracing to visualize the full path.
What SLIs matter for automation?
Success rate, latency percentiles per step, queue depth, and error categories. Tie them to SLAs and alert on breaches.
How verbose should logs be?
Log enough context to debug (inputs, outputs, errors) without dumping sensitive data. Use structured logs and redact PII.
How do I detect regressions?
Compare metrics before/after releases, set alerts on latency/error deltas, and watch DLQ/queue growth for signs of trouble.
Can observability be sampled?
Yes—sample traces/logs in high-volume paths, but keep full fidelity for errors and critical workflows. Ensure sampling doesn’t hide incidents.
What tools help?
Tracing (OpenTelemetry), log aggregation, and metrics stores. Dashboards and alerts should be accessible to ops and eng.
How do I keep costs down?
Sample non-critical traces, set log retention policies, and drop noisy fields. Focus on actionable signals over raw volume.
Should business metrics be included?
Yes—pair technical metrics with business outcomes (processed orders, approved tickets) to align ops with impact.
Agentic AI
An AI approach where models autonomously plan next steps, choose tools, and iterate toward an objective within guardrails.
Agentic Workflow
A sequence where an AI agent plans, executes tool calls, evaluates results, and loops until success criteria are met.
Agent Handoff
A pattern where one AI agent passes context and state to another specialized agent to keep multi-step automation modular.
Ship glossary-backed automations
Bring your terms into GrowthAX delivery—map them to owners, SLAs, and instrumentation so your automations launch with shared language.
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