google-agents-cli-observability

name	google-agents-cli-observability
description	This skill should be used when the user wants to "set up tracing", "monitor my ADK agent", "configure logging", "add observability", "debug production traffic", or needs guidance on monitoring deployed ADK (Agent Development Kit) agents. Covers Cloud Trace, prompt-response logging, BigQuery Agent Analytics, third-party integrations (AgentOps, Phoenix, MLflow, etc.), and troubleshooting. Part of the Google ADK (Agent Development Kit) skills suite. Do NOT use for deployment setup (use google-agents-cli-deploy) or API code patterns (use google-agents-cli-adk-code).
metadata	{"author":"Google","license":"Apache-2.0","version":"1.1.0","requires":{"bins":["agents-cli"],"install":"uv tool install google-agents-cli"}}

ADK Observability Guide

Cloud Trace works out of the box — no infrastructure needed. Prompt-response logging and BigQuery Agent Analytics require Terraform-provisioned infrastructure (service account, GCS bucket, BigQuery dataset). Run agents-cli infra single-project --project PROJECT_ID to provision these resources. See references/cloud-trace-and-logging.md for details, env vars, and verification commands. If your project isn't scaffolded yet, see /google-agents-cli-scaffold first.

Order of operations for agent_runtime deployments

For deployment_target = agent_runtime, run agents-cli infra single-project before the first agents-cli deploy. The Terraform module owns the entire Reasoning Engine resource (display_name, service account, deployment spec, env vars), so applying it after a SDK-based deploy creates a state mismatch — Terraform has no record of the SDK-deployed instance and cannot layer env vars onto it without taking ownership of the whole resource.

If you have already run agents-cli deploy, you have two options:

1. Switch to Terraform-managed. Delete the SDK-deployed Reasoning Engine, then run agents-cli infra single-project followed by agents-cli deploy. Sessions and any in-flight state on the previous instance are lost.
2. Keep the SDK-deployed instance. Skip infra single-project and set the observability env vars on the running instance directly via the vertexai client update API. You will also need to grant the instance's service account the IAM permissions required to emit telemetry — writing to the logs GCS bucket, BigQuery dataset access, log writer, etc. See deployment/terraform/single-project/iam.tf and telemetry.tf in your scaffolded project for the full set of bindings the Terraform module would otherwise provision. Terraform-managed env vars are not available in this mode.

Reference Files

File	Contents
references/cloud-trace-and-logging.md	Scaffolded project details — Terraform-provisioned resources, environment variables, verification commands, enabling/disabling locally
references/bigquery-agent-analytics.md	BQ Agent Analytics plugin — enabling, key features, GCS offloading, tool provenance

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Observability Tiers

Choose the right level of observability based on your needs:

Tier	What It Does	Scope	Default State	Best For
Cloud Trace	Distributed tracing — execution flow, latency, errors via OpenTelemetry spans	All templates, all environments	Always enabled	Debugging latency, understanding agent execution flow
Prompt-Response Logging	GenAI interactions exported to GCS, BigQuery, and Cloud Logging	ADK agents only	Disabled locally, enabled when deployed	Auditing LLM interactions, compliance
BigQuery Agent Analytics	Structured agent events (LLM calls, tool use, outcomes) to BigQuery	ADK agents with plugin enabled	Opt-in (--bq-analytics at scaffold time)	Conversational analytics, custom dashboards, LLM-as-judge evals
Third-Party Integrations	External observability platforms (AgentOps, Phoenix, MLflow, etc.)	Any ADK agent	Opt-in, per-provider setup	Team collaboration, specialized visualization, prompt management

Ask the user which tier(s) they need — they can be combined. Cloud Trace is always on; the others are additive.

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Cloud Trace

ADK uses OpenTelemetry to emit distributed traces. Every agent invocation produces spans that track the full execution flow.

Span Hierarchy

invocation
  └── agent_run (one per agent in the chain)
        ├── call_llm (model request/response)
        └── execute_tool (tool execution)

Setup by Deployment Type

Deployment	Setup
Agent Runtime	Automatic — traces are exported to Cloud Trace by default
Cloud Run (scaffolded)	Automatic — setup_telemetry() configures Cloud Trace/Logging exporters
GKE (scaffolded)	Automatic — setup_telemetry() configures Cloud Trace/Logging exporters
Cloud Run / GKE (manual)	Configure OpenTelemetry exporter in your app
Local dev	Works with agents-cli playground; traces visible in Cloud Console

View traces: Cloud Console → Trace → Trace explorer

For detailed setup instructions (Agent Runtime CLI/SDK, Cloud Run, custom deployments), fetch https://adk.dev/integrations/cloud-trace/index.md.

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Prompt-Response Logging

Captures GenAI interactions (model name, tokens, timing) and exports to GCS (JSONL) and BigQuery (via direct log sinks and external tables). Privacy-preserving by default — only metadata is logged unless explicitly configured otherwise.

Key env var: OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT — OTel GenAI semantic-conventions standard (modes: span_only, event_only, span_and_event, no_content). The scaffolded setup_telemetry() collapses every non-false value to NO_CONTENT (metadata-only); false disables capture. Logging is disabled locally unless LOGS_BUCKET_NAME is set.

For scaffolded project details (Terraform resources, env vars, privacy modes, enabling/disabling, verification commands), see references/cloud-trace-and-logging.md.

For ADK logging docs (log levels, configuration, debugging), fetch https://adk.dev/observability/logging/index.md.

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BigQuery Agent Analytics Plugin

Optional plugin that logs structured agent events to BigQuery. Enable with --bq-analytics at scaffold time. See references/bigquery-agent-analytics.md for details.

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Third-Party Integrations

ADK supports several third-party observability platforms. Each uses OpenTelemetry or custom instrumentation to capture agent behavior.

Platform	Key Differentiator	Setup Complexity	Self-Hosted Option
AgentOps	Session replays, 2-line setup, replaces native telemetry	Minimal	No (SaaS)
Arize AX	Commercial platform, production monitoring, evaluation dashboards	Low	No (SaaS)
Phoenix	Open-source, custom evaluators, experiment testing	Low	Yes
MLflow	OTel traces to MLflow Tracking Server, span tree visualization	Medium (needs SQL backend)	Yes
Monocle	1-call setup, VS Code Gantt chart visualizer	Minimal	Yes (local files)
Weave	W&B platform, team collaboration, timeline views	Low	No (SaaS)
Freeplay	Prompt management + evals + observability in one platform	Low	No (SaaS)

Ask the user which platform they prefer — present the trade-offs and let them choose. For setup details, fetch the relevant ADK docs page from the Deep Dive table below.

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Troubleshooting

Issue	Solution
No traces in Cloud Trace	Verify setup_telemetry() runs at startup and the service account has the cloudtrace.agent role
Prompt-response data not appearing	Check LOGS_BUCKET_NAME is set; verify SA has storage.objectCreator on the bucket; check app logs for telemetry setup warnings
Privacy mode misconfigured	Check OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT value — use NO_CONTENT for metadata-only, false to disable
BigQuery Analytics not logging	Verify plugin is configured in app/agent.py; check BQ_ANALYTICS_DATASET_ID env var is set
Third-party integration not capturing spans	Check provider-specific env vars (API keys, endpoints); some providers (AgentOps) replace native telemetry
Traces missing tool spans	Tool execution spans appear under execute_tool — check trace explorer filters
High telemetry costs	Switch to NO_CONTENT mode; reduce BigQuery retention; disable unused tiers

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Deep Dive: ADK Docs (WebFetch URLs)

For detailed documentation beyond what this skill covers, fetch these pages:

Topic	URL
Observability overview	https://adk.dev/observability/index.md
Agent activity logging	https://adk.dev/observability/logging/index.md
Cloud Trace integration	https://adk.dev/integrations/cloud-trace/index.md
BigQuery Agent Analytics	https://adk.dev/integrations/bigquery-agent-analytics/index.md
AgentOps	https://adk.dev/integrations/agentops/index.md
Arize AX	https://adk.dev/integrations/arize-ax/index.md
Phoenix (Arize)	https://adk.dev/integrations/phoenix/index.md
MLflow tracing	https://adk.dev/integrations/mlflow-tracing/index.md
Monocle	https://adk.dev/integrations/monocle/index.md
W&B Weave	https://adk.dev/integrations/weave/index.md
Freeplay	https://adk.dev/integrations/freeplay/index.md

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Related Skills

• /google-agents-cli-deploy — Deployment targets, CI/CD pipelines, and production workflows
• /google-agents-cli-workflow — Development workflow, coding guidelines, and operational rules
• /google-agents-cli-adk-code — ADK Python API quick reference for writing agent code