google-agents-cli-observability

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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).

3installs

Sourcegoogle/agents-cli

Added on2026-04-22

NPX Install

npx skill4agent add google/agents-cli google-agents-cli-observability

SKILL.md Content

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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:

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.
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

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.

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 — `otel_to_cloud=True` in the FastAPI app
GKE (scaffolded)	Automatic — `otel_to_cloud=True` in the FastAPI app
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

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

— set to

NO_CONTENT

(metadata only, default in deployed envs),

true

(full content), or

false

(disabled). 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

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.

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.

Troubleshooting

Issue	Solution
No traces in Cloud Trace	Verify `otel_to_cloud=True` in FastAPI app; check service account has `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

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`

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