/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: 1.3.1
  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 (service account, deployment spec, env vars), so applying it after an SDK-based deploy creates a state mismatch Terraform can't reconcile without taking ownership of the whole resource.

Already ran `agents-cli deploy`? Two options:

1. **Switch to Terraform-managed** — delete the SDK-deployed Reasoning Engine, then run `agents-cli infra single-project` and `agents-cli deploy` (sessions and in-flight state are lost). 2. **Keep the SDK-deployed instance** — skip `infra single-project` and set the observability env vars by re-running `agents-cli deploy --update-env-vars "KEY=VALUE,..."`; deploy matches the existing Reasoning Engine by display name and updates it in place, preserving env vars set outside the deploy. You must also grant its service account the telemetry IAM roles the Terraform module would otherwise provision: `roles/storage.admin` (write completions to the logs bucket), `roles/logging.logWriter`, `roles/cloudtrace.agent`, plus `roles/bigquery.dataOwner` + `roles/bigquery.jobUser` when scaffolded with `--bq-analytics`. The full set lives in `deployment/terraform/single-project/iam.tf` (from `app_sa_roles`) and `telemetry.tf`. Terraform-managed env vars aren't 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.

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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

invoke_workflow (top-level run)
  └── invoke_agent (one per agent in the chain)
        ├── call_llm (model request)
        │     └── generate_content (underlying GenAI model call)
        └── execute_tool (tool execution)

Setup by Deployment Type

| Deployment | Setup | |-----------|-------| | **Agent Runtime** | Automatic — `get_fast_api_app(otel_to_cloud=True)`, gated on `GOOGLE_CLOUD_AGENT_ENGINE_ENABLE_TELEMETRY` (set by deploy); exports to Cloud Trace/Logging + Agent Engine console | | **Cloud Run / GKE (scaffolded)** | Automatic — `get_fast_api_app(otel_to_cloud=True)` exports to Cloud Trace/Logging | | **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 and exports to GCS (JSONL) and BigQuery (via log sinks + external tables). Content is governed by **two independent tiers**; the net Terraform-deploy default is **full content in GCS/BigQuery, none in traces**:

| Tier | Captures | Controlled by | Default (Terraform deploy) | |------|----------|---------------|----------------------------| | **GCS/BigQuery completions** | Full prompts/responses (the prompt-response logging f