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10 · Engineering · Observability

Google Agent Alert Configuration

Plan Terraform and telemetry-based alerts for agent reliability and supported quality signals.

agentenalarmierungagent-platform-alert-configuration

Google Agent Alert Configuration · Original SKILL.md

---
name: agent-platform-alert-configuration
metadata:
version: "1.0.0"
category: AiAndMachineLearning
description: >-
Configures best-practice alerting policies for AI agents using OpenTelemetry
(OTel) metrics, generating output as Terraform (.tf) configuration files.
Use when analyzing, writing, or deploying alerting policies
to monitor agent latency, error rates, token usage, and quality metrics.
Don't use for standard infrastructure monitoring unrelated to AI agents,
or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts).
NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics
and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely
on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.
allowed-tools: terraform gcloud python
---

# Agent Platform Alert Configuration

## Critical Steps

### 1. Safety & Confirmation Tiers (CRITICAL)

Before executing any commands or writing configurations on behalf of the user,
you MUST adhere to the following safety tiers based on the action requested:

1. **Tier R: Read-only (`check_telemetry.py` / `gather_agent_info.py`)**
* **Rule**: No confirmation needed. You may execute these scripts
immediately to inspect telemetry status or gather agent configuration
details.
2. **Tier B: Billing & Resource Creation (`create_online_monitor.py` /
provisioning)**
* **Rule**: **Explicit User Confirmation Required**. These actions incur
additional billing charges and create cloud resources. The agent MUST
ALWAYS warn the user explicitly about the potential extra billing costs
of BOTH the Online Monitor (specifically mentioning **LLM evaluations**)
and Telemetry (specifically mentioning **Cloud Trace/Cloud Logging
export**). You MUST STOP and ask for explicit approval before proceeding
with provisioning or providing setup commands.

### 2. Prerequisites & Dependencies

#### Agent Telemetry

* **Disclaimer**: For Reliability, Cost, Safety, and Security alerts to
function, the underlying agent MUST be instrumented to emit OpenTelemetry
(OTel) metrics. If the agent does not emit these metrics, the alerting
policies will have no data stream to evaluate.

#### Python Environment

Before executing any python script in this skill you MUST install the required
dependencies in your environment. Run this command first:

```bash
pip install -r scripts/requirements.txt
```

### 3. Input Assumptions

* **Explicit Project Adherence**: You must ONLY configure alerts, query
telemetry, or interact with the Google Cloud Project(s) explicitly provided
by the user in the prompt. Do NOT assume or use other projects from your
environment or history unless the user explicitly directs you to do so.
* **Sequential File Transformations**: If the user explicitly asks to copy a
file and then modify it, you MUST perform these actions sequentially (copy
first, then modify) rather than writing the final content directly.

### 4. Execution Steps

1. **Mandatory Prerequisite Execution Protocol (SEQUENTIAL)**: Before
generating or writing ANY configuration, you MUST execute these steps in
order:
1. **Step 1: Streamlined Discovery (Mandatory)**: Run
`gather_agent_info.py` to automatically identify agent runtime, verify
telemetry, metric scopes, linked datasets, and more. This script covers
most of the manual verifications listed in subsequent steps.
* Command: `python3 scripts/gather_agent_info.py --project-id
{project_id} --agent-name {agent_name}`
* **Note**: If this script **fails**, returns **partial data**, or
doesn't produce everything you need, you MUST satisfy requirements
by running the manual fallback steps listed in Step 2 and then
perform Step 3 below. If Step 1 succeeds and provides all info,
**SKIP** to Step 3 (Pre-existing Policies Verification).
2. **Step 2: Metric Scope Verification (Fallback)**: Run this ONLY if Step
1 failed to determine the metric scope.
* **Action A (CLI)**: Run `gcloud beta monitoring metrics-scopes list
projects/{project_id}`. If a scoping project is returned, you MUST
deploy policies there.
* **Action B (Code Scan)**: Search Terraform configurations for
`google_monitoring_monitored_project` resources to extract the
scoping project.
* **Action C (Fallback)**: If ambiguous, ASK the user: "Are you using
a multi-project Cloud Monitoring Metric Scope? If so, what is the
scoping project ID?"
3. **Step 3: Pre-existing Policies Verification**: Avoid duplicates.
* **Action**: Scan the target directory to see if aggregated policies
already exist targeting the same metrics (grouped by
`reasoning_engine_id` or `gen_ai_agent_name`). Use
`scan_duplicates.py` to verify.
2. **Alert Policy Type Resource Files**: You MUST list and read files under
`references/` with names ending in `_alert_policies.md` to learn how to
configure alert policies based on type. By default you MUST configure all of
the following alert types UNLESS the user requests to generate explicit
alert policies and/or types. Follow their tables of content to help you find
the reference sections you need to read:

Alert Type | Reference File
:-------------- | :-------------
**Reliability** | [reliability_alert_policies.md](references/reliability_alert_policies.md)
**Quality** | [quality_alert_policies.md](references/quality_alert_policies.md)
**Cost** | [cost_alert_policies.md](references/cost_alert_policies.md)
**Safety** | [safety_alert_policies.md](references/safety_alert_policies.md)
**Security** | [security_alert_policies.md](references/security_alert_policies.md)

### 5. Outputs & Formats

* **Always configure the supported alerting policies** for the target agent:
* **For Reliability Monitoring**: You MUST configure exactly five alerting
policies:
1. **Latency** (anomaly monitoring)
2. **Error Rate - Fast Burn SLO** (1-Hour Window)
3. **Error Rate - Slow Burn SLO** (3-Day Window)
4. **Model Call Error Rate** (SQL-based Observability Analytics
Alerting)
5. **Tool Call Error Rate** (SQL-based Observability Analytics
Alerting)
* **For Quality Monitoring**: You MUST configure exactly three alerting
policies (Requires Vertex AI Online Monitors):
1. **Final Response Quality**
2. **Tool Use Quality**
3. **Hallucination**
* **For Cost Monitoring**: You MUST configure exactly one cost alerting
policy:
1. **Rapid Token Burn Rate** (anomaly monitoring)
* **For Safety Monitoring**: You MUST configure exactly one safety
alerting policy:
1. **High Model Armor Safety Policy Trigger Rate** (SQL-based
Observability Analytics Alerting)
* **For Security Monitoring**: You MUST configure exactly one security
alerting policy:
1. **High IAM Permission Denied Trigger Rate** (SQL-based Observability
Analytics Alerting)
* **Terraform Only**: Write the generated observability configuration ONLY as
Terraform (`.tf`) files (such as `alerts.tf`, `variables.tf`).
- You **ONLY** need to install Terraform if you're asked to deploy the
alerts AND there is no valid Terraform install. SQL-based alerting using
`condition_sql` requires the provider version **>= 6.0.0** (or late 5.x
versions supporting the feature).
- If you are **NOT** asked to deploy the alerts you do not need to install
terraform.
* **Dynamic Multi-Resource Alerting (No Single-Resource Pinning)**: You MUST
NOT hardcode specific agent IDs or resource name filters (for example,
`{gen_ai_agent_name="{agent_name}"}` or
`metric.labels.agent_resource_name="{agent_name}"`) in alerting conditions
unless explicitly requested (for example, "ONLY for this agent"). Merely mentioning
a specific agent name or ID in the request does NOT constitute an explicit
request to pin/filter; you MUST still default to dynamic grouping to cover
all agents. To cover all active agents in the project dynamically:

*Good Example (PromQL Grouping):*

```promql
sum(rate(workload_googleapis_com:gen_ai_invoke_agent_duration_count{monitored_resource="generic_node"}[5m])) by (gen_ai_agent_name)
```

*Bad Example (PromQL Hardcoded Filter):*

```promql
sum(rate(workload_googleapis_com:gen_ai_invoke_agent_duration_count{monitored_resource="generic_node", gen_ai_agent_name="support-bot"}[5m]))
```

* **For Reliability Metrics using PromQL**: ALWAYS use grouping
aggregations. Group by `gen_ai_agent_name` (for example, `by
(gen_ai_agent_name)`). Avoid filtering to a single ID/Name unless
requested.
* **For Quality Metrics using Standard Threshold Filters**: Omit the
`agent_resource_name` filter entirely. Configure the condition filter to
only target the monitored resource type
(`aiplatform.googleapis.com/OnlineEvaluator`) and metric type
(`aiplatform.googleapis.com/online_evaluator/scores`) globally for the
project.

*Good Example (SQL Grouping):*

```sql
SELECT
JSON_VALUE(resource.attributes, '$."cloud.resource_id"') as agent_id,
...
FROM ...
GROUP BY agent_id
```

*Bad Example (SQL Hardcoded Filter):*

```sql
SELECT ...
FROM ...
WHERE JSON_VALUE(resource.attributes, '$."cloud.resource_id"') = 'support-bot'
```

* **For Downstream Calls using SQL**: Omit the `ENDS_WITH` filter
targeting a specific agent name. Instead, extract the agent identifier
(for example, `JSON_VALUE(resource.attributes, '$."cloud.resource_id"')`) and
add it to the `GROUP BY` clause alongside the model or tool name.
* **Directory Inference**: Prefer the path explicitly provided by the user (if
any). Otherwise, deploy configuration files to target Terraform or SRE
folders (such as `monitoring/`, `ops/`, `sre/`). Use tools to locate where
alert policies or state pointers exist in the project, rather than blindly
writing to the root.
* **Notification Channels**: By default, never configure any notification
channels without user input. If the user explicitly provides a notification
channel in their prompt, configure the alerts to use it. If no notification
channel is provided, you MUST explicitly ask the user in your final response
if they would like to configure notification channels. **This is a mandatory
question and you MUST NOT omit it from your response.** **IMPORTANT** Do NOT
make assumptions about notification channels. If you search the codebase for
a notification channel you must ALWAYS confirm with the user before using
it.
* **Plain English Response**: You MUST include a plain English explanation for
what the alerts do in your response. This must explain in plain English what
the alert measures, how the algorithm works, and what a trigger indicates.

### 6. Output Verification

* **Background Task Cleanup**: You MUST verify the status of all background
tasks that you spawn. Before completing your execution and returning your
final response, you MUST terminate or kill any active or hanging background
tasks (using the `manage_task` tool with action `kill`).
* **Validate Configuration**: Run the **Config Linting** tool to make sure all
the output files are written with the correct grammar and structure. See
details about the tool in the `Tooling Scripts` section below.

## Tooling Scripts

Use the following scripts to discover agents, gather configuration details,
resolve duplicates, and validate configs:

1. **Agent Information Gathering**: Streamlines discovery, environment auditing
(Metric Scopes, BQ Datasets, Notification Channels), table derivations (Log
& Trace), and Online Evaluator verifications.
* Command: `python3 scripts/gather_agent_info.py --project-id {project_id}
--agent-name {agent_name}`
2. **Duplicate Verification & Merge**: Verifies pre-existing alerts in the
target folder to ensure changes are merged in-place rather than appended:
* Command: `python3 scripts/scan_duplicates.py {target_tf_dir}
--engine-var '${var.gen_ai_agent_name}'`
3. **Config Linting**: Validates PromQL grammar, matching engine labels, and
HCL structure:
* Command: `python3 scripts/lint_syntax.py {path_to_tf_file}`
* **Self-Correction Loop**: If validation fails (exits non-zero or outputs
errors), you MUST read the command output, locate the line/file
containing the lint error, analyze the PromQL syntax or Terraform HCL
issue, apply adjustments in-place, and re-run the `lint_syntax.py`
validation. Repeat this loop until the validation script passes
successfully.

## Gotchas & Behavioral Corrections

* **Raw Error Boundaries**: Explain that raw error counts or absolute failed
request count boundaries do not scale under changing traffic throughput.
Recommend ratio-based error rate alerts instead.
* **Safe Threshold Modulation E2E Validation**: When verifying a dynamic
metric threshold policy end-to-end, do NOT attempt to force real platform
errors. Instead, deploy the alert policy with standard safe bounds (Z-score
multiplier > 15), then temporarily update standard deviation Z-score limits
to a negative value (for example, > -3) to trigger/verify the "Firing" state before
reverting. Always get confirmation before taking this action proactively.
* **Expected Script Failures**:
* `scan_duplicates.py` exiting with code 1: Parse the JSON
output for duplicate resource targets. Perform in-place upgrade edits,
then re-check until it passes with 0.
* **Avoid Redundant Discovery Calls**: If `gather_agent_info.py`
successfully returns the Trace or Log table names (or writes them to
variables file), do NOT redundantly call
`list_trace_scope_table_names.py` or `list_log_scope_table_names.py`.
These scripts are run internally by `gather_agent_info.py` and are
provided as external Fallbacks only.
* **Script Execution Failures & Self-Correction**: If the execution of
utility scripts (such as `gather_agent_info.py`, `check_telemetry.py`,
`create_online_monitor.py`, `analyze_traffic.py`,
`list_log_scope_table_names.py`, or `list_trace_scope_table_names.py`)
fails unexpectedly, you MUST read and inspect the stdout/stderr logs or
error output. Analyze the error message and attempt to dynamically
correct parameters and retry execution before escalating or
falling back to manual plans. Consult the relevant domain-specific
reference file for detailed troubleshooting steps for specific scripts.
* **Distribution Metric Aligner Constraint**: Standard `ALIGN_MEAN` cannot be
applied to `DELTA` distribution metrics like `online_evaluator/scores`. You
MUST use percentile-based aligners (like `ALIGN_PERCENTILE_50`) to reduce
the score distribution into a comparable numeric stream.
* **HCL Heredoc Interpolation**: When referencing Terraform variables inside
PromQL or SQL queries (which are defined as strings), you MUST use the
${var.variable_name} syntax. Bare references like var.variable_name will
fail at deployment time.
* **Avoid Recursive Directory Operations**: You MUST NOT run recursive listing
or search commands (such as `ls -R`, `find .`, or raw recursive `grep`) from
the repository root if it contains a very large number of files, as this
will freeze your session. Always target specific subdirectories.

## Supporting Links

* [Continuous evaluation with online monitors](https://docs.cloud.google.com/gemini-enterprise-agent-platform/optimize/evaluation/evaluate-online)
* [Agent Platform Quality Metrics](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/rubric-metric-details)
* [Google Cloud Alerting Policies Guide](https://docs.cloud.google.com/monitoring/alerts)
* [Google Cloud Monitoring PromQL Documentation](https://docs.cloud.google.com/monitoring/promql)

When to use

Plan Terraform and telemetry-based alerts for agent reliability and supported quality signals.

What you get

Complete original guidance with source attribution and declared limitations.

How it works

Clarify the actual task, inspect available evidence and plan the relevant source workflow within authorized scope.

Requirements

Agent environment, telemetry availability, alert thresholds and notification ownership.

Delivery and review notes

Thresholds are starting assumptions. Do not assert that alerts are installed or quality telemetry exists without actual evidence. Referenced scripts, templates, packages and remote documentation are not bundled or installed. Brief obvious-danger screening only; no functional test or comprehensive security certification.

Starting prompt

Use Google Agent Alert Configuration for [TASK]. Clarify Agent environment, telemetry availability, alert thresholds and notification ownership. Identify missing dependencies and assumptions. Loading this guidance grants no permission to change resources, transfer data or incur charges.

Not for

Automatic cloud execution, bypassing resource-owner permissions, or treating untested examples as deployed and verified results.

What M11 added

German task routing and precise scope. Original attribution: Google (google/skills repository). Thresholds are starting assumptions. Do not assert that alerts are installed or quality telemetry exists without actual evidence. Referenced scripts, templates, packages and remote documentation are not bundled or installed. Brief obvious-danger screening only; no functional test or comprehensive security certification.

Original authorship remains with google/skills · Original source ↗

Original license & copyright

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Brand Voice Guide Design

Content Pipeline Orchestration

Website Brand Profile

Buyer Objection Clarification

Klaviyo Integration Guidance

Offer Bonus Planning

Klaviyo Marketing Review

Cross-Platform Advertising Review

Expertise Business Model

LinkedIn Advertising Guidance

TikTok Advertising Guidance

Evidence Research Brief

Cinematic Video Prompts

Visual Prompt Model Adaptation

Visual Prompt Diagnostics

Short Film Development

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