release v1.1.0 at 2026-07-10 18:54:45 UTC

Author: eliasecchig

.claude-plugin/plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "google-agents-cli",
-  "version": "1.0.0",
+  "version": "1.1.0",
   "description": "Scaffold, develop, evaluate, and deploy AI agents with Google ADK. Bundles 7 skills for the agent development lifecycle.",
   "author": { "name": "Google LLC" },
   "homepage": "https://github.com/google/agents-cli",

RELEASE_NOTES.md

@@ -2,6 +2,15 @@
 
 All notable changes to this project will be documented in this file.
 
+## [1.1.0] - 2026-07-10
+
+- **Guided brainstorming for new agents.** The workflow skill's Phase 0 is now an interactive brainstorming dialogue that helps you shape an agent's spec before any code is written, and surfaces its assumptions for review when it can't ask.
+- `eval generate` and `eval grade` no longer clutter output with benign third-party warnings and progress bars that never affected results. This output is still shown on failure and can be re-enabled for debugging.
+- Generated projects now name the default scaffolded eval metric module `tests/eval/response_quality.py` (was `metrics.py`) to match the metric it implements.
+- Broad Windows compatibility fixes across the CLI.
+- Fixed a command-name typo in user-facing hints so they point to the correct `agents-cli` command.
+- Refreshed the bundled skills, including pointing the RAG samples at `core/python/` in `google/adk-samples`.
+
 ## [1.0.0] - 2026-06-30
 
 **Agents CLI is now 1.0 — Generally Available.** This is our first GA release: a stable, production-ready CLI for scaffolding, evaluating, and deploying ADK agents on Google Cloud.

gemini-extension.json

@@ -1,5 +1,5 @@
 {
   "name": "google-agents-cli",
-  "version": "1.0.0",
+  "version": "1.1.0",
   "description": "Scaffold, develop, evaluate, and deploy AI agents with Google ADK. Bundles 7 skills for the agent development lifecycle."
 }

skills/google-agents-cli-adk-code/SKILL.md

@@ -12,7 +12,7 @@ description: >
 metadata:
   author: Google
   license: Apache-2.0
-  version: 1.0.0
+  version: 1.1.0
   requires:
     bins:
       - agents-cli

skills/google-agents-cli-deploy/SKILL.md

@@ -4,15 +4,15 @@ description: >
   This skill should be used when the user wants to "deploy an agent",
   "deploy my ADK agent", "set up CI/CD", "configure secrets",
   "troubleshoot a deployment", or needs guidance on Agent Runtime,
-  Cloud Run, or GKE deployment targets.
+  Cloud Run, or GKE deployment targets, or awareness of Agent Gateway.
   Covers deployment workflows, service accounts, rollback, and production infrastructure.
   Part of the Google ADK (Agent Development Kit) skills suite.
   Do NOT use for API code patterns (use google-agents-cli-adk-code), evaluation
   (use google-agents-cli-eval), or project scaffolding (use google-agents-cli-scaffold).
 metadata:
   author: Google
   license: Apache-2.0
-  version: 1.0.0
+  version: 1.1.0
   requires:
     bins:
       - agents-cli
@@ -47,7 +47,6 @@ Choose the right deployment target based on your requirements:
 
 | Criteria | Agent Runtime | Cloud Run | GKE |
 |----------|-------------|-----------|-----|
-| **Languages** | Python | Python | Python (+ others via custom containers) |
 | **Scaling** | Managed auto-scaling (configurable min/max, concurrency) | Fully configurable (min/max instances, concurrency, CPU allocation) | Full Kubernetes scaling (HPA, VPA, node auto-provisioning) |
 | **Networking** | VPC-SC and PSC-I supported (private VPC connectivity via network attachments) | Full VPC support, direct VPC egress, IAP, ingress rules | Full Kubernetes networking |
 | **Session state** | Native `VertexAiSessionService` (persistent, managed) | In-memory (dev), Cloud SQL, or Agent Platform Sessions backend | In-memory (dev), Cloud SQL, or Agent Platform Sessions backend |
@@ -58,6 +57,8 @@ Choose the right deployment target based on your requirements:
 
 **Ask the user** which deployment target fits their needs. Each is a valid production choice with different trade-offs.
 
+All three targets are container-based, so any language works.
+
 > **Product name mapping:** "Agent Engine" / "Vertex AI Agent Engine" is now **Agent Runtime**. Use `--deployment-target agent_runtime`.
 
 > **Ambient / scheduled / event-driven agents:** ADK's `trigger_sources` registers `/apps/{app}/trigger/*` endpoints on the same FastAPI app for **all** targets. On **Cloud Run** / **GKE** these are public HTTP routes you point a Pub/Sub push subscription or Eventarc trigger at; on **Agent Runtime** the same routes are reachable through the Agent Engine `/api` passthrough (e.g. `.../reasoningEngines/v1/{resource}/api/apps/{app}/trigger/pubsub`). Cloud Run remains the simplest target for unauthenticated trigger sources. See `/google-agents-cli-adk-code` (`references/adk-python.md`, section "12. Event-Driven / Ambient Agents") for the `trigger_sources` pattern.
@@ -359,6 +360,31 @@ For registering deployed agents with Gemini Enterprise, see `/google-agents-cli-
 
 ---
 
+## Agent Gateway & Semantic Governance (Gemini Enterprise Agent Platform)
+
+> **Note:** There are no `agents-cli` commands for these yet. Deploy your agent as usual
+> with `agents-cli deploy`, then configure Agent Gateway and Semantic Governance separately
+> (via Terraform or the Cloud Console).
+
+**Agent Gateway** is the networking + security entry/exit point for all agent interactions
+(user↔agent, agent↔tool, agent↔agent) — it centralizes access control and governed
+connectivity (ingress/egress). It is not a deployment target. Deploy your agent with
+`agents-cli deploy`, then attach it to a gateway one of two ways:
+
+- **Add the existing agent to a gateway** via the Console / docs flow — see [Route Agent Runtime traffic through Agent Gateway](https://docs.cloud.google.com/gemini-enterprise-agent-platform/scale/runtime/agent-gateway-runtime-deploy) ("For existing agents").
+- **Manage the gateway in Terraform** with [`google_network_services_agent_gateway`](https://registry.terraform.io/providers/hashicorp/google/latest/docs/resources/network_services_agent_gateway) (currently in the `google-beta` provider) — recommended for production, so `agents-cli deploy` owns the agent version and CI/CD handles updates.
+
+Background: [Agent Gateway overview](https://docs.cloud.google.com/gemini-enterprise-agent-platform/govern/gateways/agent-gateway-overview).
+
+**Semantic Governance Policies (SGP)** add a natural-language security/compliance layer
+that keeps an agent's tool invocations aligned with user intent and organizational
+constraints.
+
+- Overview: https://docs.cloud.google.com/gemini-enterprise-agent-platform/govern/policies/semantic-governance-overview
+- Configure: https://docs.cloud.google.com/gemini-enterprise-agent-platform/govern/policies/configure-semantic-governance
+
+---
+
 ## Related Skills
 
 - `/google-agents-cli-workflow` — Development workflow, coding guidelines, and operational rules

skills/google-agents-cli-eval/SKILL.md

@@ -11,7 +11,7 @@ description: >
 metadata:
   author: Google
   license: Apache-2.0
-  version: 1.0.0
+  version: 1.1.0
   requires:
     bins:
       - agents-cli

skills/google-agents-cli-eval/references/metrics-guide.md

@@ -44,7 +44,7 @@ Custom metrics are declared in `eval_config.yaml` (or `.json`) under `custom_met
 
 Code-based metrics default to **local in-process execution** (no GCP project or region required); opt into the Vertex AI sandbox with `execution: "remote"`.
 
-> **Scaffolded default metric.** The scaffolded `eval_config.yaml` ships `custom_response_quality` as a local LLM-judge in `tests/eval/metrics.py` (referenced via `custom_function_file`, run in-process via `google-genai`). It grades on either backend — `genai.Client()` uses `GEMINI_API_KEY` (AI Studio) or ADC (Vertex) — and reads each case's `reference` (ground truth) when present. To grade with the managed Vertex eval service instead, replace it with a built-in metric or an `LLMMetric` (`prompt_template`).
+> **Scaffolded default metric.** The scaffolded `eval_config.yaml` ships `custom_response_quality` as a local LLM-judge in `tests/eval/response_quality.py` (referenced via `custom_function_file`, run in-process via `google-genai`). It grades on either backend — `genai.Client()` uses `GEMINI_API_KEY` (AI Studio) or ADC (Vertex) — and reads each case's `reference` (ground truth) when present. To grade with the managed Vertex eval service instead, replace it with a built-in metric or an `LLMMetric` (`prompt_template`).
 
 ### Example
 

skills/google-agents-cli-observability/SKILL.md

@@ -13,7 +13,7 @@ description: >
 metadata:
   author: Google
   license: Apache-2.0
-  version: 1.0.0
+  version: 1.1.0
   requires:
     bins:
       - agents-cli

skills/google-agents-cli-publish/SKILL.md

@@ -15,7 +15,7 @@ description: >
 metadata:
   author: Google
   license: Apache-2.0
-  version: 1.0.0
+  version: 1.1.0
   requires:
     bins:
       - agents-cli
@@ -197,6 +197,6 @@ Docs: https://docs.cloud.google.com/agent-registry/manage-agents
 
 ## Related Skills
 
-- `/google-agents-cli-deploy` — Deployment targets, CI/CD pipelines, and production workflows
+- `/google-agents-cli-deploy` — Deployment targets, CI/CD pipelines, and production workflows (also covers Agent Gateway governed ingress/egress and Semantic Governance awareness)
 - `/google-agents-cli-workflow` — Development workflow, coding guidelines, and operational rules
 - `/google-agents-cli-scaffold` — Project creation and enhancement with `agents-cli scaffold create` / `scaffold enhance`

skills/google-agents-cli-scaffold/SKILL.md

@@ -12,7 +12,7 @@ description: >
 metadata:
   author: Google
   license: Apache-2.0
-  version: 1.0.0
+  version: 1.1.0
   requires:
     bins:
       - agents-cli