Gemini Enterprise Agent Platform Updates & Release Notes

Feature

Gemini Enterprise: Feedback service (Preview)

The Feedback service on Gemini Enterprise Agent Platform is available in Preview. The Feedback service lets you collect, analyze, and manage end-user qualitative feedback, such as thumbs-up and thumbs-down sentiments and feedback labels from interactions between your agents and users. You can also view feedback entries alongside traces in the console or export them to Cloud Trace to troubleshoot your agents.

For more information, see Feedback service overview.

Feature

Gemini 3.6 Flash and 3.5 Flash-Lite are generally available (GA)

Gemini 3.6 Flash and Gemini 3.5 Flash-Lite are now generally available (GA) and available for production use. These models are designed to improve upon their predecessors' capabilities, including improved token usage and improved document understanding. See the linked model information pages for more information.

This release includes some potentially breaking changes from previous Flash and Flash-Lite models:

• Sampling parameters: Custom values for temperature, top-K, and top-P are not supported and will be ignored if set.
• Penalty parameters: Custom values for frequency and presence penalty parameters are not supported. Setting these will result in an API error.
• API turn structure: API requests where the last input turn has a role of Model are no longer supported and will return an error:
  • Interactions API: Requests where the last object in the input array has "type": "model_output" will fail.
  • GenerateContent API: Requests where the last object in the contents array has "role": "model" will fail.

Deprecated

Open model endpoint deprecations

The following open model endpoints are deprecated and will be retired on October 21, 2026. For more information, see Open model deprecations.

• deepseek-ocr-maas
• deepseek-r1-0528-maas
• deepseek-v3.2-maas
• deepseek-v3.1-maas
• glm-5-maas
• glm-4.7-maas
• gpt-oss-20b-maas
• kimi-k2-thinking-maas
• llama-3.3-70b-instruct-maas
• minimax-m2-maas
• multilingual-e5-large-instruct-maas
• multilingual-e5-small-maas
• qwen3-235b-a22b-instruct-2507-maas
• qwen3-coder-480b-a35b-instruct-maas
• qwen3-next-80b-a3b-instruct-maas
• qwen3-next-80b-a3b-thinking-maas

Security

Security update for Server-Side Request Forgery (SSRF) in Agent Studio

This release fixes a Server-Side Request Forgery (SSRF) vulnerability in the auto-generated /api-proxy backend endpoint for web applications created before July 1, 2026, using Agent Studio.

If you downloaded, generated, or deployed web application code from Agent Studio before July 1, 2026, regenerate the app from Agent Studio and deploy the new version. For more information, see Quickstart: Deploy your Agent Studio prompt as a web application

The updated backend code includes strict domain allowlist validation, ensuring that destination hostnames for the /api-proxy endpoint end with allowed Google Cloud domains, such as *-aiplatform.clients6.google.com

Deprecated

Grok 4.1 models deprecation

The Grok 4.1 model family (including xai/grok-4.1-fast-reasoning and xai/grok-4.1-fast-non-reasoning) on the Gemini Enterprise Agent Platform is deprecated and will be shut down on August 20, 2026. After this date, Google Agent Platform Model as a Service (MaaS) will no longer serve these models. Specifically, API requests directed to the https://aiplatform.googleapis.com/v1/projects//locations/global/endpoints/openapi/chat/completions endpoint using the Grok 4.1 model IDs will fail and return a 400 error. To maintain service, migrate your applications to newer xAI models (such as Grok 4.2 or Grok 4.3) or choose an alternative model from the Google Cloud Model Garden.

Feature

Memory Bank support for Gemini Embedding 2

Memory Bank supports the gemini-embedding-2 model for similarity search configurations.

When you configure gemini-embedding-2 as the embedding model, you must use one of the global, us, or eu endpoints in the model's resource name (for example, projects/{project}/locations/us/publishers/google/models/gemini-embedding-2). Memory Bank does not support using regional locations (for example, us-central1) for gemini-embedding-2.

For details, see Similarity search configuration.

Feature

Memory Bank IngestEvents is generally available (GA)

The Memory Bank IngestEvents API is generally available. The IngestEvents API decouples event ingestion from memory generation, letting you continuously stream content to Memory Bank and configure when memory generation is triggered.

This GA release includes the following features:

• Carry over context between generation windows: Use the overlap_event_count parameter to re-include already-processed events at the start of the next window to keep memories coherent.
• Configure memory revisions for ingested events: Use revision_labels, revision_ttl, or disable_memory_revisions to customize how generated revisions are managed.
• Attach metadata to memories: Use the metadata and metadata_merge_strategy configuration parameters to store structured information alongside generated memories.

For details, see Ingest events.

Feature

AlphaGenome released for Gemini Enterprise Agent Platform

AlphaGenome, Google DeepMind's state-of-the-art genomics foundation model, is now available for deployment and use with Gemini Enterprise Agent Platform.

Designed to decipher the functional regulatory code of the human genome, AlphaGenome analyzes large-scale DNA sequences at single-base resolution to predict how genetic variations affect molecular and biological mechanisms like gene expression, chromatin accessibility, and RNA splicing. For information on how to use AlphaGenome in Agent Platform, see the documentation.

Feature

Gemini 3.5 Flash default model for Memory Bank

The default model used for Memory Bank generation is now Gemini 3.5 Flash instead of Gemini 2.5 Flash. For information, see Set up Memory Bank.

Feature

Semantic Governance Policies available in Public Preview

Semantic Governance Policies (SGP) and the SGP engine are now available in Preview. SGP provides an intelligent security and compliance layer that evaluates an AI agent's proposed tool calls against user intent and organizational business rules at runtime.

Key capabilities include:

• Natural Language Constraints (NLC): Author declarative business rules and security guardrails in plain English without needing to write code or redeploy agent applications.
• Layered Intent Gating: Intercepts agent tool calls at runtime to verify alignment with trusted user intent and prevent unauthorized actions, rogue tool use, and data exfiltration.
• Granular Scoping: Apply constraints globally across all tools for an agent or target specific tools and parameters (e.g., enforcing strict financial limits or geographic restrictions).
• Agent Skills Lifecycle Governance: Protects agents from context poisoning and supply-chain exploits by governing the dynamic loading of Agent Skills (tool packages) during sessions.
• Dry Run Mode: Test and observe policy verdicts in Log Explorer before enforcing them on active traffic.

For more information, see Semantic governance policies overview.

Feature

Provisioned Throughput: Email notifications GA

The ability to get email notifications for Provisioned Throughput events is generally available. See Get email notifications.

Feature

New Provisioned Throughput features

The following Provisioned Throughput features are generally available:

• Change an order: See Change a standard Provisioned Throughput order.
• Schedule a new order: Set a start date and time when you create an order. See Place a standard Provisioned Throughput order.
• Schedule a change to an order: Set a start date and time when you change an order. See Change a standard Provisioned Throughput order.
• Split an order: Divide an active order into two orders. See Split an order.

Feature

AI security findings in Agent Platform

Viewing AI security findings and posture management summaries in Gemini Enterprise Agent Platform is generally available.

With this release, the Security dashboard introduces the Top security findings widget.

Also, specific features within the AI security widgets are available in Preview, including the following:

• Vulnerability findings and threat monitoring for agent runtimes (such as Cloud Run)
• Historical content violation trends (Violations over time)

For more information, see View security findings.

Feature

Model Armor for Agent Gateway in General Availability

You can enable Model Armor on an Agent Gateway resource to apply your organization's content security guardrails to prompts and responses that pass through the gateway. This feature is generally available (GA).

For more information, see Configure Model Armor on a gateway.

Change

Supervised fine-tuning available for Gemini 3.1 Flash Lite and Gemini 3.5 Flash in Public Preview

Supervised fine-tuning is now available for the gemini-3.1-flash-lite and gemini-3.5-flash models in Preview. Model tuning for Gemini 3.1 Flash Lite and Gemini 3.5 Flash is restricted to us-central1 and europe-west4 and tuned model serving is restricted to the us and eu multi-region endpoints.

See About supervised fine-tuning for more information.

Feature

Provisioned Throughput support for supervised fine-tuned Gemini 3 model inference.

Provisioned Throughput can be used to assure supervised fine-tuned inference using the same quota. Supervised fine-tuned inference for Gemini 3 models incurs a higher burndown rate compared to base model inference. Learn more here.