add google AI

Author: micheleRP

modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc

@@ -19,16 +19,16 @@ After completing this guide, you will be able to:
 == Prerequisites
 
 * Access to the Redpanda Cloud Console with administrator privileges
-* API keys for at least one LLM provider (OpenAI or Anthropic)
+* API keys for at least one LLM provider (OpenAI, Anthropic, Google AI)
 * (Optional) MCP server endpoints if you plan to use tool aggregation
 
 == Enable a provider
 
-Providers represent upstream services (Anthropic, OpenAI) and associated credentials. Providers are disabled by default and must be enabled explicitly by an administrator.
+Providers represent upstream services (Anthropic, OpenAI, Google AI) and associated credentials. Providers are disabled by default and must be enabled explicitly by an administrator.
 
 . In the Redpanda Cloud Console, navigate to *AI Gateway* → *Providers*.
-. Select a provider (for example, Anthropic or OpenAI).
-. On the *Configuration* tab for the provider, click *Add configuration*.
+. Select a provider (for example, Anthropic).
+. On the Configuration tab for the provider, click *Add configuration*.
 . Enter your API Key for the provider.
 +
 TIP: Store provider API keys securely. Each provider configuration can have multiple API keys for rotation and redundancy.

modules/ai-agents/pages/ai-gateway/builders/connect-your-agent.adoc

@@ -8,7 +8,7 @@
 
 include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
 
-This guide shows you how to connect your AI agent or application to a Redpanda AI Gateway. You'll configure your client SDK, make your first request, and validate the integration.
+This guide shows you how to connect your AI agent or application to a Redpanda AI Gateway. This is also called "Bring Your Own Agent" (BYOA). You'll configure your client SDK, make your first request, and validate the integration.
 
 After completing this guide, you will be able to:
 

modules/ai-agents/pages/ai-gateway/builders/discover-gateways.adoc

@@ -226,29 +226,29 @@ If you have access to multiple gateways, consider which one to use based on your
 
 Organizations often create separate gateways for different environments:
 
-* *Production gateway*: Higher rate limits, access to all models, monitoring enabled
-* *Staging gateway*: Lower rate limits, restricted models, aggressive cost controls
-* *Development gateway*: Minimal limits, all models for experimentation
+* Production gateway: Higher rate limits, access to all models, monitoring enabled
+* Staging gateway: Lower rate limits, restricted models, aggressive cost controls
+* Development gateway: Minimal limits, all models for experimentation
 
 Choose the gateway that matches your deployment environment.
 
 === By team or project
 
 Gateways may be organized by team or project for cost tracking and isolation:
 
-* *team-ml-gateway*: For machine learning team
-* *team-product-gateway*: For product team
-* *customer-facing-gateway*: For production customer workloads
+* team-ml-gateway: For machine learning team
+* team-product-gateway: For product team
+* customer-facing-gateway: For production customer workloads
 
 Use the gateway designated for your team to ensure proper cost attribution.
 
 === By capability
 
 Different gateways may have different features enabled:
 
-* *Gateway with MCP tools*: Use if your agent needs to call tools
-* *Gateway without MCP*: Use for simple LLM completions
-* *Gateway with specific models*: Use if you need access to particular models
+* Gateway with MCP tools: Use if your agent needs to call tools
+* Gateway without MCP: Use for simple LLM completions
+* Gateway with specific models: Use if you need access to particular models
 
 == Example: Complete discovery workflow
 

modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc

@@ -94,54 +94,54 @@ For MCP tool requests, the lifecycle differs slightly to support deferred tool l
 
 The gateway only loads and exposes specific tools when requested, which dramatically reduces the token overhead compared to loading all tools upfront.
 
-== Supported features
+// == Supported features
 
-=== LLM providers
+// === LLM providers
 
-* OpenAI
-* Anthropic
-* // PLACEHOLDER: Google, AWS Bedrock, Azure OpenAI, others?
+// * OpenAI
+// * Anthropic
+// * // PLACEHOLDER: Google, AWS Bedrock, Azure OpenAI, others?
 
-=== API compatibility
+// === API compatibility
 
-* OpenAI-compatible `/v1/chat/completions` endpoint
-* // PLACEHOLDER: Streaming support?
-* // PLACEHOLDER: Embeddings support?
-* // PLACEHOLDER: Other endpoints?
+// * OpenAI-compatible `/v1/chat/completions` endpoint
+// * // PLACEHOLDER: Streaming support?
+// * // PLACEHOLDER: Embeddings support?
+// * // PLACEHOLDER: Other endpoints?
 
-=== Policy features
+// === Policy features
 
-* CEL-based routing expressions
-* Rate limiting (// PLACEHOLDER: per-gateway, per-header, per-tenant?)
-* Monthly spend limits (// PLACEHOLDER: per-gateway, per-workspace?)
-* Provider pools with automatic failover
-* // PLACEHOLDER: Caching support?
+// * CEL-based routing expressions
+// * Rate limiting (// PLACEHOLDER: per-gateway, per-header, per-tenant?)
+// * Monthly spend limits (// PLACEHOLDER: per-gateway, per-workspace?)
+// * Provider pools with automatic failover
+// * // PLACEHOLDER: Caching support?
 
-=== MCP support
+// === MCP support
 
-* MCP server aggregation
-* Deferred tool loading (often 80-90% token reduction depending on configuration)
-* JavaScript orchestrator for multi-step workflows
-* // PLACEHOLDER: Tool execution sandboxing?
+// * MCP server aggregation
+// * Deferred tool loading (often 80-90% token reduction depending on configuration)
+// * JavaScript orchestrator for multi-step workflows
+// * PLACEHOLDER: Tool execution sandboxing?
 
-=== Observability
+// === Observability
 
-* Request logs with full prompt/response history
-* Token usage tracking
-* Estimated cost per request
-* Latency metrics
-* // PLACEHOLDER: Metrics export? OpenTelemetry support?
+// * Request logs with full prompt/response history
+// * Token usage tracking
+// * Estimated cost per request
+// * Latency metrics
+// * PLACEHOLDER: Metrics export? OpenTelemetry support?
 
-== Current limitations
+// == Current limitations
 
-* // PLACEHOLDER: List current limitations, for example:
-** // - Custom model deployments (Azure OpenAI BYOK, AWS Bedrock custom models)
-** // - Response caching
-** // - Prompt templates/versioning
-** // - Guardrails (PII detection, content moderation)
-** // - Multi-region active-active deployment
-** // - Metrics export to external systems
-** // - Budget alerts/notifications
+// * // PLACEHOLDER: List current limitations, for example:
+// ** // - Custom model deployments (Azure OpenAI BYOK, AWS Bedrock custom models)
+// ** // - Response caching
+// ** // - Prompt templates/versioning
+// ** // - Guardrails (PII detection, content moderation)
+// ** // - Multi-region active-active deployment
+// ** // - Metrics export to external systems
+// ** // - Budget alerts/notifications
 
 == Next steps
 

modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc

@@ -16,23 +16,18 @@ Before starting, ensure you have:
 
 * Access to the AI Gateway UI (provided by your administrator)
 * Admin permissions to configure providers and gateways
-* API key for at least one LLM provider (OpenAI or Anthropic)
+* API key for at least one LLM provider (OpenAI, Anthropic, or Google AI)
 * Python 3.8+, Node.js 18+, or cURL (for testing)
 
 == Configure a provider
 
 Providers represent upstream LLM services (OpenAI, Anthropic) and their associated credentials. Providers are disabled by default and must be enabled explicitly.
 
 . In AI Gateways, navigate to *Providers*.
-. Select a provider (for example, OpenAI or Anthropic).
-. On the *Configuration* tab, click *Add configuration* and enter your API Key.
+. Select a provider (for example, OpenAI, Anthropic, Google AI).
+. On the Configuration tab, click *Add configuration* and enter your API Key.
 . Verify the provider status shows "Active".
 
-AI Gateway currently supports:
-
-* OpenAI
-* Anthropic
-
 == Enable models
 
 After enabling a provider, enable the specific models you want to make available through your gateways.
@@ -60,9 +55,9 @@ A gateway is a logical configuration boundary that defines routing policies, rat
 . Click *Create Gateway*.
 . Configure the gateway:
 +
-* *Name*: Choose a descriptive name (for example, `my-first-gateway`)
-* *Workspace*: Select a workspace (conceptually similar to a resource group)
-* *Description*: Optional metadata for documentation
+* Name: Choose a descriptive name (for example, `my-first-gateway`)
+* Workspace: Select a workspace (conceptually similar to a resource group)
+* Description: Optional metadata for documentation
 
 . After creation, copy the *Gateway Endpoint* and *Gateway ID* from the gateway detail page. You'll need these for sending requests.
 
@@ -72,11 +67,11 @@ Gateway Endpoint: https://gw.ai.panda.com
 Gateway ID: gw_abc123...
 ----
 
-Common gateway patterns:
+Common gateway patterns include the following:
 
-* *Environment separation*: Create separate gateways for staging and production
-* *Team isolation*: One gateway per team for budget tracking
-* *Customer multi-tenancy*: One gateway per customer for isolated policies
+* Environment separation: Create separate gateways for staging and production
+* Team isolation: One gateway per team for budget tracking
+* Customer multi-tenancy: One gateway per customer for isolated policies
 
 == Send your first request
 
@@ -193,10 +188,10 @@ Expected response:
 
 If your request fails, check these common issues:
 
-* *401 Unauthorized*: Verify your API key is valid
-* *404 Not Found*: Confirm the base URL matches your gateway endpoint
-* *Model not found*: Ensure the model is enabled in Step 2
-* *Missing rp-aigw-id*: Add the gateway ID header to your request
+* 401 Unauthorized: Verify your API key is valid
+* 404 Not Found: Confirm the base URL matches your gateway endpoint
+* Model not found: Ensure the model is enabled in Step 2
+* Missing rp-aigw-id: Add the gateway ID header to your request
 
 == Verify in observability dashboard
 
@@ -290,7 +285,7 @@ To add external tools (for example, Slack, GitHub), add their MCP server endpoin
 
 === Deferred tool loading
 
-When many tools are aggregated, listing all tools upfront can consume significant tokens. With *deferred tool loading*, the MCP gateway initially returns only:
+When many tools are aggregated, listing all tools upfront can consume significant tokens. With deferred tool loading, the MCP gateway initially returns only:
 
 * A tool search capability
 * The MCP orchestrator

modules/ai-agents/pages/ai-gateway/index.adoc

@@ -1,8 +1,6 @@
 = AI Gateway
-:description: Unified access layer for LLM providers and AI tools with centralized routing, policy enforcement, cost management, and observability.
+:description: Learn about the unified access layer for LLM providers and AI tools with centralized routing, policy enforcement, cost management, and observability.
 :page-layout: index
 :personas: platform_admin, app_developer, evaluator
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
-
-Redpanda AI Gateway provides a unified access layer for LLM providers and AI tools that sits between your applications and the AI services they use. It delivers centralized routing, policy enforcement, cost management, and observability for all your AI traffic.
+include::ai-agents:partial$ai-gateway-byoc-note.adoc[]

modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc

@@ -20,7 +20,7 @@ MCP aggregation benefits:
 
 == What is MCP?
 
-*Model Context Protocol (MCP)* is a standard for exposing tools (functions) that AI agents can discover and invoke. MCP servers provide tools like:
+Model Context Protocol (MCP) is a standard for exposing tools (functions) that AI agents can discover and invoke. MCP servers provide tools like:
 
 * Database queries
 * File system operations