redpanda-data · micheleRP · Feb 19, 2026 · Feb 19, 2026
@@ -7,7 +7,7 @@
 :learning-objective-2: Describe how each component addresses enterprise governance and reliability requirements
 :learning-objective-3: Determine whether Redpanda ADP fits your organization's requirements for AI agent deployment
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 As glossterm:AI agent[,AI agents] evolve from experimental prototypes to business-critical systems, companies face new challenges. How do you ensure your AI agents are reliable? How do you maintain control over costs and compliance? And how do you scale them across your organization without creating technical debt?
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Explain how agent cards enable discovery
 :learning-objective-3: Identify how authentication secures agent communication
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 The Agent-to-Agent (A2A) protocol is an open standard for agent communication and discovery. Redpanda Cloud uses A2A for both external integration and internal pipeline-to-agent communication.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Choose appropriate LLM models based on task requirements
 :learning-objective-3: Apply agent boundary design principles for maintainability
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 This topic helps you design agent systems that are maintainable, discoverable, and reliable by choosing the right architecture pattern and applying clear boundary principles.
 

@@ -2,6 +2,6 @@
 :page-layout: index
 :description: Create production AI agents with effective prompts and scalable architecture.
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Create agents, write effective prompts, and design scalable agent systems.
@@ -6,7 +6,7 @@
 :learning-objective-2: Describe how agents manage context and state across interactions
 :learning-objective-3: Identify error handling strategies for agent failures
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 After you declaratively configure an agent's behavior (its LLM, system prompt, and tools), the framework manages execution through a reasoning loop. The LLM analyzes context, decides which tools to invoke, processes results, and repeats until the task completes. Understanding this execution model helps you fine-tune agent settings like iteration limits and tool selection.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Connect MCP servers and select tools for your agent
 :learning-objective-3: Set agent execution parameters including max iterations
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Create a new AI agent declaratively through the Redpanda Cloud Console. No Python or JavaScript code required. This guide walks you through configuring the agent's model, writing the system prompt, connecting tools from built-in connectors, and setting execution parameters.
 

@@ -2,6 +2,6 @@
 :page-layout: index
 :description: Get started with declarative AI agents in Redpanda Cloud. Connect tools, configure behavior, and deploy without writing agent code.
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Start here to create your first declarative AI agent. Select an LLM, define behavior, and connect tools from built-in connectors.
@@ -2,4 +2,4 @@
 :page-layout: index
 :description: Declare agent behavior using built-in connectors in Redpanda Cloud. No custom agent code required.
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
@@ -2,7 +2,7 @@
 :page-layout: index
 :description: Connect agents to external applications, pipelines, and other systems.
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Choose integration patterns and connect agents to your systems.
 
@@ -6,7 +6,7 @@
 :learning-objective-2: Apply appropriate authentication for internal versus external integration
 :learning-objective-3: Select the right communication protocol for your integration scenario
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Redpanda Cloud supports multiple integration patterns for agents, pipelines, and external applications. Choose the pattern that matches your integration scenario.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Track token usage and performance metrics
 :learning-objective-3: pass:q[Debug agent execution using *Transcripts*]
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Use monitoring to track agent performance, analyze conversation patterns, debug execution issues, and optimize token costs.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Explain how Redpanda Cloud streaming infrastructure benefits agent architectures
 :learning-objective-3: Identify use cases where Redpanda Cloud agents provide value
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 AI agents in Redpanda Cloud take a declarative approach: instead of writing Python or JavaScript agent code, you declare the behavior you want by selecting an LLM, writing a system prompt, and connecting tools drawn from 300+ built-in Redpanda Connect connectors. The framework handles execution, tool orchestration, and scaling, backed by real-time streaming infrastructure and built-in filtering and data enrichment.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: pass:q[Design event-driven agent invocation using the `a2a_message` processor]
 :learning-objective-3: Implement streaming enrichment with AI-generated fields
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Build Redpanda Connect pipelines that invoke agents for automated, event-driven processing. Pipelines use the `a2a_message` processor to call agents for each event in a stream when you need AI reasoning, classification, or enrichment at scale.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Apply constraint patterns to prevent unintended agent behavior
 :learning-objective-3: Evaluate system prompts for clarity and completeness
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Write system prompts that produce reliable, predictable agent behavior. Good prompts define scope, specify constraints, and guide tool usage.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Configure the agent with a system prompt and model selection
 :learning-objective-3: Test the agent by generating and publishing events through natural language
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 This quickstart helps you build your first AI agent in Redpanda Cloud. You'll create an agent that understands natural language requests and uses MCP tools to generate and publish event data to Redpanda topics.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Resolve runtime behavior issues including tool selection and iteration limits
 :learning-objective-3: Fix tool execution problems and authentication failures
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Use this page to diagnose and fix common issues with AI agents, including deployment failures, runtime behavior problems, tool execution errors, and integration issues.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Apply tool orchestration patterns to handle multi-step workflows
 :learning-objective-3: Evaluate how system prompt design affects agent tool selection
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 This tutorial shows you how to build a customer support agent to learn how agents orchestrate multiple tools, make context-aware decisions, and handle incomplete data.
 

@@ -8,7 +8,7 @@
 
 This tutorial shows you how to build a transaction dispute resolution system using multi-agent architecture, secure data handling, and execution monitoring.
 
-include::ai-agents:partial$byoc-aws-requirement.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 After completing this tutorial, you will be able to:
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Create and configure gateways with routing policies, rate limits, and spend limits
 :learning-objective-3: Set up MCP tool aggregation for AI agents
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 This guide walks administrators through the setup process for AI Gateway, from enabling LLM providers to configuring routing policies and MCP tool aggregation.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Make LLM requests through the gateway and handle responses appropriately
 :learning-objective-3: Validate your integration end-to-end
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 This guide shows you how to connect your glossterm:AI agent[] or application to Redpanda Agentic Data Plan. This is also called "Bring Your Own Agent" (BYOA). You'll configure your client SDK, make your first request, and validate the integration.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: View which models and MCP tools are available through each gateway
 :learning-objective-3: Test gateway connectivity before integration
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 As a builder, you need to know which gateways are available to you before integrating your agent or application. This page shows you how to discover accessible gateways, understand their configurations, and verify connectivity.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Test CEL routing logic using the UI editor or test requests
 :learning-objective-3: Troubleshoot common CEL errors using safe patterns
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Redpanda AI Gateway uses CEL (Common Expression Language) for dynamic request routing. CEL expressions evaluate request properties (headers, body, context) and determine which model or provider should handle each request.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Explain the request lifecycle through policy evaluation stages
 :learning-objective-3: Identify supported providers, features, and current limitations
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 This page provides technical details about AI Gateway's architecture, request processing, and capabilities. For an overview of AI Gateway, see xref:ai-agents:ai-gateway/what-is-ai-gateway.adoc[]
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Route your first request through AI Gateway and verify it works
 :learning-objective-3: Verify request routing and token usage in the gateway overview
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Redpanda AI Gateway keeps your AI-powered applications running and your costs under control by routing all LLM and MCP traffic through a single managed layer with automatic failover and budget enforcement. This quickstart walks you through configuring your first gateway and routing requests through it.
 

@@ -3,4 +3,4 @@
 :page-layout: index
 :personas: platform_admin, app_developer, evaluator
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
@@ -6,7 +6,7 @@
 :learning-objective-2: Write orchestrator workflows to reduce multi-step interactions
 :learning-objective-3: Manage approved MCP servers with security controls and audit trails
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 The MCP Gateway provides glossterm:MCP[,Model Context Protocol (MCP)] aggregation, allowing glossterm:AI agent[,AI agents] to access glossterm:MCP tool[,tools] from multiple MCP servers through a single unified endpoint. This eliminates the need for agents to manage multiple MCP connections and significantly reduces token costs through deferred tool loading.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Describe how AI Gateway prevents runaway AI spend with centralized budget controls and tenancy-based governance
 :learning-objective-3: Identify when AI Gateway fits your use case based on availability requirements, cost governance needs, and multi-provider or MCP tool usage
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Redpanda AI Gateway keeps your AI-powered applications highly available and your AI spend under control. It sits between your applications and the LLM providers and AI tools they depend on. If a provider goes down, the gateway provides automatic failover to keep your apps running. It also offers centralized budget controls to prevent runaway costs. For platform teams, it adds governance at the model-fallback level, tenancy modeling for teams, individuals, apps, and service accounts, and a single proxy layer for both LLM models and glossterm:MCP server[,MCP servers].
 

@@ -3,4 +3,4 @@
 :page-layout: index
 :page-aliases: develop:agents/about.adoc, develop:ai-agents/about.adoc
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
@@ -6,7 +6,7 @@
 :learning-objective-2: Interpret transcript structure for debugging and monitoring
 :learning-objective-3: Distinguish between transcripts and audit logs
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Redpanda provides complete observability and governance for AI agents through automated glossterm:transcript[] capture. Every agent execution, from simple tool calls to complex multi-agent, multi-turn workflows, generates a permanent, write-once record stored on Redpanda's glossterm:log[distributed log]. This captures all agent reasoning, tool invocations, model interactions, and data flows with 100% sampling and no gaps. 
 

@@ -2,6 +2,6 @@
 :page-layout: index
 :description: Govern agentic AI with complete execution transcripts built on Redpanda's immutable distributed log.
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 {description}
@@ -5,7 +5,7 @@
 :learning-objective-2: Validate trace data format and compatibility with existing MCP server traces
 :learning-objective-3: Secure the ingestion endpoint using authentication mechanisms
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 You can extend Redpanda's transcript observability to custom agents built with frameworks like LangChain or instrumented with OpenTelemetry SDKs. By ingesting traces from external applications into the `redpanda.otel_traces` topic, you gain unified visibility across all agent executions, from Redpanda's declarative agents, Remote MCP servers, to your own custom implementations.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Use the timeline interactively to navigate to specific time periods
 :learning-objective-3: Navigate between detail views to inspect span information at different levels
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Use the Transcripts view to filter, inspect, and debug agent execution records. Filter by operation type, time range, or service to isolate specific executions, then drill into span hierarchies to trace request flow and identify where failures or performance bottlenecks occur.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Configure user preference toggles to customize routing behavior
 :learning-objective-3: Manage provider credentials for OpenAI, Anthropic, and Google Gemini
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 AI Hub mode provides instant, pre-configured access to OpenAI, Anthropic, and Google Gemini with zero setup complexity. Platform admins add provider credentials, and all teams immediately benefit from intelligent routing.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Prepare for ejection by documenting current configuration
 :learning-objective-3: Execute the ejection process and configure the gateway post-ejection
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Ejecting a gateway from AI Hub mode to Custom mode is a one-way transition that gives you full control over all routing rules, backend pools, and policies. After ejection, the gateway behaves exactly like a Custom mode gateway.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Determine which mode suits your use case based on configuration needs
 :learning-objective-3: Identify which mode a gateway is running in using Console or API
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 AI Gateway supports two modes to accommodate different organizational needs: AI Hub mode for zero-configuration access and Custom mode for full control over routing and policies.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Connect your application to an AI Hub gateway using the OpenAI SDK
 :learning-objective-3: Describe how intelligent routing directs requests to providers
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 AI Hub mode gateways provide instant access to OpenAI, Anthropic, and Google Gemini with pre-configured intelligent routing. As a builder, you benefit from zero-configuration access while your administrator manages provider credentials and routing policies.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Set up authentication and access control for Claude Code clients
 :learning-objective-3: Deploy MCP tool aggregation for Claude Code tool discovery
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Configure Redpanda AI Gateway to support Claude Code clients accessing LLM providers and MCP tools through a unified endpoint.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Set up MCP server integration through AI Gateway
 :learning-objective-3: Verify Claude Code is routing requests through the gateway
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gateway], set up Claude Code to route LLM requests and access MCP tools through the gateway's unified endpoints.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Set up authentication and access control for Cline clients
 :learning-objective-3: Deploy MCP tool aggregation for Cline tool discovery
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Configure Redpanda AI Gateway to support Cline (formerly Claude Dev) clients accessing LLM providers and MCP tools through a unified endpoint.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Set up autonomous mode with custom instructions and browser integration
 :learning-objective-3: Verify Cline routes requests through the gateway and optimize for cost
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gateway], set up Cline (formerly Claude Dev) to route LLM requests and access MCP tools through the gateway's unified endpoints.
 

@@ -6,7 +6,7 @@
 :learning-objective-2: Set up multi-provider backends with native format routing
 :learning-objective-3: Deploy MCP tool aggregation for Continue.dev tool discovery
 
-include::ai-agents:partial$ai-gateway-byoc-note.adoc[]
+include::ai-agents:partial$adp-la.adoc[]
 
 Configure Redpanda AI Gateway to support Continue.dev clients accessing multiple LLM providers and MCP tools through flexible, native-format endpoints.