Merge branch 'ai-agents-documentation' into adp-pkg1

# Conflicts:
#	modules/ai-agents/pages/index.adoc

Author: Paulo Borges

modules/ai-agents/pages/agents/concepts.adoc

@@ -1,12 +1,12 @@
 = Agent Concepts
-:description: Understand how agents execute, manage context, invoke tools, and handle errors.
+:description: Understand how declaratively configured agents execute reasoning loops, manage context, invoke tools, and handle errors.
 :page-topic-type: concepts
 :personas: agent_developer, streaming_developer, data_engineer
 :learning-objective-1: Explain how agents execute reasoning loops and make tool invocation decisions
 :learning-objective-2: Describe how agents manage context and state across interactions
 :learning-objective-3: Identify error handling strategies for agent failures
 
-Agents execute through a reasoning loop where the LLM analyzes context, decides which tools to invoke, processes results, and repeats until the task completes. Understanding this execution model helps you design reliable agent systems.
+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.
 
 include::ai-agents:partial$byoc-aws-requirement.adoc[]
 

modules/ai-agents/pages/agents/create-agent.adoc

@@ -1,12 +1,12 @@
 = Create an Agent
-:description: Configure agents with model selection, system prompts, tool connections, and execution parameters.
+:description: Declaratively configure an agent by choosing an LLM, writing a system prompt, connecting tools from built-in connectors, and setting execution parameters.
 :page-topic-type: how-to
 :personas: agent_developer, app_developer, streaming_developer
 :learning-objective-1: Configure an agent with model selection and system prompt
 :learning-objective-2: Connect MCP servers and select tools for your agent
 :learning-objective-3: Set agent execution parameters including max iterations
 
-Create a new AI agent through the Redpanda Cloud Console. This guide walks you through configuring the agent's model, system prompt, tools, and execution settings.
+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.
 
 include::ai-agents:partial$byoc-aws-requirement.adoc[]
 

modules/ai-agents/pages/agents/get-started-index.adoc

@@ -1,5 +1,5 @@
 = Get Started with AI Agents
 :page-layout: index
-:description: Learn what AI agents are and build your first agent in Redpanda Cloud.
+:description: Get started with declarative AI agents in Redpanda Cloud. Connect tools, configure behavior, and deploy without writing agent code.
 
-Start here to understand AI agents and build your first one.
+Start here to create your first declarative AI agent. Select an LLM, define behavior, and connect tools from built-in connectors.

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

@@ -1,5 +1,5 @@
 = AI Agents
 :page-layout: index
-:description: Build AI agents that use Redpanda Cloud for real-time streaming data and tool execution.
+:description: Declare agent behavior using built-in connectors in Redpanda Cloud. No custom agent code required.
 
-Build AI agents that combine large language models with MCP tools to process streaming data and execute actions.
+Declare agent behavior by selecting an LLM and connecting tools from 300+ built-in Redpanda Connect connectors with filtering and data enrichment built in. No Python or JavaScript agent code required.

modules/ai-agents/pages/agents/overview.adoc

@@ -1,12 +1,12 @@
 = AI Agents Overview
-:description: Learn what AI agents are and how Redpanda Cloud supports agent development with real-time streaming.
+:description: Learn how Redpanda Cloud agents use a declarative approach backed by 300+ built-in connectors to replace custom agent code.
 :page-topic-type: overview
 :personas: evaluator, agent_developer, app_developer, streaming_developer
 :learning-objective-1: Describe what AI agents are and their essential components
 :learning-objective-2: Explain how Redpanda Cloud streaming infrastructure benefits agent architectures
 :learning-objective-3: Identify use cases where Redpanda Cloud agents provide value
 
-AI agents are systems that combine large language models (LLMs) with the ability to execute actions and process data. Redpanda Cloud provides real-time streaming infrastructure and standardized tool access to support agent development.
+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.
 
 include::ai-agents:partial$byoc-aws-requirement.adoc[]
 
@@ -18,11 +18,11 @@ After reading this page, you will be able to:
 
 == What is an AI agent?
 
-An AI agent is a system built around a glossterm:large language model (LLM)[] that can interpret user intent, decide which actions are required, invoke external tools, process live and historical data, and chain multiple steps into a workflow. AI agents differ from text-only LLMs by executing actions and invoking external tools.
+An AI agent is a system built around a glossterm:large language model (LLM)[] that interprets user intent, selects the right tools, and chains multiple steps into a workflow. In Redpanda Cloud, agents are declarative: you configure what the agent should do (its role, constraints, and available tools) rather than writing imperative agent code. This is possible because Redpanda Connect provides 300+ connectors and robust data processing capabilities that the framework orchestrates for you.
 
 == How agents work
 
-Every AI agent consists of four essential components:
+When you create an agent, you configure these four components through the Redpanda Cloud Console rather than writing code:
 
 * *System prompt*: Defines the agent's role, responsibilities, and constraints
 * *LLM*: Interprets user intent and decides which tools to invoke
@@ -37,7 +37,7 @@ For a deeper understanding of how agents execute, manage context, and maintain s
 
 == Key benefits
 
-Redpanda Cloud provides real-time streaming data so agents access live events instead of batch snapshots. xref:ai-agents:mcp/remote/overview.adoc[Remote MCP] support enables standardized tool access. Managed infrastructure handles deployment, scaling, and security for you. Low-latency execution means tools run close to your data. Integrated secrets management securely stores API keys and credentials.
+A declarative approach means you configure agent behavior instead of coding it, with access to 300+ built-in Redpanda Connect connectors for data sources, APIs, and services. Real-time streaming data ensures agents access live events instead of batch snapshots. xref:ai-agents:mcp/remote/overview.adoc[Remote MCP] support enables standardized tool access. Managed infrastructure handles deployment, scaling, and security for you. Low-latency execution means tools run close to your data. Integrated secrets management securely stores API keys and credentials.
 
 == Use cases
 

modules/ai-agents/pages/index.adoc

@@ -1,10 +1,8 @@
-= Agentic AI
-:description: Learn about the Redpanda Agentic Data Plane. Keep AI-powered apps highly available, control costs across providers, and govern access for teams, apps, and service accounts.
+= AI Agents in Redpanda Cloud
+:description: Build AI agents declaratively in Redpanda Cloud using 300+ built-in connectors. Configure behavior instead of writing custom agent code.
 :page-layout: index
 :page-aliases: develop:agents/about.adoc, develop:ai-agents/about.adoc
 
-glossterm:AI agent[,AI agents] are configurable assistants that autonomously perform specialist tasks by leveraging large language models (LLMs) and connecting to external data sources and tools.
+glossterm:AI agent[,AI agents] in Redpanda Cloud are declarative assistants that perform specialist tasks without requiring you to write Python or JavaScript agent code. You declare the behavior you want by choosing an LLM, defining a system prompt, and connecting tools powered by 300+ built-in Redpanda Connect connectors with robust filtering and data enrichment. The framework handles execution, orchestration, and scaling.
 
-Redpanda's xref:ai-agents:adp-overview.adoc[Agentic Data Plane (ADP)] provides enterprise-grade infrastructure for building, deploying, and governing AI agents at scale with four key components that deliver enterprise governance, cost controls, and compliance-grade audit trails.
-
-Redpanda Cloud provides two complementary Model Context Protocol (MCP) options to help you build AI agents.
+Redpanda Cloud provides two complementary Model Context Protocol (MCP) options to connect agents to data sources and external services.