cleanup

Author: micheleRP

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

@@ -23,7 +23,7 @@ After reading this page, you will be able to:
 
 == AI agents
 
-With Redpanda AI agents, you declare the agent behavior you want and Redpanda handles execution and orchestration. Instead of writing Python or JavaScript, you define behaviors in YAML, orchestrate multiple specialized glossterm:subagent[,sub-agents], or bring your own frameworks like LangChain or LlamaIndex.
+With Redpanda AI agents, you declare the agent behavior you want and Redpanda handles execution and orchestration. Instead of writing Python or JavaScript, you define behaviors in YAML. You can orchestrate multiple specialized glossterm:subagent[,sub-agents], or bring your own frameworks like LangChain or LlamaIndex.
 
 What makes this practical at scale is xref:develop:connect/about.adoc[Redpanda Connect]. More than 300 connectors with built-in filtering, enrichment, and routing give declarative definitions real power. Upcoming templates will provide default behaviors for common domains such as customer success, legal, and finance.
 
@@ -37,15 +37,15 @@ glossterm:MCP server[,MCP servers] translate agent intent into connections to da
 
 Under the hood, MCP servers wrap the same proven connectors that power some of the world's largest e-commerce, EV, electricity, and AI companies. Built on xref:develop:connect/about.adoc[Redpanda Connect], they are lightweight, support OIDC-based authentication, and enforce deterministic policies at the tool level. You define tools in YAML, and policy enforcement programmatically prevents prompt injection, SQL injection, and other agent-based attacks.
 
-With 300+ out-of-the-box connectors and real-time debugging capabilities, you reduce integration time while getting enterprise-grade security. You can reuse your existing infrastructure and data sources rather than building new integrations from scratch.
+With over 300 connectors and real-time debugging capabilities, you reduce integration time while getting enterprise-grade security. You can reuse your existing infrastructure and data sources rather than building new integrations from scratch.
 
 For more information, see xref:ai-agents:mcp/overview.adoc[MCP Servers Overview].
 
 == Transcripts
 
 Every agent action is recorded in an end-to-end execution log. A single glossterm:transcript[] can span multiple agents, tools, and models, covering interactions that last minutes to days.
 
-Transcripts are the keystone of agent governance. They are built on Redpanda's immutable log with Raft consensus and TLA+ correctness proofs. No gaps, no tampering. For regulated industries that require multi-year audit trails, this provides a compliance-grade record of every decision an agent makes and every data source it uses.
+Transcripts are the keystone of agent governance. They are built on Redpanda's immutable log with glossterm:transcript[] consensus and TLA+ correctness proofs. No gaps, no tampering. For regulated industries that require multi-year audit trails, this provides a compliance-grade record of every decision an agent makes and every data source it uses.
 
 Redpanda captures 100% of agent actions through OpenTelemetry standards, with end-to-end lineage across the entire execution chain. You can materialize execution logs to Iceberg tables for long-term retention and analysis, or replay them to evaluate and improve agent performance over time.
 
@@ -65,17 +65,17 @@ For more information, see xref:ai-agents:ai-gateway/what-is-ai-gateway.adoc[AI G
 
 Redpanda ADP addresses critical enterprise requirements across all components.
 
-*Security by design*:: MCP servers enforce policies at the tool level, programmatically preventing prompt injection, SQL injection, and other agent-based attacks. Policy enforcement is deterministic and controlled. Agents cannot bypass security constraints even through creative prompting.
+* *Security by design* MCP servers enforce policies at the tool level, programmatically preventing prompt injection, SQL injection, and other agent-based attacks. Policy enforcement is deterministic and controlled. Agents cannot bypass security constraints even through creative prompting.
 
-*Unified authorization*:: All components use OIDC-based authentication with an on-behalf-of authorization model. When a user invokes an agent, the agent inherits the intersection of its own permissions and the user's permissions. This ensures proper data access scoping.
+* *Unified authorization*:: All components use OIDC-based authentication with an on-behalf-of authorization model. When a user invokes an agent, the agent inherits the intersection of its own permissions and the user's permissions. This ensures proper data access scoping.
 
-*Complete observability*:: Redpanda ADP provides two levels of inspection. Execution logs (transcripts) capture every agent action with 100% sampling using OpenTelemetry standards. Real-time debugging tools allow you to inspect individual MCP server calls down to individual tool invocations with full timing data. You can view detailed agent actions in glossterm:Redpanda Console[] and replay data for agent evaluations.
+* *Complete observability*:: Redpanda ADP provides two levels of inspection. Execution logs (transcripts) capture every agent action with 100% sampling using OpenTelemetry standards. Real-time debugging tools allow you to inspect individual MCP server calls down to individual tool invocations with full timing data. You can view detailed agent actions in glossterm:Redpanda Console[] and replay data for agent evaluations.
 
-*Compliance and audit*:: For industries requiring multi-year audit trails, Redpanda ADP records every agent action and data source used in decision-making. Execution logs are stored in Redpanda topics and can be materialized to Iceberg tables for long-term retention and analysis.
+* *Compliance and audit*:: For industries requiring multi-year audit trails, Redpanda ADP records every agent action and data source used in decision-making. Execution logs are stored in Redpanda topics and can be materialized to Iceberg tables for long-term retention and analysis.
 
 == Use cases
 
-Organizations use Redpanda ADP to:
+Some ways organizations can leverage Redpanda ADP include:
 
 * *Automate operational workflows*: Create specialized agents for building management, infrastructure monitoring, compliance reporting, and other domain-specific tasks.
 * *Monitor manufacturing and operations*: Deploy multi-agent systems that analyze factory machine telemetry in real-time, detect anomalies, search equipment manuals, and create maintenance tickets automatically.

modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc

@@ -141,25 +141,17 @@ endif::[]
 
 == Common gateway patterns
 
-=== Team isolation
+Some common patterns for configuring gateways include:
 
-When multiple teams share infrastructure but need separate budgets and policies, create one gateway for each team. For example, you might configure Team A's gateway with a $5K/month budget for both staging and production environments, while Team B's gateway has a $10K/month budget with different rate limits. Each team sees only their own traffic in the observability dashboards, providing clear cost attribution and isolation.
-
-=== Environment separation
-
-To prevent staging traffic from affecting production metrics, create separate gateways for each environment. Configure the staging gateway with lower rate limits, restricted model access, and aggressive cost controls to prevent runaway expenses. The production gateway can have higher rate limits, access to all models, and alerting configured to detect anomalies.
-
-=== Primary and fallback for reliability
-
-To ensure uptime during provider outages, configure provider pools with automatic failover. For example, you can set OpenAI as your primary provider (preferred for quality) and configure Anthropic as the fallback that activates when the gateway detects rate limits or timeouts from OpenAI. Monitor the fallback rate to detect primary provider issues early, before they impact your users.
-
-=== A/B testing models
-
-To compare model quality and cost without dual integration, route a percentage of traffic to different models. For example, you can send 80% of traffic to `claude-sonnet-4.5` and 20% to `claude-opus-4.6`, then compare quality metrics and costs in the observability dashboard before adjusting the split.
+* *Team isolation*: When multiple teams share infrastructure but need separate budgets and policies, create one gateway for each team. For example, you might configure Team A's gateway with a $5K/month budget for both staging and production environments, while Team B's gateway has a $10K/month budget with different rate limits. Each team sees only their own traffic in the observability dashboards, providing clear cost attribution and isolation.
+* *Environment separation*: To prevent staging traffic from affecting production metrics, create separate gateways for each environment. Configure the staging gateway with lower rate limits, restricted model access, and aggressive cost controls to prevent runaway expenses. The production gateway can have higher rate limits, access to all models, and alerting configured to detect anomalies.
+* *Primary and fallback for reliability*: To ensure uptime during provider outages, configure provider pools with automatic failover. For example, you can set OpenAI as your primary provider (preferred for quality) and configure Anthropic as the fallback that activates when the gateway detects rate limits or timeouts from OpenAI. Monitor the fallback rate to detect primary provider issues early, before they impact your users.
+* *A/B testing models*: To compare model quality and cost without dual integration, route a percentage of traffic to different models. For example, you can send 80% of traffic to `claude-sonnet-4.5` and 20% to `claude-opus-4.6`, then compare quality metrics and costs in the observability dashboard before adjusting the split.
+* *Customer-based routing*: For SaaS products with tiered pricing (for example, free, pro, enterprise), use CEL routing based on request headers to match users with appropriate models:
 
 === Customer-based routing
 
-For SaaS products with tiered pricing (free, pro, enterprise), use CEL routing based on request headers to match users with appropriate models:
+For SaaS products with tiered pricing (for example, free, pro, enterprise), use CEL routing based on request headers to match users with appropriate models:
 
 [source,cel]
 ----

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

@@ -10,7 +10,7 @@ include::ai-agents:partial$ai-gateway-byoc-note.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. 
 
-With transcripts, organizations gain the ability to debug agent behavior, identify performance bottlenecks, meet regulatory compliance requirements, and maintain accountability for AI-driven decisions. Transcripts use OpenTelemetry standards and Raft-based consensus for correctness, establishing a trustworthy foundation for agent governance.
+With transcripts, organizations gain the ability to debug agent behavior, identify performance bottlenecks, meet regulatory compliance requirements, and maintain accountability for AI-driven decisions. Transcripts use OpenTelemetry standards and glossterm:Raft[]-based consensus for correctness, establishing a trustworthy foundation for agent governance.
 
 After reading this page, you will be able to:
 

modules/get-started/pages/whats-new-cloud.adoc

@@ -8,9 +8,9 @@ This page lists new features added to Redpanda Cloud.
 
 == February 2026
 
-=== Agentic Data Plane (ADP): limited availability
+=== Agentic Data Plane (ADP): LA
 
-Redpanda Agentic Data Plane (ADP) provides enterprise-grade infrastructure for building, deploying, and governing AI agents at scale. Key capabilities include declarative agents, MCP servers backed by 300+ connectors, an AI Gateway with model failover and fiscal controls, and compliance-grade transcripts built on Redpanda's immutable log. Redpanda ADP is supported on BYOC clusters running with AWS and Redpanda version 25.3 and later.
+Redpanda Agentic Data Plane (ADP) is now available in a glossterm:LA[, limited availability] (LA) release. Redpanda ADP provides enterprise-grade infrastructure for building, deploying, and governing AI agents at scale. Key capabilities include declarative agents, MCP servers backed by 300+ connectors, an AI Gateway with model failover and fiscal controls, and compliance-grade transcripts built on Redpanda's immutable log. 
 
 See xref:ai-agents:adp-overview.adoc[Agentic Data Plane Overview].