adp

name

adp

description

Redpanda's Agentic Data Plane: governance infrastructure for building, running, and governing AI agents and MCP servers, plus a proxying AI Gateway for LLM providers, operated via `rpk ai` and the ADP API. Use when creating or managing AI agents (managed or self-managed) via `rpk ai agent` or `AgentRegistryService`; configuring MCP servers (remote or managed catalog, code mode, auth); setting up LLM providers or querying models via `rpk ai llm`/`rpk ai model` or the AI Gateway proxy; or configuring budgets, guardrails, or Cedar access-control policies through the governance APIs. Also covers reading agent transcripts and spending insights, and wiring OAuth clients or providers to the aigw Authorization Server. For the separate rpk cloud mcp control-plane MCP server, see `/redpanda:rpk-cloud`.

Agentic Data Plane

The Agentic Data Plane is Redpanda's governance infrastructure for AI agents and MCP servers. It is its own product surface: it runs on Redpanda and provisions its own environment when you add it. It provides a managed runtime for AI agents and MCP servers, a proxying AI Gateway for LLM providers, and governance surfaces (budgets, guardrails, Cedar access-control policies) to operate those workloads safely. This skill is written for an AI agent operating the platform programmatically via rpk ai and the Agentic Data Plane API or MCP tools. Optimize for correct field names and service names; confirm the live surface before acting.

Component overview

Maturity note: Redpanda Agentic Data Plane is generally available. Its APIs are on the v1alpha1 version path and carry no LaunchStage annotation in the protos, so treat field-level details as still evolving and confirm them live. The rpk ai CLI is in Preview; InsightsService is Experimental.

AI agents

Managed agents run inside the Agentic Data Plane platform. Self-managed (user-hosted) agents are registered as metadata-only records. Both are managed through AgentRegistryService (proto) or the AIAgentService MCP tool group (v1alpha3). Key fields: model, llm_provider, system_prompt, max_iterations (0-200), mcp_servers (max 32 refs), subagents (max 16). A2A agent cards are published at /.well-known/agent-card.json. Triggers (Teams, Cron) fire agents on external events; a trigger can be paused and resumed via Trigger.enabled (toggled through UpdateTrigger with update_mask = ["enabled"]) without deleting it or losing its run history.

See references/agents.md.

MCP servers

Each MCP server is either REMOTE (you own the upstream) or MANAGED (a pre-integrated catalog entry). The managed catalog covers 7 categories (AI, AWS, Communication, Database, Google, Streaming, Utility) with ~50 managed types; the exact set is gated per cluster, so use ListManagedMCPTypes for the live list. Enabling code_mode on a server adds {name}_search and {name}_execute tools, reducing token usage by 80-90% for large tool sets. Two API layers exist: adp.v1alpha1.MCPServerService (management plane, 9 RPCs) and dataplane.v1alpha3.MCPServerService (public Cloud API, 9 RPCs including Start/Stop/Lint). Knowledge bases are a separate v1alpha3 resource, not a sub-resource of MCP servers.

See references/mcp-servers.md.

AI Gateway and LLM providers

The AI Gateway is a managed HTTP proxy. It stores upstream API keys in the Redpanda secret store and injects them on outbound requests; calling applications never see the raw keys. Per-provider URL pattern: <gateway-base>/llm/v1/providers/<provider-name>/<upstream-path>. Manage providers via LLMProviderService (CreateLLMProvider, UpdateLLMProvider, CheckConnection) and discover available models via ModelService (ListModels, GetModel). Supported provider types: OpenAI, Anthropic, Google/Gemini, AWS Bedrock, OpenAI-compatible. Pricing overrides use microcents per million tokens on the provider_models field.

Scope: spend is capped by budgets (hard per-agent caps), and the gateway is a credential-injecting proxy, not a routing or load-balancing layer. Routing/failover, cross-provider load balancing, and per-second/minute/day rate limits are not part of the AI Gateway. To cap spend rather than request rate, use budgets.

See references/gateway-and-providers.md.

Governance: budgets, guardrails, and policies

• Budgets (BudgetService): per-agent or tenant-wide spend caps. All cost fields use microcents (limit_microcents, warn_at_microcents). No limit_cents or current_spend_cents fields exist.
• Spending analysis (SpendingService): GetSpendingSummary, GetSpendingTimeSeries, GetSpendingBreakdown, GetSpendingTimeSeriesByDimension. start_time and end_time are required.
• Guardrails (GuardrailService): Bedrock-backed content safety. Six content filter categories: hate, insults, sexual, violence, misconduct, prompt_attack. Word filters, denied topics, PII filters, and grounding policies are additional sub-policies. Provider is always AWS Bedrock.
• Access control (PolicyService, PolicyTemplateService, SystemPolicyService, EffectivePolicySetService): Cedar policy dialect. No ValidatePolicy, EvaluateAccess, or ListPolicyVersions RPCs exist.
• Data policies (preview): per-MCP-server data shaping (MCPServer.data_policies) that masks, redacts, drops, or clamps fields and filters response rows before the model sees them. Cedar decides whether a call runs; data policies decide how the data is shaped and to whom.
• OAuth / identity (OAuthClientService, OAuthProviderService, OAuthConnectionService): manage OAuth clients (for external tools calling the aigw Authorization Server) and OAuth providers (third-party identity sources).

Services absent from the Agentic Data Plane v1alpha1 surface: SpendLimitService, RateLimitService, RoutingService, BackendPoolService, AccessControlService, SSOService. The names exist only in the legacy aigateway/v1 generated tree used by rpk cloud mcp. OCSF-based authorization-decision accountability is served by the preview AuditLogService on v1alpha1 — see the observability section.

See references/governance.md.

Observability: transcripts, audit log, and insights

• TranscriptsService: ListTranscripts, GetTranscript. Execution-level observability of an agent conversation. Conversations are grouped by OTel gen_ai.conversation.id. TranscriptSummary includes token counts and estimated_cost_usd. Supports managed and self-managed (BYOA) agents.
• AuditLogService (Preview): ListAuditLogEntries, GetAuditLogEntry. Read-only OCSF audit trail across the Agentic Data Plane (management API, LLM proxy, MCP gateway, A2A, spending). Entries carry a call-level outcome (ALLOWED / DENIED / PARTIAL), server-minted correlation_id for grouping a collection-filtered call's per-resource decisions, on-behalf-of fields (invoked_by, agent_name, agent_uid), and redacted entity_before/entity_after diffs on config changes. Reading the audit log is itself audited.
• InsightsService (Experimental): single GetInsights RPC returning active_agents, total_requests, total_cost_microcents over a time window. May change or be removed without a version bump.

Use TranscriptsService for what an agent did in a conversation, and AuditLogService for what any principal was allowed or refused to do against the platform's APIs.

See references/observability.md.

Operating the Agentic Data Plane: CLI and API

The primary CLI is rpk ai, delivered as an rpk managed plugin: rpk downloads and manages the rpai binary (install path ~/.local/bin/.rpk.managed-rpai), so rpk ai install, rpk ai upgrade, and rpk ai uninstall manage that binary's lifecycle. You invoke it as rpk ai. There is no FIPS build of rpai.

Top-level subcommands: agent, auth, connection, env, llm, mcp, model, oauth-client, oauth-provider, policy, run, version.

Programmatic access uses the Agentic Data Plane API directly (gRPC/Connect) or via the MCP tools exposed on the cluster.

See references/rpk-ai.md.

Auth model

rpk ai is self-contained: it owns its own credentials and Agentic Data Plane environment selection, rather than riding the rpk cloud session. Sign in and pick a target:

rpk ai auth login          # OAuth device-authorization flow; caches creds in ~/.rpai/credentials (0600)
rpk ai env list            # list local + live Agentic Data Plane environments
rpk ai env use <environment>  # select the Agentic Data Plane environment whose AI Gateway becomes the active target
rpk ai agent list          # now works

rpk ai auth status shows the current token state, and rpk ai env show prints the resolved environment. Selecting an Agentic Data Plane environment with rpk ai env use replaces the old rpk cloud cluster select step; the connection target is an Agentic Data Plane environment, not a cluster.

Auth modes are device|rpk|token|none (default device). The rpk cloud token is one selectable fallback (--auth-mode rpk), not the primary path. Define a local or manual gateway with rpk ai env add <name> --ai-gateway-url <url> --auth-mode none.

For headless or CI use, pass a static token via --token or the RPAI_TOKEN env var. To override the gateway endpoint for a single invocation, pass --rpai-endpoint <url>; this flag is intentionally not bound to any environment variable. Confirm available auth flows via rpk ai auth --help.

Discover the live surface

Before acting on the Agentic Data Plane, confirm the live API surface. Reference files document a point-in-time snapshot; the catalog and field defaults evolve:

# Confirm all rpk ai subcommands and global flags
rpk ai --help

# Per-group help
rpk ai agent --help
rpk ai mcp --help
rpk ai llm --help
rpk ai model --help

# Discover managed MCP catalog types
rpk ai mcp types

# List tools on a specific MCP server
rpk ai mcp tools list <server-name>

# List available models (optionally filter by provider type)
rpk ai model list

When using the Agentic Data Plane MCP tools: list the available tools for the target service, then describe the tool before calling it to confirm current field names.

For what changed / which release introduced a feature, read the user-facing changelog at adp/RELEASE_NOTES.md in cloudv2 (one section per release, e.g. v0.2.9) rather than relying on this skill — version and feature history is volatile and intentionally not duplicated here.

Key patterns and gotchas

• Cost unit is microcents throughout. limit_microcents, warn_at_microcents, total_cost_microcents: 1 cent = 1,000,000 microcents; $1.00 = 100,000,000 microcents. Never use limit_cents.
• Static-key auth field is key_secret_ref. Some earlier docs called it key_ref. The proto (auth.proto:26) is authoritative: key_secret_ref.
• Guardrail content filter has 6 categories, not 14. The 14-category taxonomy (violent_crimes, etc.) is an RFC draft; the shipped API has hate, insults, sexual, violence, misconduct, prompt_attack.
• Routing and rate limits do not exist in the Agentic Data Plane AI Gateway. The docs explicitly call these out of scope. Do not attempt to configure RoutingService, BackendPoolService, or RateLimitService via the Agentic Data Plane; those are legacy aigateway/v1 names.
• A2A agent card path. The canonical path is /.well-known/agent-card.json. There is no bare /agent.json route.
• subagents.mcp_servers is independent, not a subset. Each subagent's mcp_servers list is independent of the parent agent's list; a subagent may reference servers the parent does not.
• tools field does not exist on ManagedAgentSpec. Agents access tools through mcp_servers references only. The tools field is on mcp_server.proto.
• MCP tool name truncation. The MCP protocol enforces a 64-character limit on tool names. The Agentic Data Plane truncates long managed-catalog names with a hash prefix while preserving the method suffix.
• No RPAI_ENDPOINT env var. --rpai-endpoint is flag-only and applies to one invocation only. Binding it to an env var would silently override the selected Agentic Data Plane environment.
• connection manages your own OAuth grants. rpk ai connection list lists your personal OAuth connections to third-party providers and revokes one. Connections are created through the browser consent flow, not by these commands.

Control-plane MCP server

For the rpk cloud mcp control-plane server (manages Redpanda Cloud clusters, networks, IAM, and legacy AI Gateway aigateway/v1 surfaces), see /redpanda:rpk-cloud.

Reference files

• references/agents.md: AgentRegistryService RPCs, ManagedAgentSpec fields, subagents, A2A agent card, triggers, agent credentials.
• references/mcp-servers.md: MCPServerService API layers, MCPServer fields, remote auth modes, code mode, managed catalog, knowledge bases.
• references/gateway-and-providers.md: LLMProviderService RPCs, provider types and auth schemes, ModelService, pricing overrides, AI Gateway proxy behavior, explicit out-of-scope list.
• references/governance.md: BudgetService, SpendingService, GuardrailService (Bedrock, 6 categories), Cedar access-control services, OAuth/identity services, absent service names.
• references/rpk-ai.md: rpk ai subcommand tree, lifecycle management, global flags, common errors, per-group subcommand details.
• references/observability.md: TranscriptsService RPCs and fields, AuditLogService (Preview) RPCs, filters, verdicts, grouped-call semantics and delegation fields, InsightsService (Experimental), transcripts-vs-audit-log framing.