# Build Agents

> For the complete documentation index, see [llms.txt](https://docs.redpanda.com/llms.txt). Component-specific: [agentic-data-plane-full.txt](https://docs.redpanda.com/agentic-data-plane-full.txt)

---
title: Build Agents
latest-operator-version: v26.2.1
latest-console-tag: v3.10.0
latest-connect-version: 4.105.0
latest-redpanda-tag: v26.2.1
docname: agents
page-component-name: agentic-data-plane
page-version: master
page-component-version: master
page-component-title: Agentic Data Plane
page-relative-src-path: agents.adoc
page-edit-url: https://github.com/redpanda-data/adp-docs/edit/main/modules/connect/pages/agents.adoc
description: Understand how AI agents work in the Agentic Data Plane, then create, register, and design them.
page-git-created-date: "2026-06-01"
page-git-modified-date: "2026-06-18"
---

<!-- Source: https://docs.redpanda.com/agentic-data-plane/connect/agents.md -->

Agents are the workloads that call LLMs and tools through the Agentic Data Plane. Start with how agents work, then create a declarative agent, set up an agent you host yourself, and apply architecture and system-prompt best practices.

-   [How Agents Work](https://docs.redpanda.com/agentic-data-plane/connect/agents-overview/)

    Learn how Agentic Data Plane agents use a declarative approach backed by a broad library of prebuilt tools and integrations to replace custom agent code.

-   [Understand Agent Concepts](https://docs.redpanda.com/agentic-data-plane/connect/concepts/)

    Understand how declaratively configured agents execute reasoning loops, manage context, invoke tools, and handle errors.

-   [Connect Agents with A2A](https://docs.redpanda.com/agentic-data-plane/connect/a2a-concepts/)

    The A2A protocol enables agents to discover and call each other across platforms. Learn how agent cards, authentication, and protocol versioning work in Agentic Data Plane.

-   [Choose an Agent Architecture](https://docs.redpanda.com/agentic-data-plane/connect/architecture-patterns/)

    Design maintainable agent systems with single-agent and multi-agent patterns based on domain complexity.

-   [Write Effective System Prompts](https://docs.redpanda.com/agentic-data-plane/connect/system-prompts/)

    Write system prompts that produce reliable, predictable agent behavior through clear constraints and tool guidance.

-   [Create an Agent](https://docs.redpanda.com/agentic-data-plane/connect/create-agent/)

    Configure a managed Agentic Data Plane agent declaratively on a single create canvas. No Python or JavaScript code required.

-   [Draw Charts from an Agent](https://docs.redpanda.com/agentic-data-plane/connect/draw-charts/)

    Make an agent render inline bar and line charts in the Playground by emitting a chart code block that follows the Agentic Data Plane chart contract.

-   [Set Up a Self-Managed Agent](https://docs.redpanda.com/agentic-data-plane/connect/self-managed-agents/)

    Register a self-managed agent, issue it a client credential, and route its LLM and tool calls through the AI Gateway so spend, traces, and transcripts attribute back to the agent.

-   [Trigger Agents](https://docs.redpanda.com/agentic-data-plane/connect/triggers/overview/)

    Triggers invoke a deployed agent without calling its API directly, from Microsoft Teams or on a schedule.