# How Agents Work > 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: How Agents Work latest-operator-version: v26.1.5 latest-console-tag: v3.7.4 latest-connect-version: 4.96.1 latest-redpanda-tag: v26.1.10 docname: agents-overview page-component-name: agentic-data-plane page-version: master page-component-version: master page-component-title: Agentic Data Plane page-relative-src-path: agents-overview.adoc page-edit-url: https://github.com/redpanda-data/adp-docs/edit/main/modules/connect/pages/agents-overview.adoc description: Learn how ADP agents use a declarative approach backed by a broad library of prebuilt tools and integrations to replace custom agent code. page-topic-type: overview personas: agent_builder, platform_engineer learning-objective-1: Describe what AI agents are and their essential components learning-objective-2: Explain how ADP streaming infrastructure benefits agent architectures learning-objective-3: Identify use cases where ADP agents provide value page-git-created-date: "2026-05-28" page-git-modified-date: "2026-06-10" --- Redpanda ADP AI agents 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 attaching prebuilt tools and integrations. The framework handles execution, tool orchestration, and scaling, backed by real-time streaming infrastructure and built-in filtering and data enrichment. After reading this page, you will be able to: - Describe what AI agents are and their essential components - Explain how ADP streaming infrastructure benefits agent architectures - Identify use cases where ADP agents provide value ## [](#what-is-an-ai-agent)What is an AI agent? An AI agent is a system built around a [large language model (LLM)](https://docs.redpanda.com/agentic-data-plane/reference/glossary/#large-language-model-llm) that interprets user intent, selects the right tools, and chains multiple steps into a workflow. In ADP, agents are declarative: you configure the agent’s role, constraints, and available tools rather than writing imperative agent code. Agents reach tools through MCP servers, and Redpanda Connect provides the real-time streaming and data-processing capabilities the framework orchestrates for you. ## [](#declarative-agent-components)Declarative agent components When you create an agent, you configure the components through ADP rather than writing code: - **System prompt**: Defines the agent’s role, responsibilities, and constraints - **LLM**: Interprets user intent and decides which tools to invoke - **Tools**: External capabilities exposed through the [Model Context Protocol (MCP)](https://docs.redpanda.com/agentic-data-plane/connect/mcp-overview/) - **Context**: Conversation history, tool results, and real-time events from Redpanda topics Agents reach these tools through MCP servers. Redpanda Connect pipelines can also invoke agents for event-driven processing, so agents support both interactive workflows and automated streaming. When a user makes a request, the LLM receives the system prompt and context, decides which tools to invoke, and processes the results. This cycle repeats until the task completes. For a deeper understanding of how agents execute, manage context, and maintain state, see [Understand Agent Concepts](https://docs.redpanda.com/agentic-data-plane/connect/concepts/). ## [](#key-benefits)Key benefits ADP agents are designed to reduce the infrastructure and integration work of building production AI systems. - Declarative configuration: Configure agent behavior instead of coding it, with access to a broad library of prebuilt tools and integrations for data sources, APIs, and services. - Real-time streaming data: Agents access live events instead of batch snapshots. - [Remote MCP](https://docs.redpanda.com/agentic-data-plane/connect/mcp-overview/) support: Standardized tool access across any MCP-compatible server. - Managed infrastructure: Deployment, scaling, and security handled for you. - Low-latency execution: Tools run close to your data. - Integrated secrets management: API keys and credentials stored securely. ## [](#use-cases)Use cases ADP AI agents unlock new capabilities across multiple fields. ### [](#for-ai-agent-developers)For AI agent developers Build agents grounded in real-time data instead of static snapshots. Connect your agent to live order status, inventory levels, and customer history so responses reflect current business state, not stale training data. ### [](#for-application-developers)For application developers Add conversational AI to existing applications without rebuilding your backend. Expose your services as MCP tools and let agents orchestrate complex multi-step workflows through natural language. ### [](#for-streaming-developers)For streaming developers Process every event with AI reasoning at scale. Invoke agents automatically from pipelines for fraud detection, content moderation, or sentiment analysis. No batch jobs, no delayed insights. ## [](#limitations)Limitations - MCP servers must be hosted in ADP - Cross-agent calling between separate agents is not supported. Use internal subagents for delegation within a single agent. ## [](#next-steps)Next steps - [Redpanda ADP Quickstart](https://docs.redpanda.com/agentic-data-plane/get-started/adp-quickstart/) - [Understand Agent Concepts](https://docs.redpanda.com/agentic-data-plane/connect/concepts/) - [Create an Agent](https://docs.redpanda.com/agentic-data-plane/connect/create-agent/)