Agentic Data Plane

How Agents Work

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?

An AI agent is a system built around a 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

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)

  • 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.

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 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

ADP AI agents unlock new capabilities across multiple fields.

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

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

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

  • 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