📰DZone Microservices·February 25, 2026

Docker Cagent: A Declarative Platform for AI Agent Orchestration

Docker Cagent introduces a new low-code, YAML-centric approach to building and running AI agents, simplifying their deployment and orchestration. It shifts from traditional programmatic agent frameworks to a declarative model, allowing developers to define agent personas and capabilities in portable YAML files. This platform is designed for rapid deployment and standardized tasks, integrating with various LLM providers and facilitating multi-agent workflows.

AI & ML Infrastructure Distributed Systems Microservices

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Docker Cagent is an open-source framework designed to simplify the creation and execution of AI agents. It diverges from traditional agentic frameworks, which often require extensive programming in languages like Python, by adopting a configuration-first, declarative philosophy. This design choice aims to reduce complexity and accelerate development cycles for AI agent solutions.

Declarative Agent Definition and Orchestration

The core of Cagent's architecture lies in its YAML-centric approach. Instead of writing custom code for agent logic and orchestration, developers define an agent's persona, capabilities, and instruction sets within a single, portable YAML file. This decouples the agent's logic from the underlying infrastructure, promoting easier distribution and management. For multi-agent systems, Cagent enables the definition of a root agent that orchestrates sub-agents, delegating tasks and consolidating outputs declaratively.

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Trade-offs in Design

Cagent prioritizes rapid deployment and standardized tasks, achieving portability and execution speed. However, this comes at the cost of granular programmatic control, which frameworks like LangGraph or AutoGen offer for architectural flexibility and complex reasoning loops. System designers must weigh the benefits of speed and simplicity against the need for deep customization and intricate control when choosing an agent orchestration platform.

Key Architectural Components

Model Context Protocol (MCP): An open-source standard for connecting agents with real-world entities like databases, search engines, and APIs. Cagent integrates with MCP to extend agent capabilities.
Docker Model Runner (DMR): Facilitates integration with multiple LLM providers, offering vendor-agnostic support for various AI models.
Toolsets: Pre-defined or custom tools (e.g., filesystem, shell, memory, MCP-based tools) that agents can leverage to perform actions and interact with external systems.
Docker Registry Integration: Enables simplified distribution of agent configurations by pushing them to Docker Hub, similar to container images.

Example: Multi-Agent Workflow for Content Creation

A practical application of Cagent's multi-agent capabilities involves setting up a workflow for technical content creation. A 'Project Manager' root agent orchestrates a 'Researcher' sub-agent (specialized in API documentation retrieval using MCP) and a 'Writer' sub-agent (specialized in technical content generation). The Project Manager delegates tasks, ensuring the Researcher gathers relevant information and the Writer transforms it into a polished output, demonstrating a simple yet powerful declarative workflow.

yaml

version: "1"
agents:
  root:
    model: openai/gpt-4o
    description: "Project Manager for AI Integration guides."
    instruction: |
      You are the Project Manager. Your goal is to explain how to build an agent using the Gemini API.
      1. Ask the 'researcher' to find the specific Gemini API methods for "System Instructions" and "Tool Use".
      2. Send the researcher's findings to the 'writer'.
      3. Ensure the final blog post includes a clear code example discovered by the researcher.
    sub_agents:
      - researcher
      - writer
  researcher:
    model: openai/gpt-4o-mini
    description: "Gemini Documentation Specialist."
    instruction: |
      You gather technical specifications from the Gemini API documentation. Focus on finding:
      - How to initialize the model.
      - How to pass 'system_instruction' to an agent.
      - The syntax for 'tools' (function calling).
    toolsets:
      - type: mcp
        ref: docker:gemini-api-docs
  writer:
    model: openai/gpt-4o
    description: "Technical Content Creator."
    instruction: |
      You take technical research notes and turn them into a polished blog post. Explain the Gemini API implementation in a way that a developer can follow. Always include a Python or Node.js snippet based on the researcher's data.

AI AgentsLow-CodeOrchestrationYAMLDockerDeclarativeLLM IntegrationMulti-Agent Systems

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Architecture Design

Design this yourself

Design a scalable platform for deploying and orchestrating AI agents, similar to Docker Cagent. Your platform should support declarative agent definitions using YAML, integrate with multiple LLM providers, allow for toolset integration (e.g., via Model Context Protocol), and facilitate multi-agent workflows with clear delegation and coordination between specialized agents. Consider how to handle agent distribution, versioning, and lifecycle management in a cloud-native environment.

Focus: AI agent orchestration platform with declarative configuration

Other design angles

· Design a custom AI agent framework that prioritizes programmatic control and architectural flexibility over rapid deployment, focusing on complex reasoning loops and custom integrations.· Design a system for managing and deploying AI models as microservices, focusing on inference serving, model versioning, and scaling strategies for various LLM providers.· Design a multi-tenant SaaS platform that leverages AI agents for automated customer support, ensuring secure tenant isolation, configurable agent personas, and efficient resource allocation across tenants.