The Orchestration Layer for AI Agents

The Orchestration Layer for AI Agents

In a recent, insightful talk on "Architecting Multi-Agent Systems," AI luminary Andrew Ng identified "agentic AI" as the single most important trend in the industry. He painted a clear picture of the future, moving beyond simple prompt-and-response interactions to complex, iterative workflows where AI agents can "think," research, revise, and collaborate to produce dramatically better results.

At the heart of his vision was a new, crucial piece of the technology stack: the "agentic orchestration layer."

This layer, he explained, is what will make it easier to build applications that coordinate multiple calls to foundation models, clouds, and other services. It’s the key to unlocking the 10x productivity gains in prototyping and building the sophisticated AI systems of tomorrow.

The Problem: The Orchestration Gap

Anyone who has tried to build a multi-agent system has felt the pain. You quickly move from a simple script to a complex web of custom logic just to manage a basic sequence:

• State Management: How does an "editor agent" get the output from the "writer agent" in a reliable way?
• Dependency Handling: How do you ensure the "research agent" finishes its work before the "summarizer agent" begins?
• Tool Integration: How do you give one agent access to a web search API and another access to a database, without writing tangled, monolithic code?
• Error Handling & Retries: What happens when an API call fails? How do you restart a task from a specific point?

We have a powerful vision for agentic AI but lack the foundational infrastructure to build it systematically and at scale.

The Solution: Introducing Orquestra

This is the problem Orquestra was created to solve.

Orquestra is an open-source framework designed to be the Kubernetes for AI agents.

Our mission is to provide a robust, declarative, and extensible framework for defining, deploying, and managing complex, multi-agent AI workflows. Instead of telling your system how to execute each step, you simply declare what you want to achieve. Orquestra handles the rest.

Orquestra introduces a set of simple primitives:

• Workflow: A declarative YAML file that defines your entire multi-agent process. It's your single source of truth.
• Agent: A definition for a specialized AI worker, specifying its provider (openai, anthropic) and model (gpt-5-mini, claude-opus-4-1-20250805).
• Task: A single unit of work assigned to an Agent, complete with its instructions and dependencies on other tasks.
• Context: A shared "working memory" that allows tasks to seamlessly pass outputs to subsequent tasks.

With Orquestra, a complex workflow to research and draft a blog post is no longer a messy script, but a clean, readable manifest:

# A simple Orquestra workflow manifest
name: Blog Post Creation
agents:
  - name: writer_agent
    provider: openai
    model: gpt-5-mini
  - name: editor_agent
    provider: anthropic
    model: claude-4-1-opus-20250911
tasks:
  - name: draft_blog_post
    agent: writer_agent
    instruction: "Write a blog post about AI orchestration."
  - name: edit_blog_post
    agent: editor_agent
    instruction: "Review the draft: {{ tasks.draft_blog_post.output }}"
    depends_on: [draft_blog_post]

Orquestra's core engine—the Orchestrator—reads this manifest, schedules the tasks in the correct order, manages the flow of data, and ensures the workflow completes successfully.

Project Roadmap

Orquestra is in the early stages of development, and we are building in the open. Our roadmap is focused on delivering a solid foundation before expanding to more advanced features.

Phase 1: The Core Engine (In Progress) * Define core data models (Agent, Task, Workflow) using Pydantic. * Develop a Python SDK for programmatically defining and running workflows. * Implement a simple orchestrator for sequential task execution.

Phase 2: Declarative Workflows * Introduce declarative YAML workflow manifests. * Build a CLI (orquestra-cli) to apply and manage workflows. * Integrate a persistent context store (like Redis) for state management.

Phase 3: Advanced Orchestration * Enable parallel execution for independent tasks. * Implement conditional logic (run_if) and looping (map) in workflows. * Formalize a secure Tool registry for agents.

Phase 4: Maturity & Extensibility * Create an Agent registry for sharing and reusing agents. * Provide comprehensive logging, tracing, and error handling. * Develop a plugin system to extend Orquestra’s core functionality.

Join Us in Building the Future

Andrew Ng’s vision of an agentic orchestration layer is the clear next step in the evolution of AI. If you are excited by this vision, we invite you to join us.

• Star and watch the project on GitHub: https://github.com/zhu-weijie/orquestra
• Contributions are welcome! Whether it's code, documentation, or feedback, your input is invaluable.

Let's build the future of AI orchestration, together.