Agentic RAG Orchestration: Multi-Agent Gemini & Antigravity SDK

As enterprise AI applications shift from static chat interfaces to autonomous agents, organizing agent interaction becomes the core architectural challenge. A single LLM call is rarely sufficient for complex, multi-step actions. Instead, we orchestrate a network of specialized agents, each equipped with specific tools and scopes.

Using Google's Antigravity SDK alongside the Gemini API, developers can define and chain agentic pipelines that automatically route tasks based on intent.

Architecture of Multi-Agent Systems

In a standard multi-agent setup, we define a Router agent that parses the user's initial request and delegates it to downstream agents:

1. Researcher Agent: Queries internal databases, vector stores, and web APIs.
2. Coder Agent: Writes, runs, and debugs code inside a secure sandbox.
3. Reviewer Agent: Audits output quality and safety standards before returning results.

This architecture can be visualised as an interactive node network dashboard:

Google Antigravity SDK

System Prompt

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A sleek glowing dark UI dashboard showing a network node graph of multi-agent AI workflows. Lines connecting glowing neural nodes with micro-metrics. Translucent glass containers, blue and purple neon highlights, professional technical layout.

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Orchestration in Python

To orchestrate this workflow programmatically, the Antigravity SDK provides simple definitions. Below is the script initializing the Router and executing a delegated workflow:

500 italic"># Multi-agent setup "text-brand-cyan font-bold">with Antigravity SDK and Gemini
"text-brand-cyan font-bold">from antigravity "text-brand-cyan font-bold">import Agent, Workspace, Router

500 italic"># Create isolated workspace
workspace = Workspace(name="rag-env")

500 italic"># Define specialized agents
researcher = Agent(
    name="CodebaseResearcher",
    system_prompt="Analyze codebases and extract symbol definitions.",
    tools=["ripgrep_search", "view_file"],
    workspace=workspace
)

coder = Agent(
    name="SoftwareDeveloper",
    system_prompt="Create and edit source files ">in the project.",
    tools=["write_file", "replace_content"],
    workspace=workspace
)

500 italic"># Initialize router agent to orchestrate execution
router = Router(agents=[researcher, coder])
result = router.dispatch("Refactor database connections to use pooled connections.")
"text-brand-cyan font-bold">print("Workflow output:", result)

With this architecture, agent loops run autonomously, self-correcting syntax errors and checking files before declaring success.