> Markdown version of [/videos/1976-designing-and-deploying-distributed-multimodal-multi-agent-systems-with-google-s-ai-stac](https://www.wearedevelopers.com/videos/1976-designing-and-deploying-distributed-multimodal-multi-agent-systems-with-google-s-ai-stac). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

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# Designing and Deploying Distributed Multimodal Multi-Agent Systems with Google's AI Stac

Tired of brittle integrations and monolithic AI workflows? Discover how to build scalable, multi-agent microservices using Google's AI stack to execute complex multimodal tasks in seconds.

- **Speakers:** [Saoussen Chaabnia](https://www.wearedevelopers.com/@saoussen-chaabnia)
- **Event:** World Congress 2026 Europe - Virtual Stage
- **Published:** July 2, 2026
- **Duration:** 54:24
- **URL:** https://www.wearedevelopers.com/videos/1976-designing-and-deploying-distributed-multimodal-multi-agent-systems-with-google-s-ai-stac

## Summary

Modern AI workflows often struggle with monolithic designs and brittle third-party integrations. By leveraging Google's AI stack, developers can build sophisticated, decentralized platforms like an 'AI Creative Studio'—a distributed, multimodal multi-agent system that transforms a single text brief into a comprehensive Instagram campaign in under a minute. Instead of pairing heavy central processing with complex hard-coded dependencies, this architecture uses a stateful 'Creative Director' orchestrator to coordinate five independent specialist agents, such as a Brand Strategist and a Designer. These specialists mirror real-world marketing teams, intelligently executing tasks spanning market research, multimodal image generation, automated quality critiques, and timeline creation.

The engine powering this separation of concerns relies on four foundational technologies. The Agent Development Kit (ADK) provides flexible foundational classes and dynamic 'Skills' that load domain knowledge solely on demand, preventing token bloat and 'context rot'. For orchestration, the Agent-to-Agent (A2A) protocol acts essentially as HTTP for AI. It utilizes metadata 'Agent Cards' and JSON-RPC, enabling independent tools to dynamically discover and invoke each other's capabilities at runtime rather than relying on brittle endpoint URLs. Simultaneously, external tool integration is radically simplified through the Model Context Protocol (MCP), which eliminates custom API glue code by providing a standardized server layer to seamlessly parse and write to complex, dynamic platforms like Notion.

Architecturally, this decoupled approach unlocks immense enterprise scalability. The lightweight, stateless specialist agents execute efficiently on Cloud Run while the orchestrator utilizes Gemini Enterprise Agent Runtime to securely maintain operational state and memory context across long-running, multi-agent workflows. This serverless hybrid pattern proves that modular AI microservices—freed from monolithic prompts and custom networking code—offer a highly resilient, maintainable, and cost-effective foundation for next-generation enterprise automation.

**Keywords:** distributed multi-agent systems, google AI stack, agent development kit ADK, agent-to-agent protocol A2A, model context protocol MCP, gemini enterprise agent runtime, cloud run deployment, multimodal AI workflows, automated marketing campaigns, notion MCP server, dynamic agent discovery, AI microservices architecture, mitigating context rot, stateful orchestrator patterns, JSON-RPC agent communication, multimodal image generation, agent cards metadata

## Chapters

1. **Introduction to distributed multi-agent systems** (00:00) — An overview of building distributed AI workflows utilizing infrastructure stacks.
1. **Overview of the campaign generation application** (00:47) — A reference application demonstrating five specialist agents coordinated by a centralized orchestrator.
1. **Designing decoupled microservice architectural patterns** (02:05) — How centralized orchestrators dispatch tasks downstream without explicitly hardcoding network endpoints.
1. **Introducing foundation technologies for autonomous networks** (03:43) — The core framework tools required to generate, test, and deploy model-agnostic agents safely.
1. **Utilizing the ADK framework for agent logic** (04:29) — Executing complex reasoning loops automatically using open-source abstraction modules and tracking interfaces.
1. **Core components for structuring individual agents** (05:58) — Defining agents through discrete configuration blocks assigned to reasoning models, isolated tools, and runtimes.
1. **Defining operating procedures with system instructions** (07:34) — Writing concise agent implementations configured with specific responsibilities, structured formats, and explicit capabilities.
1. **Managing context windows using dynamic skills** (09:49) — Loading domain knowledge into prompt constraints only when triggered to avoid token waste.
1. **Wrapping multimodal toolset configurations for deployment** (14:10) — Structuring multimodal outputs like image generation to be passed precisely into cloud storage directories.
1. **Assembling orchestrators without hardcoded function routes** (15:45) — Wrapping fully operational subordinate agents as direct tools for an orchestrator to manage organically.
1. **Standardizing network configurations via A2A protocols** (17:31) — Allowing language-agnostic components to communicate freely regardless of original programming environments or infrastructure.
1. **Discovering target capabilities using static cards** (19:33) — Fetching explicit JSON configuration descriptors to establish operational bounds automatically at runtime.
1. **Processing remote instructions using JSON RPC** (23:17) — Formatting executable interactions reliably as standard query structures served natively over HTTP connections.
1. **Binding executable logic into network servers** (24:09) — Abstracting protocol boilerplate files to expose standard container code as isolated network endpoints.
1. **Orchestrating dynamic connections gracefully at scale** (25:37) — Directing parent platforms at external metadata variables to parse specific tool functions efficiently.
1. **Utilizing the model context protocol layer** (27:05) — Connecting to external database environments rapidly without managing complicated authentication patterns internally.
1. **Mapping structural dependencies with external implementations** (29:38) — Discovering nested schemas continuously without rebuilding dependencies using standard third-party proxy platforms.
1. **Persisting application states across execution runtimes** (32:23) — Storing asynchronous tasks securely on dedicated container hardware explicitly designed for memory resilience.
1. **Releasing orchestrated infrastructure projects via deployment APIs** (36:26) — Pushing functional code directories instantly using single configuration lines for immediate cloud production.
1. **Live monitoring and testing deployed systems** (39:57) — Evaluating complex session timelines against multiple agent handoffs in cloud execution interfaces.
1. **Accessing tutorial implementations and codelab instances** (53:05) — Resources and deployable examples containing infrastructure schemas to build autonomous applications effectively.

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