👾 5 Multi-Agent Architectures Explained - The Future Is Now

Welcome to Edition #6 of Agents Made Simple

AI programming got another upgrade this week, with Google now leading the rankings. We will look at how to design agentic systems with agents that effectively work together.

This week’s topics:

• Google’s new record-breaking Gemini model

• Specialized agents for research

• Mistral’s new model for business operations

• 5 multi-agent architectures for building intelligent systems

• Why the Agent Development Kit is trending

• Plus AI investments, community highlights, trending AI tools, and more

AI Agent News Roundup

💥 Breakthroughs

📈 Investments¹

🇺🇸 Nvidia released Parakeet V2, a powerful new open-source automatic speech recognition (ASR) model that can transcribe an hour of audio in a single second while achieving commercial-grade accuracy.

🇺🇸 Google introduced "implicit caching" for its Gemini 2.5 models, allowing automatic detection and reuse of cached content from API requests for up to 75% cost savings. Google agreed to fund the development of three nuclear sites to meet the vast energy needs of data centers, with each site generating up to 600 megawatts.

🇺🇸 Netflix unveiled its biggest update in over a decade with a major homepage redesign, alongside beta tests of a generative AI-powered search tool and a TikTok-style vertical video feed on its mobile app.

🇺🇸 LinkedIn announced a new AI-powered job search tool allowing users to find career opportunities that match their dream roles using natural language commands.

🇺🇸 Amazon is reportedly developing its own AI coding app, code-named 'Kiro', which will leverage agents for developer tasks and feature multimodal capabilities.

🇺🇸 Shopify released an upgrade to its Sidekick AI assistant, integrating new reasoning capabilities and free image generation tools for merchants on the platform.

🇺🇸 Microsoft and OpenAI are reportedly negotiating to rewrite their partnership's terms, with OpenAI seeking to cut Microsoft's revenue and Microsoft eyeing access to OpenAI's tech beyond 2030.

🇨🇳 Alibaba researchers introduced ZeroSearch, a technique that teaches AI systems to search for information without using real search engines, cutting training costs by 88% while matching or outperforming models trained with actual search APIs.

🇨🇳 Tencent launched HunyuanCustom, a new open-source AI system that generates customized video from text, images, audio, and video inputs with consistent subjects.

🇨🇳 Baidu filed a patent for a system that uses AI to translate data from animal sounds, behavior, and emotional states into human language.

🇦🇪 The United Arab Emirates announced mandatory AI education for all K-12 students starting this year, as part of the country's strategy to establish regional AI leadership.

Multi-Agent Architectures: Building Blocks for Intelligent Systems

When building AI systems, you have two main options: a single agent or multiple agents working together. A single agent uses one LLM as the brain of the operation. It handles all tasks, manages tools, and evaluates results. This works for simple tasks. But complex problems need a different approach.

Multi-agent systems split work among specialized agents. Each handles specific tasks. Often, one agent acts as the lead coordinator. This approach solves a common problem: performance drops when one agent tries to do too much.

By creating agents with smaller, well-defined tasks, each can excel at its role. You can use different models for different agents based on their needs. Some agents can even communicate with agents on other systems (agent-to-agent or A2A communication).

Let's look at six architectures to arrange these agents.

1. Hierarchical (Vertical) Architecture

In this setup, a supervisor agent coordinates several specialized agents beneath it. The supervisor delegates tasks, manages workflow, and synthesizes results.

Example: A content research system where one agent retrieves information from internal databases, another gathers public information from web searches, and a third accesses personal accounts like email or chat histories. The supervisor combines these findings into a comprehensive report.

2. Human-in-the-Loop Architecture

This architecture integrates human verification at critical decision points. The system pauses for human approval before proceeding to the next steps.

Example: A contract drafting system where agents create different sections of legal documents, but a human lawyer reviews each section before the final assembly. This maintains quality control while automating routine aspects.

3. Network (Horizontal) Architecture

Agents communicate directly with each other in a decentralized network. There's no strict hierarchy, allowing for flexible collaboration.

Example: A customer service system where specialized agents handle different aspects of customer inquiries (billing, technical support, shipping) and communicate directly with each other to resolve complex issues without centralized coordination.

4. Sequential Architecture

Agents work in a defined sequence, with each agent's output becoming the next agent's input.

Example: A research assistant with three sequential agents: the first retrieves information from vector search, the second gathers additional details from web searches based on those initial findings, and the final agent synthesizes all information into a comprehensive response.

5. Data Transformation Architecture

This architecture focuses on agents that process and transform data at different stages.

Example: A content system where one agent transforms raw financial data into structured formats, another enriches it with market context, and a third converts the enriched data into natural language reports tailored to different audience needs.

Common Agentic Patterns

Beyond these architectures, several patterns can be applied across different systems:

• Loop Pattern: Agents work iteratively on tasks, refining outputs through multiple passes.

• Parallel Pattern: Multiple agents work simultaneously on different parts of a complex task.

• Router Pattern: A specialized agent evaluates incoming requests and directs them to the most appropriate agent.

• Aggregator/Synthesizer Pattern: A dedicated agent collects and combines outputs from multiple sources into coherent results.

The power of multi-agent systems comes from this flexibility. By combining different architectures and patterns, you can create AI systems that handle complex workflows while maintaining specialized expertise in each component.

As these systems evolve, we're seeing more sophisticated collaboration between agents. The ability to break down complex tasks into specialized roles mirrors how effective human teams operate.

Multi-agent architectures create more capable, reliable, and scalable AI solutions. I covered just a few examples. They show how to think about breaking down complex tasks into separate agents.

The real power comes from how you connect these pieces into a complete system. Start with these building blocks. Then create architectures that fit your specific needs.

Tool Spotlight

👾 Google’s Agent Development Kit (ADK)

The ADK breaks barriers in AI development. It’s open-source and lets you build enterprise-grade multi-agent systems.

How It Works

ADK uses a Python-based approach. You define each agent’s role, tasks, what tools it can use, and how it processes information. The framework handles the rest.

from google.adk.agents import LlmAgent
from google.adk.tools import google_Search

agent = LlmAgent(
    model="gemini-2.0-flash-exp",
    name="question_answer_agent",
    description="A helpful assistant that answers questions.",
    instruction="Respond using google search",
    tools=[google_search]
)

Key Benefits

• Multi-Agent Architecture: Build systems where specialized agents work together in a hierarchy.

• Model Flexibility: Optimized for Gemini models but also supports others.

• Rich Tool Ecosystem: Use pre-built tools like Search and Code Execution or connect third-party libraries (MCP, LangChain, LlamaIndex, CrewAI, etc)

• Natural Interactions: Create voice and video conversations with built-in streaming.

• Smart Workflows: Define precise sequences with workflow agents or let the AI decide the best path forward.

• Developer-Friendly: Test and debug locally with a CLI and a visual Web UI.

• Built-In Testing: Evaluate your agents against test cases before deployment.

• Easy Deployment: Move from development to production with minimal effort.

Cloud-Ready But Not Cloud-Dependent

ADK works anywhere but is optimized for the Google Cloud ecosystem.

For businesses, the real game-changer is connectivity. ADK connects directly to your systems through over 100 pre-built connectors. This means your agents can access your data, workflows, and existing APIs.

Who Should Use It?

The framework can be used for simple single-agent flows but also complex, collaborative AI systems. ADK offers a very direct path forward.

Learn more at Google ADK Documentation.

Community Highlights

Made By Agents Updates

Trending AI Tools

👾 ADK: A developer toolkit for building and deploying AI agents that wrap Python functions and third-party libraries as callable “tools,” enabling agents to perform real-world actions and workflow orchestration autonomously.

🙋‍♀️ HeyGen: An AI-driven video creation platform that transforms scripts into professional, AI-avatar videos with high-quality voices, dubbing, and localization at scale—without needing a camera or studio.

🌅 Reve: An advanced text-to-image generation service that produces high-resolution, aesthetically precise images from prompts and allows users to iteratively modify existing visuals via simple language commands.

☎️ Twilio: A cloud communications platform as a service (CPaaS) offering programmable APIs for SMS, voice, video, email, and omnichannel messaging to seamlessly embed real-time customer engagement into applications.

More Resources

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See you next time!

Tobias from MadeByAgents

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