---
type: source
title: "10x Your AI Agents with this ONE Agent Architecture"
description: "Fragmenting a single overloaded AI agent into a team of narrowly-scoped specialized sub-agents that run in parallel and are combined by a synthesizer agent produces far more powerful, focused agentic systems."
youtube_id: AgN3RHSZGwI
url: https://www.youtube.com/watch?v=AgN3RHSZGwI
slug: 10x-your-ai-agents-with-this-one-agent-architecture
published: 2025-03-23
duration: "0:41:36"
recency_rank: 107
raw: "../raw/10x-your-ai-agents-with-this-one-agent-architecture.md"
tags: [agent-teams, subagents, parallel-execution, pydantic-ai, langgraph]
created: 2026-07-21
updated: 2026-07-21
---

Cole argues that just like human teams, AI agents perform better the more narrow their role and goals are, and that stuffing one agent with ever more instructions and tools causes it to get overwhelmed and hallucinate. The fix is the parallel agent architecture (Anthropic's "parallelization" / orchestrator-workers pattern): a user request is fanned out to several specialized agents, each with its own tools and instructions, all running simultaneously, then their outputs are combined by an aggregator or, in Cole's variant, a synthesizer agent that formats and summarizes the final result.

He builds a travel-planner assistant to demonstrate, using Pydantic AI for the individual agents and LangGraph for the orchestration graph. Each Pydantic AI agent is framed as three parts: dependencies (API keys, DB connections, user preferences), the agent definition (model, system prompt, retries), and tools (functions with docstrings telling the LLM when and how to call them). Specialized flight, hotel, and activity agents each own a tool over mock data; an info-gathering agent uses structured outputs to guarantee all required trip details are collected before the parallel work begins (gatekeeping to prevent hallucination); and a tool-less synthesizer combines the three recommendations.

LangGraph is likewise framed as three parts: state (conversation history, travel details, per-agent results, final plan), nodes (each running an agent or deterministic code), and the graph itself (nodes wired with edges, including a conditional edge that loops on the info-gathering agent and a router that returns a list of nodes to trigger true simultaneous parallel execution). The video also covers streaming structured and text output token-by-token, human-in-the-loop via LangGraph's interrupt, and a memory saver for persisting graph state.

## Concepts covered

- [Subagents Pattern](../concepts/subagents-pattern.md)
- [Agent Teams](../concepts/agent-teams.md)
- [Context Rot](../concepts/context-rot.md)
- [Structured Outputs](../concepts/structured-outputs.md)
- [Human in the Loop](../concepts/human-in-the-loop.md)
- [Separate Reviewer](../concepts/separate-reviewer.md)
- [Deterministic Workflows](../concepts/deterministic-workflows.md)
- [Memory Systems](../concepts/memory-systems.md)
- [Streaming Responses](../concepts/streaming-responses.md)

## Entities

- [Pydantic AI](../entities/tools/pydantic-ai.md)
- [LangGraph](../entities/tools/langgraph.md)
- [Archon](../entities/tools/archon.md)
- [Anthropic](../entities/organizations/anthropic.md)
- [Streamlit](../entities/tools/streamlit.md)
- [Lutra](../entities/tools/lutra.md)
- [OpenAI](../entities/organizations/openai.md)
- [Cole Medin](../entities/people/cole-medin.md)

## Key moments

- **[0:01:52]** Defines the parallel agent architecture via Anthropic's "Building Effective Agents" article
- **[0:03:05]** Cole's variant: replace the non-LLM aggregator with a synthesizer AI agent
- **[0:03:39]** Introduces the travel-planner assistant and its LangGraph workflow
- **[0:04:42]** Streamlit UI demo showing all sub-agents printing at the exact same time
- **[0:05:52]** Points to Archon as a full-fledged real-world use of the parallel architecture
- **[0:08:20]** Pydantic AI agents as three parts: dependencies, agent definition, tools
- **[0:17:55]** Info-gathering agent uses structured outputs to gatekeep before parallel work
- **[0:20:56]** LangGraph as three parts: state, nodes, and the graph
- **[0:33:52]** Returning a list of nodes from a router = simultaneous parallel execution
- **[0:34:37]** Human-in-the-loop via LangGraph interrupt + memory saver for state persistence

## Transcript

[Raw transcript](../raw/10x-your-ai-agents-with-this-one-agent-architecture.md)
