---
type: source
title: "Revealing my COMPLETE AI Agent Blueprint"
description: "Building a production-ready AI agent follows a repeatable end-to-end blueprint: plan, prototype in no/low-code, add a database, optionally move to Python, build a UI, test, deploy, monitor, and evaluate."
youtube_id: FGeB9w1ZeHE
url: https://www.youtube.com/watch?v=FGeB9w1ZeHE
slug: revealing-my-complete-ai-agent-blueprint
published: 2024-12-22
duration: "0:14:40"
recency_rank: 131
raw: "../raw/revealing-my-complete-ai-agent-blueprint.md"
tags: [agent-development, production, blueprint, deployment]
created: 2026-07-21
updated: 2026-07-21
---

# Revealing my COMPLETE AI Agent Blueprint

This video opens a miniseries laying out Cole Medin's complete, repeatable process for taking an AI agent from idea to production, using an upcoming GitHub-repository Q&A agent as the running example. The [Agent Development Lifecycle](../concepts/agent-development-lifecycle.md) starts with deliberate [planning](../concepts/planning-with-ai.md): pin down core functionality, [which LLM](../concepts/model-selection.md) to use, which APIs are needed, and what a realistic V1 looks like, so no effort is wasted. Only then does he build a fast, working [prototype](../concepts/rapid-prototyping.md) in a no/low-code tool like [n8n](../entities/tools/n8n.md), [Flowise](../entities/tools/flowise.md), or [Voiceflow](../entities/tools/voiceflow.md), before touching a front end or database.

From there the blueprint layers on infrastructure: a simple [Supabase](../entities/tools/supabase.md)/Postgres database for chat history and [RAG](../concepts/rag.md), an optional migration to Python ([Pydantic AI](../entities/tools/pydantic-ai.md) plus [LangGraph](../entities/tools/langgraph.md), aided by [Cursor](../entities/tools/cursor.md) or [Windsurf](../entities/tools/windsurf.md)) when more customization is needed, and a clean UI built with [Lovable](../entities/tools/lovable.md) or Bolt (React), [Streamlit](../entities/tools/streamlit.md), or his [Live Agent Studio](../entities/tools/live-agent-studio.md). He insists on real testing (unit and integration, to catch errors and edge cases) as a distinct step from later evaluation.

The production phase means containerizing with [Docker](../entities/tools/docker.md), hosting on [RunPod](../entities/tools/runpod.md) (GPU and local LLMs) or [DigitalOcean](../entities/organizations/digital-ocean.md) (general instances), and fronting the agent with a [FastAPI](../entities/tools/fastapi.md) API for [deployment](../concepts/agent-deployment.md). The final, interchangeable steps are [monitoring](../concepts/agent-observability.md) with [LangSmith](../entities/tools/langsmith.md), [Langfuse](../entities/tools/langfuse.md), or [Logfire](../entities/tools/logfire.md), and [agent evaluation](../concepts/agent-evaluation.md), verifying the agent gives correct answers and takes correct actions, not just that it runs. He closes by naming advanced enterprise concerns (prompt caching, token-window management, batching, load balancing, security, rate limiting, input sanitization) as ongoing improvements, weighing [no-code vs. code](../concepts/no-code-vs-code.md) at each layer.

## Concepts covered

- [Agent Development Lifecycle](../concepts/agent-development-lifecycle.md)
- [Rapid Prototyping](../concepts/rapid-prototyping.md)
- [Model Selection](../concepts/model-selection.md)
- [Retrieval-Augmented Generation (RAG)](../concepts/rag.md)
- [Production-Grade Agents](../concepts/production-grade-agents.md)
- [Agent Evaluation](../concepts/agent-evaluation.md)
- [Agent Observability](../concepts/agent-observability.md)
- [Agent Deployment](../concepts/agent-deployment.md)
- [No-Code vs. Code](../concepts/no-code-vs-code.md)

## Entities

- [Cole Medin](../entities/people/cole-medin.md)
- [n8n](../entities/tools/n8n.md)
- [Voiceflow](../entities/tools/voiceflow.md)
- [Flowise](../entities/tools/flowise.md)
- [Google Gemini 2.0 Flash](../entities/tools/gemini.md)
- [Supabase](../entities/tools/supabase.md)
- [Pydantic AI](../entities/tools/pydantic-ai.md)
- [LangGraph](../entities/tools/langgraph.md)
- [Cursor](../entities/tools/cursor.md)
- [Windsurf](../entities/tools/windsurf.md)
- [Lovable](../entities/tools/lovable.md)
- [Streamlit](../entities/tools/streamlit.md)
- [Docker](../entities/tools/docker.md)
- [RunPod](../entities/tools/runpod.md)
- [DigitalOcean](../entities/organizations/digital-ocean.md)
- [FastAPI](../entities/tools/fastapi.md)
- [LangSmith](../entities/tools/langsmith.md)
- [Langfuse](../entities/tools/langfuse.md)
- [Logfire](../entities/tools/logfire.md)
- [Live Agent Studio](../entities/tools/live-agent-studio.md)

## Key moments

- **[0:02:29]** Step 1, plan your agent: core functionality, which LLM, which APIs, what a good V1 looks like
- **[0:03:43]** Step 2, build a fast, powerful prototype in a no/low-code tool (n8n, Flowise, Voiceflow)
- **[0:06:31]** Step 3, set up a database (Supabase/Postgres) for chat history and RAG
- **[0:07:11]** Step 4 (optional), move the agent to Python for customization (Pydantic AI, LangGraph)
- **[0:08:13]** Step 5, build a UI (Bolt/Lovable React, Streamlit, or Live Agent Studio)
- **[0:09:28]** Step 6, test the agent (unit and integration) to cover edge cases
- **[0:10:13]** Step 7, deploy: containerize with Docker, host on RunPod or DigitalOcean, front with FastAPI
- **[0:11:35]** Step 8, set up monitoring (LangSmith, Langfuse, Logfire)
- **[0:12:06]** Agent evaluation, ensuring correct responses/actions, distinct from testing
- **[0:12:56]** Advanced enterprise topics: cost optimization, load balancing, security, rate limiting

## Transcript

[Raw transcript](../raw/revealing-my-complete-ai-agent-blueprint.md)
