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A decision guide to building AI agents that work: 28 chapters, 148 illustrations, no code.
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Agents Towards Production is your go‑to resource for building production‑ready GenAI agents that scale from prototype to enterprise. Tutorials cover stateful workflows, vector memory, real‑time web search APIs, Docker deployment, FastAPI endpoints, security guardrails, GPU scaling, browser automation, fine‑tuning, multi‑agent coordination, observability, evaluation, and UI development.
Related repos: RAG Techniques · GenAI Agents · Prompt Engineering · Agent Memory Techniques
AI Agents Made Simple - the decision guide to building agents that work: 28 chapters on what each pattern does, what breaks, what it costs, and when it is overkill. No code.
28 production-grade tutorials covering stateful workflows, vector memory, web search APIs, Docker deployment, security guardrails, GPU scaling, multi-agent coordination, and more.
Companies that have contributed step-by-step tutorials to this repository.
Click a logo to open the tutorial. Use Ctrl‑/⌘‑click to keep this page open.
Agent Framework & Workflows 
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Memory & Vector Database
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RAG & Knowledge Management
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Web Data Platform
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Real‑time Web Search API
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Self-Improving AI Memory
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GPU Cloud Computing
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Durable Execution
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Companies that support this project through partnerships and resources.
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AI Code Review
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I break these ideas down into short, one-idea-per-episode explainers on YouTube.
AI Agents Are Just While Loops. That's the Scary Part. the whole agent is a text file re-read in a loop, and it can trap itself |
Context Is the New Code the shift from writing the code to shaping what the model sees |
Stop Thinking Claude Code Is Magic. Here's How It Works what the agent loop is actually doing on every turn |
How LLMs Actually Work (and Why AI Makes Things Up) recalling a fact and inventing one are literally the same move |
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Agents Towards Production is your hands-on guide to every building block of a GenAI agent stack.
All knowledge is delivered through runnable tutorials covering orchestration, memory, observability, deployment, security, and more. Each tutorial lives in its own folder with ready-to-run notebooks or code files, so you can move from concept to working agent in minutes.
This diagram shows the flow of building a production-level agent. The tutorials in this repository cover each of these components step-by-step.
| Tutorial | Description | View |
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| Secure Tool Calling (Arcade) | Enable agents to securely call external tools (Gmail, Slack, Notion) with OAuth2 authentication and human-in-the-loop safety controls. Learn production-ready tool integration with user isolation and approval workflows. |
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| Tutorial | Description | View |
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Web Data Collection for AI Agents (Bright Data)  |
Build agents that collect and process web data at scale using enterprise-grade scraping infrastructure. Learn to integrate proxy networks, handle CAPTCHAs, and extract structured data from complex websites. |
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| Real-Time Web Data Integration for Agents (Tavily) | Enable agents to access, search, and extract real-time web data. Build workflows that combine live web information with private knowledge for research, monitoring, and up-to-date recommendations. |
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Agent File Conversion, Locally and Privately (hushvert)  |
Kept for reference: the hosted hushvert API this tutorial calls was retired on 12 Oct 2026, so its server-lane code no longer runs. Still useful for the two-lane routing rule (convert on the user's device when possible, send only what needs a server) and an honest fidelity benchmark against popular extractors. |
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| Tutorial | Description | View |
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| Durable RAG Ingestion (Inngest) |
Build an ingestion pipeline that survives partial failures, re-runs and backfills. Measure what a failed run at document 3,000 of 5,000 actually costs in duplicate chunks and re-billed embeddings, then fix it with idempotent upserts, durable steps, per-document fan-out, throttling and per-tenant concurrency, and a human review gate. |
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| Production-Ready RAG Agents with Contextual AI (Contextual AI) | Build enterprise-grade RAG systems in 15 minutes using Contextual AI's managed platform. Learn document processing, intelligent indexing, agent deployment, and automated evaluation with LMUnit testing framework for financial document analysis. |
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| Tutorial | Description | View |
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| Agent Memory: Dual-Memory & Semantic Search (Redis) | Implement dual-memory (short-term and long-term), semantic search, and persistent state for agents that remember user preferences and learn from conversations. |
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| Self-Improving Memory with Mem0: Hybrid Vector, Keyword & Entity Search |
Build intelligent agents with self-improving memory that automatically extracts insights and evolves with each interaction. Learn hybrid retrieval that combines vector search for semantic recall with keyword and entity matching, on mem0 2.x. |
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| AI Memory with Cognee | Build intelligent AI memory systems that learn from Python's creator and improve your development workflow. Transform scattered development data into unified knowledge graphs with contextual insights. |
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| Tutorial | Description | View |
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| AWS Bedrock AgentCore: Managed Agent Deployment |
Deploy and manage AI agents on AWS Bedrock AgentCore Runtime. Learn to transform local agents into production-ready managed services with automatic infrastructure, request tracking, and standardized communication patterns. |
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| Containerizing Agents with Docker | Containerize agents for portability and scalability. Learn foundational patterns for running agents in containers across environments. |
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| On-Prem LLM Deployment with Ollama | Run and interact with large language models locally. Replace cloud APIs with on-prem models for privacy, cost control, and low-latency agent workflows. |
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| Tutorial | Description | View |
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| Multi-Agent Communication with A2A Protocol | Simulate collaborative agent workflows and message exchange using open communication protocols for interoperability. |
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