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Agents Towards Production
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
Install
$
git clone https://github.com/NirDiamant/agents-towards-productionInstall command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →Agents Towards Production
The open-source playbook for turning AI agents into real-world products.
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.
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🎓 From prototype to production, as a method
Prompt to Production - my full course on building software with AI the way professionals do: the methods and paradigms behind reliable, efficient, modular production systems, taught systematically. 17 modules, each pairing a video lecture with a hands-on lab, from your first structured prompt to a working production system.
The course is live. Every module is out, lecture and lab.
🎁 Try it on your own repo, free
Your coding agent starts every session knowing nothing about your project, so it guesses. Paste one line into the agent you already have open, and about fifteen minutes later your repository has a docs layer written from the code itself, plus a card scoring what your agent knew before and after.
We ran it on six repositories you already depend on. Each one was asked five questions about itself, cold, then again after the layer was written. The last column counts statements in that project's own documentation that its own code disproves:
| repo | before | after | own docs its code disproves |
|---|---|---|---|
| fastapi | 2 of 5 | 5 of 5 | 2 |
| flask | 3 of 5 | 5 of 5 | 6 |
| django | 3 of 5 | 4 of 5 | 1 |
| express | 2 of 5 | 4 of 5 | 4 |
| requests | 2 of 5 | 4 of 5 | 0 |
| langchain | 5 of 5 | 4 of 5 | 5 |
Flask's six include four documentation examples that raise TypeError when you run them. Langchain scored lower afterwards, because it already ships a 380-line agent instruction file and the cold read was grading theirs; that row is in the table anyway.
Clone any of those repos, paste the same line, and check the number yourself. No signup.
👉 Get the full course
28 production-grade tutorials covering stateful workflows, vector memory, web search APIs, Docker deployment, security guardrails, GPU scaling, multi-agent coordination, and more.
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