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AI Coding Agents 100 Labs to Production Mastery
Published 9/2026
Created by Dar Al Taqniya
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 112 Lectures ( 11h 55m ) | Size: 1.1 GB
From Vibe Coding to production-grade AI agents-build, secure, evaluate, and deploy autonomous coding systems.
What you'll learn
Master AI coding-agent fundamentals by configuring terminal AI harnesses, local models, project context, Git workflows, approval gates, and safe code-generation Architect secure tool-calling systems using JSON Schema, Pydantic, least-privilege permissions, container sandboxes, network restrictions, credential scopes, ra Build stateful autonomous agents with LangGraph, persistent memory, RAG-based code retrieval, checkpointing, long-running tasks, recovery mechanisms, and Plan-A Engineer multi-agent development teams that coordinate planners, coders, reviewers, testers, debuggers, and routing agents to solve complex software-engineering Build professional evaluation infrastructure using test-generation pipelines, execution-based correctness metrics, LLM-as-a-Judge evaluation, regression testing Automate complex software modernization including AST-based refactoring, multi-file migrations, framework upgrades, API contract changes, database migrations, d Integrate autonomous agents into CI/CD and GitHub workflows to triage issues, respond to pull requests, review code, run tests, enforce branch protections, reco Operate production-grade agent infrastructure with OpenTelemetry, LangSmith, Prometheus, structured audit logs, cost monitoring, failure alerts, retrieval traci Implement enterprise AI governance including RBAC, PII protection, human-in-the-loop controls, emergency kill switches, compliance policies, risk scoring, spend Architect and deploy a sovereign autonomous coding platform using containers, Kubernetes, Ollama/vLLM, persistent storage, Redis/PostgreSQL, message queues, VauRequirements
Requirements & Prerequisites 1. 8 GB RAM minimum 2. A GitHub account 3. At least 30 GB of free disk space 4. Basic familiarity with using a terminal/command line 5. Basic understanding of programming concepts is helpful 6. Stable internet connection for downloading software, models, packages, and containers Software Used Throughout the Course 1. Python 3.12 2. Docker 3. Git and GitHub 4. Kubernetes 5. LangChain 6. LangGraph 7. Pydantic 8. Ollama 9. LangSmith 10. vLLM 11. OpenTelemetry 12. PrometheusDescription
This course contains the use of artificial intelligence.
I only charge a fee solely for the time invested in building this comprehensive curriculum.
Stop Vibe Coding. Start Engineering Autonomous Systems.
AI can already generate a function in seconds.
It can explain an error.
It can refactor a file.
It can suggest a pull request.
But there is a massive difference betweenasking an AI to write code andengineering an autonomous coding system that can safely modify a production codebase.
That difference is where modern AI engineering begins.
The first generation of AI-assisted development was largely about prompts and chat windows. The next generation is about agents that can inspect repositories, call tools, execute tests, retrieve context, modify multiple files, interact with GitHub, recover from failures, and operate continuously.
That creates enormous engineering opportunities-and equally enormous risks.
An uncontrolled coding agent can execute dangerous commands, leak credentials, introduce regressions, consume unlimited tokens, make architectural decisions nobody reviewed, or produce code that appears correct but fails under real execution.
This course is built around solving that problem.
The 100-Lab Journey
Instead of spending dozens of hours watching disconnected theory lectures, you will progress through100 hands-on engineering labs.
The progression is deliberate.
You begin by configuring your local AI coding environment.
Then you learn how agents execute tools.
Then you secure those tools.
Then you introduce state and memory.
Then multiple agents begin working together.
Then you evaluate their output.
Then you integrate them with GitHub and CI/CD.
Then you instrument them.
Then you govern them.
Finally, you deploy the entire architecture as a sovereign production system.
This is not a collection of random AI-agent tutorials.
It is anengineering progression from local experimentation to enterprise autonomy.
What's Inside?
Module 1 - Build Your First Reliable Coding Agent
Start with terminal AI harnesses, local model routing, Git-based agent rules, custom context, code generation, approval gates, debugging, testing, diff review, and automated repair.
You will establish the foundational discipline
AI generates. Engineers verify. Systems enforce.
Module 2 - Secure the Agent's Hands
An intelligent agent is only as safe as the tools it can access.
You will build secure tool schemas, read-only file access, container sandboxes, network restrictions, credential scopes, database tools, validation layers, rate limits, cost caps, and prompt-injection defenses.
The goal is not merely to make an agent capable.
It is to make itconstrained by design.
Module 3 - Give Agents Memory and State
Real engineering tasks rarely finish in one conversation.
You will build stateful workflows with LangGraph, implement Plan-Act-Observe-Refine loops, manage token windows, introduce vector-based code retrieval, persist state, execute long-running jobs, checkpoint workflows, and recover interrupted agents.
Your agents evolve from simple scripts intostateful systems.
Module 4 - Build a Multi-Agent Engineering Team
One agent does not have to do everything.
You will architect specialized agents for planning, coding, reviewing, testing, debugging, routing, and task delegation.
You will also confront the problems that appear when autonomous systems collaborate
Conflicting outputs Shared context Communication Deadlocks Dynamic routing Result aggregationThe result is a collaborativeAI software-development team.
Module 5 - Stop Trusting AI Output Without Measuring It
A production agent cannot be evaluated with
"The code looks good."
You will build evaluation datasets, automated test-generation frameworks, execution-based correctness measurements, LLM-as-a-Judge systems, hallucination detection, regression tracking, latency measurements, token-cost benchmarks, linting, formatting enforcement, and SAST integration.
You will learn to answer the question that matters
Does the agent actually work?
Module 6 - Turn AI Into a Legacy-Migration Engine
Modern engineering is not only about creating new applications.
Companies have enormous existing codebases.
You will use AST analysis and agentic workflows to perform multi-file refactoring, framework upgrades, language modernization, merge-conflict resolution, database migrations, microservice decomposition, dependency cleanup, and cross-repository API changes.
This module transforms the agent from a code generator into asoftware modernization engine.
Module 7 - Connect Agents to the Software Delivery Lifecycle
Now the agent leaves the development machine.
You will connect autonomous workflows to GitHub pull requests, issue triage, specifications, GitHub Actions, automated code reviews, test gates, branch protection, token monitoring, merge automation, and self-healing CI.
By the end of this stage, your agent can participate directly in thesoftware delivery pipeline.
Module 8 - Make Autonomous Systems Observable
Autonomy without observability is a production nightmare.
You will implement OpenTelemetry tracing, detailed tool-call logging, LangSmith visualization, cost monitoring, infinite-loop detection, real-time alerts, token-efficiency analysis, custom dashboards, structured audit logs, and RAG retrieval tracing.
Every important decision becomes measurable.
Every tool call becomes traceable.
Every major failure becomes diagnosable.
Module 9 - Add Enterprise Governance
Production AI needs boundaries.
You will implement privacy filters, PII masking, RBAC, human approval gates, emergency kill switches, copyright and license checks, corporate coding policies, financial spend limits, circuit breakers, autonomous risk scoring, and compliance reporting.
This is where an experimental agent becomes anenterprise-governed system.
The Climax: Lab 100 - The PhD-Level Capstone
Everything eventually converges inLab 100.
You will build an enterprise-grade autonomous coding platform capable of ingesting a Jira-style software ticket, reasoning about a multi-repository microservice architecture, retrieving relevant code, planning changes, implementing modifications across files, generating tests, executing those tests, passing security and governance controls, and producing a verified pull request.
The architecture brings together
LangGraph + LangChain + Python + Pydantic + Docker + Kubernetes + PostgreSQL + Redis + Ollama/vLLM + OpenTelemetry + Prometheus + LangSmith
But the real achievement is not the technology list.
It is the system architecture.
Your final platform incorporates
Stateful multi-agent orchestration Secure containerized execution Least-privilege tool access Network restrictions Hybrid code memory Persistent checkpoints Automated evaluations Security validation Human approval gates PII protection RBAC Cost controls Full telemetry Audit trails Failure recovery Load and stress testing Security testing Sovereign deployment architectureThis is the point where the course moves beyond "AI coding."
You are engineering anautonomous software-delivery platform.
And importantly, you will understandwhy every layer exists-not simply copy a framework tutorial.
Production-Grade Engineering, Not AI Theater
There are countless courses showing how to call an LLM API.
This course goes much further.
You will repeatedly confront the difficult engineering questions
What happens when the model is wrong?
What happens when a tool returns malformed data?
What happens when an agent enters an infinite loop?
What happens when credentials are exposed?
What happens when token costs explode?
What happens when two agents disagree?
What happens when generated code passes one test but breaks another?
What happens when the network is unavailable?
What happens when the agent needs to operate without sending sensitive source code to an external provider?
These are not theoretical questions.
They are the problems that determine whether an autonomous AI system belongs in a production environment.
Your Transformation
You start with
"How do I get AI to write code?"
You finish with
"How do I engineer, evaluate, secure, observe, govern, and deploy an autonomous coding platform?"
That is the transformation this course is designed to deliver.
If you are serious about becoming an AI engineer who understands what happensafter the demo works, this 100-lab journey is built for you.
The future of software engineering will not simply belong to people who can prompt models.
It will belong to engineers who canarchitect reliable systems around them.
Enroll now and start building.
Your first agent can be running locally in the opening labs.
Your production architecture begins in Lab 1.
Your sovereign autonomous coding system awaits in Lab 100.
Who this course is for
1. The Aspiring AI Engineer You understand Python and software development, but you want to move beyond basic chatbot APIs and prompt engineering. 2. The Production Developer / AI-Augmented Engineer You already build applications professionally and are watching AI coding tools transform software engineering. 3. The Senior Engineer Seeking Sovereignty You are responsible for architecture, infrastructure, security, automation, or engineering strategy-and vendor lock-in is a concern.Homepage
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