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AI-Agent-Mastery

(★ 25)

Hands On AI Agent Mastery: 30-Day Intensive Production Engineering Course

파일 탐색기

ZIP 다운로드

- - .env.example
  - .gitignore
  - docker-compose.yml
  - README.md
  - start.sh
  - stop.sh
  - verify_setup.sh
- - .gitignore
  - Day 2: Step-by-Step Implementation Guide.pdf
  - demo_test.py
  - README.md
- - .gitignore
  - docker-compose.yml
  - Dockerfile
  - start.sh
  - stop.sh
- - .gitignore
  - build.sh
  - demo.sh
  - docker-compose.yml
  - README.md
  - start.sh
  - stop.sh
  - test.sh
- - build.sh
  - docker-compose.yml
  - start.sh
  - stop.sh
README.md

# CDN으로 사용하기

jsDelivr

jsDelivr는 공개 GitHub 리포지토리를 별도 설정 없이 CDN으로 즉시 서빙합니다. 버전과 파일을 고르면 웹페이지에 바로 붙일 수 있는 링크와 예시 코드가 만들어집니다.

버전

파일

링크

예시

명령어 용어집

이 문서에서 사용된 명령어를 모아봤습니다. 낯선 명령어가 있다면 펼쳐서 확인해보세요.

🔍

`pipenv`

설명 보기 ▼

간단하고 통합된 Python 개발 워크플로우.
프로젝트의 패키지 및 가상 환경 관리.
더 많은 정보: <https://pypi.org/project/pipenv>.

pipenv

새 프로젝트 생성:

pipenv --three

Python 3를 사용하여 새 프로젝트 생성:

pipenv install {{package}}

패키지 설치:

🔍

`poetry`

설명 보기 ▼

Python 패키지 및 의존성을 관리.
관련 항목: `asdf`, `pipenv`, `hatch`.
더 많은 정보: <https://python-poetry.org/docs/cli/>.

poetry new {{project_name}}

특정 이름으로 디렉토리에 새 Poetry 프로젝트 생성:

poetry add {{dependency}}

현재 디렉토리의 `pyproject.toml` 파일에 의존성과 그 하위 의존성 설치 및 추가:

poetry install

현재 디렉토리의 `pyproject.toml` 파일을 사용하여 프로젝트 의존성 설치:

// repository documentation

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>### Special Note:
>All lesson are accessible at repo - https://github.com/sysdr/AI-Agent-Mastery-p
>
>Upgrade to Paid subscription to access all lessons https://aiamastery.substack.com/subscribe

------------------
# Hands On AI Agent Mastery

> ### What you will get beyond this?
> We would keep adding **new** **Courses** with upcoming latest Features/Technology for annual/lifetime subscribers.
> Current 30 Lessons as per the curriculum.

---

Course Link : https://aiamastery.substack.com/p/curriculum

## Why This Course?

AI agents are reshaping enterprise software—from automating complex workflows to orchestrating multi-million dollar trading systems. Yet most engineers learn AI through theoretical tutorials that don't translate to production environments where reliability, security, and cost optimization are paramount. This course bridges that gap by building enterprise-grade agents from day one.

Traditional AI education focuses on models and algorithms. We focus on engineering resilient, scalable agent systems that pass enterprise security audits and handle production loads. You'll master not just *how* to build agents, but *why* specific architectural decisions matter when systems need 99.9% uptime and SOC 2 compliance.

**Subscribe**

---

## What You'll Build

### Week 1: Foundation & Security
- CLI automation agent with encrypted state persistence and audit logging
- Rate-limited web scraping agent with proxy rotation and circuit breakers
- Document processing agent with PII detection and secure chunking

### Week 2: Production Integration
- Enterprise chat agent with SSO integration and conversation encryption
- Code review agent with security scanning and compliance checking
- Multi-modal agent with content moderation and data classification

### Week 3: LLMOps & Orchestration
- Multi-agent collaboration system with cost optimization and monitoring
- Self-healing orchestrator with automatic failover and health checks
- Production monitoring agent with anomaly detection and alerting

### Week 4: Enterprise Deployment
- Scalable agent API with OAuth2, rate limiting, and comprehensive logging
- Kubernetes deployment with auto-scaling and disaster recovery
- Enterprise-grade agent platform with compliance reporting and audit trails

---

## Who Should Take This Course?

### Primary Audience:
- Software engineers integrating AI into production systems requiring enterprise standards
- Senior developers leading AI implementation initiatives with security and compliance requirements
- DevOps/Platform engineers building AI-powered automation for enterprise environments
- Solutions architects designing agent systems for enterprise clients

### Secondary Audience:
- Engineering managers evaluating AI agent platforms for enterprise adoption
- Security engineers implementing AI governance and risk management frameworks
- System reliability engineers building monitoring and observability for AI systems
- Product managers launching AI-powered features in regulated industries

---

## What Makes This Course Different?

- **Production-First Engineering:** Every lesson prioritizes enterprise requirements: security, monitoring, compliance, and cost optimization over proof-of-concept implementations.
- **LLMOps Mastery:** Comprehensive coverage of Large Language Model Operations—the production discipline that distinguishes professional AI engineers from hobbyist practitioners.
- **Industry Validation:** Partnership with OpenAI, Anthropic, and leading vector database providers for certification pathways that enhance professional credibility.
- **Portfolio Development:** Build 5 progressively complex agents forming a professional portfolio demonstrating enterprise-ready capabilities to potential employers.

---

## Key Topics Covered

### Enterprise Agent Architecture
- Agent lifecycle management with state encryption and audit logging
- Memory systems optimized for cost, performance, and compliance
- Tool integration with security sandboxing and error propagation
- Multi-agent orchestration with distributed consensus and monitoring

### LLMOps & Production Engineering
- Cost optimization strategies and budget management systems
- Performance monitoring, observability, and debugging workflows
- Security frameworks for prompt injection prevention and data protection
- Deployment pipelines with automated testing and rollback capabilities

### Enterprise Integration Patterns
- SSO/OAuth2 integration and enterprise authentication flows
- Database integration with encryption at rest and secure access patterns
- Event-driven architectures with message queuing and dead letter handling
- API design for enterprise clients with comprehensive documentation

### Advanced Production Patterns
- Multi-tenant agent architectures with resource isolation
- Compliance automation and audit trail generation
- Disaster recovery planning and business continuity systems
- Advanced monitoring with custom metrics and alerting frameworks

---

## Prerequisites

### Required:
- Intermediate Python programming (`async/await`, `decorators`, `context managers`)
- REST API development and authentication pattern experience
- Git workflow proficiency including branching and code review
- Basic Docker containerization and command-line operations

### Strongly Recommended:
- Cloud platform experience (AWS/Azure/GCP) with IAM and security groups
- Database design experience with both SQL and vector databases
- CI/CD pipeline experience with automated testing
- Production system monitoring and logging experience

### Environment Setup:
- `Python 3.11+` with `poetry` or `pipenv` for dependency management
- Docker Desktop with Kubernetes enabled
- VSCode or PyCharm with Python debugging and testing extensions
- API access to OpenAI, Anthropic, and Pinecone (free tier guidance provided)
- AWS/Azure/GCP account for production deployment exercises

---

## Course Structure

### Daily Format (2.5-3 hours per day):
- **Architecture Review (20 minutes):** System design principles with enterprise context
- **Hands-On Implementation (120-150 minutes):** Building production-ready components
- **Peer Code Review (20 minutes):** Professional development practices and collaboration
- **Monitoring & Testing (15 minutes):** Validation, performance measurement, and debugging
- **Portfolio Documentation (15 minutes):** Professional presentation and architecture decisions

### Weekly Deliverables:
- Production-deployed agent with monitoring dashboard and documentation
- Architecture decision records (ADRs) explaining design choices and trade-offs
- Performance benchmarks and cost analysis reports
- Security assessment and compliance checklist completion

### Professional Development:
- Daily standup sessions simulating professional development environments
- Code review partnerships following industry best practices
- Technical presentation practice for stakeholder communication
- Portfolio development with GitHub showcase and LinkedIn optimization