Agentic AI Engineering Program

The Agentic AI Engineering Program is a 6-month course from ALA Academy, followed by a 6-month embedded internship with ALA Core on real Agentic AI projects. You will learn to design, build, evaluate, secure, and deploy AI agents and multi-agent workflows using Python, LLM APIs, the OpenAI Agents SDK, LangGraph, MCP, and A2A.

Course Structure

- Duration: 6 months / 24 weeks

- Sessions: 72 instructor-led sessions (3 per week, 2 hours each)

- Total hours: 144 instructional hours

- Months 1-5: 60 Agentic AI engineering sessions

- Month 6: 12 career-readiness sessions (CV, LinkedIn, GitHub portfolio, interviews, graduation presentation)

- Internship: 26 weeks with ALA Core

- Entry level: foundation-to-junior, with basic programming literacy helpful

What You Will Learn

- When to use deterministic automation, LLM workflows, single agents, or multi-agent systems

- Python engineering, async programming, and REST APIs

- LLM foundations, prompting, context engineering, and structured outputs

- Tool calling and building the agent loop

- Retrieval (RAG), agentic RAG, and grounded answers with citations

- State, short-term and long-term memory, and privacy boundaries

- OpenAI Agents SDK: tools, sessions, guardrails, human approval, and tracing

- Multi-agent orchestration, delegation, and durable workflows with LangGraph

- Model Context Protocol (MCP): building servers and secure clients

- Agent2Agent (A2A) interoperability

- Evaluation-driven development: task and trajectory evaluations, regression tests, and release gates

- Security: threat modeling, prompt injection, least privilege, red teaming, and OWASP LLM risks

- Observability, reliability, FastAPI services, Docker, and production operations

- Responsible AI, data governance, and cost and latency optimization

Projects and Assessment

- Project 1: Production-quality single agent

- Project 2: Orchestrated multi-agent workflow with MCP

- Project 3: Secure, observable, and containerized agent service

- An integrated capstone project (25% of the grade)

- Career package and graduation presentation before an ALA Core panel

Career Month

Build a targeted Agentic AI CV, a LinkedIn profile, and a GitHub portfolio with architecture notes, sample traces, and evaluations. Complete a technical mock interview and present your graduation project.

Real-World Internship

Work alongside ALA Core AI, backend, data, QA, and product teams on real internal or client Agentic AI projects. You get least-privilege access under an assigned mentor and do not deploy consequential production actions independently.

Who Is This For?

Developers and aspiring AI engineers who want to build reliable, secure, production-ready AI agents.