What you’ll learn
- Build agentic AI systems that go beyond prompting by combining planning, execution, and evaluation workflows
- Design and implement multi-agent architectures using the Planner → Executor → Critic pattern
- Apply dual-mode reasoning and create structured outputs such as JSON plans and execution graphs
- Develop AI-powered solutions for code review, debugging, refactoring, and system design
- Create security-aware AI systems that detect vulnerabilities and generate risk reports with remediation steps
- Integrate memory systems (FAISS/Chroma patterns) to enable context retention and long-running workflows
- Implement guardrails, policy engines, and human-in-the-loop approval workflows for enterprise readiness
- Use LLM-as-a-judge evaluation techniques to measure quality, reliability, and performance of AI outputs
- Build systems with tool usage and API integration for real-world automation
- Design observability pipelines with logging, tracing, and cost monitoring for AI systems
- Design observability pipelines with logging, tracing, and cost monitoring for AI systems
- Deliver a complete production-grade frontier AI system as a portfolio-ready capstone project
