Agentic AI, In-Depth

In this 3-part intensive, you’ll learn to design, implement, and evaluate an end-to-end agent system with planning, tool orchestration, long-horizon memory, retries, and guardrails. Each session is live and hands-on, and you’ll complete a guided project with feedback and templates.

You’ll walk away with:

• A working repo + README
• Evaluation checklist + observability toolkit
• Clear deployment options
• Perfect for intermediate devs who’ve touched Python and LLMs—and want to ship something real.

Who It’s For

• Intermediate Python users (used OpenAI, Groq, etc.)
• Builders ready to level up to long-running agent flows
• Teams validating an internal agent use-case

Prerequisites

• Python basics, Git, VS Code or Colab
• Any LLM key (OpenAI/Groq/etc.)
• Able to read simple logs (JSON, HTTP responses)

Outcomes

• Plan-and-act agent with tools, memory, and eval hooks
• Robust routing, retries, error handling, and logging
• Evaluation + guardrails you can reuse across agents
• Final working project scoped to your use-case

Schedule

• 3 live sessions (60–90 min each) across 3 separate days
• You’ll choose dates via calendar during booking
• Async support window (project duration)
• Includes 2 DM check-ins (mid & final build)