AI Systems: From Prompts to Agents

Course Overview

AI Systems: From Prompts to Agents is a hands-on, three-day training that teaches how modern AI systems actually work—and how to design them properly.

Participants move beyond prompts and chatbots to understand AI as a system: with structure, memory, tools, and clear boundaries. The course starts from first principles and gradually builds toward autonomous agent-based systems, without assuming a technical background for the first two days.

By the end of the course, participants understand how to design reliable AI workflows, reason about agent behavior, and confidently move from ideas to working AI systems.

Delivery: Online or Classroom
Duration: 2 or 3 days (modular)
Audience: Students, professionals, career switchers, aspiring developers

Why This Course?

Most AI courses teach what to type.
This course teaches how AI systems are designed.

Participants will learn:

• Why AI behaves the way it does
• How to design before building
• How memory, tools, and agents actually work
• How to avoid fragile, prompt-only solutions

This course focuses on real systems, real constraints, and real-world thinking.

Course Structure

The course follows a 2 + 1 model:

• Days 1–2: Core foundations. No coding required.
• Day 3: Optional builder day. Python required.

Participants may attend only Days 1–2, or all three days.

Day-by-Day Outline

Day 1 – Understanding AI Systems

Focus: From “AI as magic” to “AI as systems”

Topics covered:

• What modern AI models actually do
• Tokens, context windows, and limitations
• Why hallucinations happen
• The difference between prompts and specifications
• Describing behavior using intent instead of code
• What skills are and how AI systems reuse them
• Single agents and sub-agents as system components

Outcome:
Participants understand how AI works and how to think in systems rather than prompts.

Day 2 – Memory, Tools, and Control

Focus: Making AI systems useful, safe, and repeatable

Topics covered:

• How AI systems use external knowledge
• Memory as a system component, not a model feature
• Designing workflows that retrieve and use information
• Tool usage and capability boundaries
• Local vs remote capabilities
• Hooks as control points before and after actions
• Designing guardrails and validation logic

Outcome:
Participants can design structured AI workflows that use memory and tools responsibly.

Day 3 – From Systems to Agents (Optional)

Focus: Building autonomous agent-based systems

Topics covered:

• What makes an agent different from a workflow
• Agent identity, goals, and planning
• Multi-step reasoning and decision loops
• Tool orchestration and state management
• Designing agents that can act, reflect, and adapt
• Reading and modifying real agent implementations

Outcome:
Participants can build and reason about autonomous agents using Python-based systems.

Delivery Mode

• Online (Live, Instructor-Led)
• Classroom (In-Person)

Both formats are fully hands-on and interactive.

Prerequisites

Days 1–2

• No programming experience required
• Comfort using a computer
• Willingness to think critically and experiment

Day 3

• Basic Python knowledge required
  • Variables
  • Lists
  • Functions

Participants do not need to be expert programmers.
They must be comfortable reading simple code.

Hardware & System Requirements

Participants must bring their own laptop.

Supported Operating Systems

• macOS: macOS 12 (Monterey) or newer
• Windows: Windows 10 (64-bit) or newer

These versions support modern Python tooling and local AI development workflows.

Hardware Minimums

• Memory: 8 GB RAM (16 GB recommended)
• CPU:
  • Minimum: 4 logical cores
  • Recommended: 6–8 logical cores

Permissions

Administrator permissions are required for:

• Installing Python
• Installing local development tools
• Running local servers

No GPUs are required.

Pricing

• Days 1–2: USD $200
• Day 3 (Optional): USD $100

Participants may enroll in:

• Days 1–2 only, or
• The full 3-day program

Who This Course Is For

• Students exploring AI beyond surface-level tools
• Professionals transitioning into AI-related roles
• Developers who want to understand agent systems properly
• Anyone who wants to build AI systems, not just use them