Course Description

QUICK FACTS
Concepts
● What is Agentic AI vs chatbot AI
● Where agents are used in software engineering
● What you’ll build in this course (multi-agent pipeline)
Lab
● Run and observe a sample agent workflow
● Identify “goal → plan → action → output” in a guided demo
Concepts
● Tokens, context, prompts (simple explanation)
● Why LLMs fail (hallucinations, ambiguity)
● How agents reduce errors with tools + steps
Lab
● Compare normal prompts vs structured prompts
● Create a “task checklist prompt” for reliability
Concepts
● Role prompts, constraints, output formats
● Task decomposition and chaining
● Consistency with templates
Lab
● Build an agent-style prompt template
● Validate with 3 different use cases
Concepts
● Agent components: planner, tool layer, memory, output
● Stateless vs stateful agents
● Observability: logs and trace thinking (beginner approach)
Lab
● Draw your agent architecture
● Simulate agent loop steps using a guided worksheet
Concepts
● Goal-based planning
● ReAct-style thinking (simplified)
● Guardrails: what agents must NOT do
Lab
● Build a basic planning agent
● Add “stop conditions” and “fallback response”
CAREER GROWTH
Climb the ladder of success with structured role progression.

The most effective project-based immersive learning experience The most effective project-based immersive learning experience The most effective project-based immersive learning experience

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