• Understand and apply the 6 types of context: Instructions, Examples, Knowledge, Memory, Tools, and Tool Results
• Design role-based prompts with clear objectives and behavioral requirements
• Craft few-shot and zero-shot prompts using positive and negative examples
• Inject structured domain knowledge, process workflows, and documents into agent prompts
• Architect short-term and long-term memory systems for multi-turn reasoning
• Use tool descriptions, parameters, and return values to integrate APIs and functions
• Handle tool outputs and chain results across multiple agentic steps
• Balance context length vs. token limits using summarization and prompt compression
• Implement agent orchestration frameworks like LangChain, CrewAI, and LangGraph
• Build modular, reusable, and scalable agent workflows for real-world use cases
• Debug and improve agents with self-reflection and context refresh strategies
• Complete a capstone project by building a full multi-context AI agent from scratch

Are you ready to build intelligent AI agents that go beyond simple prompts and one-off answers? In today’s fast-evolving AI landscape, it’s not just about Large Language Models (LLMs)—it’s about giving them the right context to think, reason, and act. This course will teach you how to master the art and science of context design so your agents can perform complex tasks, sustain multi-turn conversations, and integrate with real-world tools and memory systems.

In Mastering Context Design for Intelligent AI Agents, you’ll learn how to design agents that are context-aware, adaptive, and highly capable. You’ll discover how to work with six foundational context types: instructional context, example-based context, knowledge context, memory context, tool context, and tool result chaining. These aren’t just theory—they’re the building blocks behind real-world agent frameworks like LangChain, CrewAI, LangGraph, and OpenAI’s function calling systems.

We’ll show you how to move beyond static prompting into modular, orchestrated systems that automatically manage and update context over time. Whether you’re building a Document Q&A bot, a multi-agent workflow, or a self-reflective planner agent, this course will guide you step by step.

You’ll learn how to:

• Use prompt engineering effectively with role, goal, and requirement structures

• Implement few-shot prompting using positive and negative examples

• Leverage semantic search and vector databases for dynamic retrieval

• Architect short-term and long-term memory using modern tools

• Integrate tools through function calling, with clear parameter design and output handling

• Optimize token usage with prompt compression and memory pruning

• Create self-improving agents through reflection and autonomous context refresh

• Build multi-context pipelines using agent orchestration frameworks

This course is perfect for developers, AI engineers, technical product managers, and prompt engineers who want to move beyond beginner prompt patterns and develop real-world, production-grade AI agents.

By the end of the course, you’ll be able to:

– Design context-rich prompts for advanced use cases
– Build modular agent workflows with dynamic context injection
– Implement agents using LangChain, CrewAI, or OpenAI Assistants API
– Apply token-efficient strategies to keep costs low and performance high
– Debug, reflect, and improve agent behavior in autonomous systems

No prior deep learning experience is required—just a working knowledge of prompts, tools, and a curiosity for how autonomous agents really work under the hood.

If you’re aiming to lead the way in AI automation, agentic systems, or LLM-powered workflows, this course is your blueprint.

Who this course is for:

• A Prompt Engineer who wants to move beyond templates into modular, agentic design
• A Software Developer or AI Engineer building multi-step LLM-based applications
• A Technical Product Manager designing features powered by agents or assistants
• A Data Scientist experimenting with autonomous decision-making systems
• An AI Enthusiast curious about how tools like LangChain, OpenAI Assistants, and CrewAI really work
• A Researcher or Educator looking for deeper insight into contextual design principles for agents