Crafting Intelligent AI Agents: The Key to Context Design

In an era where AI is evolving at breakneck speed, simply using Large Language Models (LLMs) isn’t enough. You need to empower your AI agents with the right context to think and act intelligently. “Mastering Context Design for Intelligent AI Agents” dives deep into the essential strategies behind context design, enabling you to create adaptable agents that can handle complex tasks and engage in multi-turn conversations.

Throughout the course, you’ll explore six fundamental types of context that are crucial for effective AI design: instructional, example-based, knowledge, memory, tools, and tool result chaining. These concepts are not just theoretical; they form the backbone of real-world frameworks like LangChain and OpenAI’s function calling systems. You’ll transition from traditional prompting methods to dynamic, modular systems that systematically evolve context, making your agents capable of autonomous reasoning and integration with various tools.

Whether you’re interested in developing a Document Q&A bot, orchestrating multi-agent workflows, or designing a self-reflective planner, this course provides comprehensive, step-by-step guidance. By the end, you will master the techniques necessary to build agents that not only perform efficiently but also improve through self-reflection, ensuring they remain at the forefront of AI efficiency and capability.

What you will learn:

• 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

Course Content:

• Sections: 10
• Lectures: 46
• Duration: 2h 29m

Requirements:

• Basic understanding of how LLMs (like ChatGPT, Claude, or Gemini) work
• Familiarity with prompt engineering or prompt-based interactions
• Some exposure to tools like LangChain, OpenAI API, or CrewAI is helpful but not required
• General comfort reading or writing structured data formats like JSON
• A willingness to experiment and iterate with AI agent workflows

Who is it 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

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

👉 Don’t miss this coupon! – Cupón DEC_FREE_AI