2. What skills will I gain from context engineering training?

You can develop skills in context design, information selection, retrieval, memory management, dynamic context pipelines, agent state, and context optimization. The course also covers compression, summarization, security, evaluation, retrieval testing, and context monitoring.

3. How is context engineering different from prompt engineering?

Prompt engineering primarily focuses on designing instructions that guide an AI model's response. Context engineering takes a broader approach by managing the information, memory, tools, resources, instructions, and retrieved knowledge available to an AI agent during task execution.

4. Does the course cover agent memory and RAG?

Yes. The curriculum covers short-term and long-term agent memory, knowledge retrieval, semantic search, retrieval-augmented generation, retrieved context management, and RAG-powered agents. Learners also explore how memory and retrieval can be incorporated into dynamic agent workflows.

5. What projects are included in the context engineering for AI agents course?

Projects include building a context-aware agent, creating an agent memory system, developing a RAG agent, and designing a multi-step context pipeline. Learners also optimize context performance and explore deploying a production context system.

Context Engineering for AI Agents Course

Category Data Analytics & AI

Level Experts

Students 13 Already Enrolled

Review

(4.7)

Context engineering for AI agents focuses on designing, organizing, retrieving, and managing the information agents need to perform tasks effectively. This course explores how context differs from prompts and how carefully structured information can improve agent responses, workflows, memory, retrieval, and task execution.

Learners explore context windows, information selection, instructions, constraints, tools, resources, retrieval systems, semantic search, RAG, short-term and long-term memory, dynamic context pipelines, agent state, and multi-step workflows. Advanced topics include context compression, summarization, hierarchical organization, information prioritization, and long-conversation management.

Security and evaluation are integrated throughout the course, covering sensitive context, context leakage, prompt injection, retrieval testing, context quality, and monitoring. Through structured projects, learners build context-aware agents, memory systems, RAG agents, and dynamic context pipelines while optimizing performance for production environments.

Context engineering principles, context quality, relevance, and agent requirements
Context windows, information selection, instructions, tools, and resources
Context retrieval, knowledge sources, semantic search, and RAG
Short-term and long-term agent memory management
Dynamic context pipelines, agent state, workflows, compression, and summarization
Context security, evaluation, monitoring, optimization, and production systems

Basic familiarity with artificial intelligence, large language models, AI agents, or software development is recommended. Experience with RAG, prompt engineering, APIs, or agent workflows can be helpful but is not required.

This course is designed for AI engineers, developers, software professionals, ML practitioners, AI architects, and technical teams building agentic applications. It is also suitable for professionals seeking to improve agent memory, retrieval, context management, and multi-step AI workflows.

1.1 Understanding Context Engineering
1.2 Context Engineering Versus Prompt Engineering
1.3 Why Context Matters for AI Agents
1.4 Types of Agent Context
1.5 Context Quality and Relevance
1.6 Context Engineering Challenges

2.1 Understanding Agent Context Windows
2.2 Structuring Context for AI Agents
2.3 Selecting Relevant Information
2.4 Managing Instructions and Constraints
2.5 Organizing Tools and Resources
2.6 Reducing Context Noise

3.1 Designing Context Retrieval Systems
3.2 Connecting Knowledge Sources
3.3 Retrieval-Augmented Generation
3.4 Semantic Search for Agents
3.5 Managing Retrieved Context
3.6 Improving Retrieval Relevance

4.1 Understanding AI Agent Memory
4.2 Managing Short-Term Memory
4.3 Designing Long-Term Memory
4.4 Storing User and Task Context
4.5 Updating Agent Memory
4.6 Controlling Memory Relevance

5.1 Building Dynamic Context Pipelines
5.2 Managing Multi-Step Tasks
5.3 Updating Context During Execution
5.4 Passing Context Between Tools
5.5 Managing Agent State
5.6 Designing Context-Aware Workflows

6.1 Context Compression Strategies
6.2 Context Summarization Techniques
6.3 Hierarchical Context Management
6.4 Prioritizing Relevant Information
6.5 Managing Long Conversations
6.6 Optimizing Context Window Usage

7.1 Protecting Sensitive Context
7.2 Preventing Context Leakage
7.3 Defending Against Prompt Injection
7.4 Evaluating Context Quality
7.5 Testing Context Retrieval
7.6 Monitoring Agent Context

8.1 Building a Context-Aware Agent
8.2 Creating an Agent Memory System
8.3 Developing a RAG Agent
8.4 Designing a Multi-Step Context Pipeline
8.5 Optimizing Agent Context Performance
8.6 Deploying a Production Context System

A context engineering for AI agents course is suitable for AI developers, engineers, software professionals, ML practitioners, AI architects, and technical teams building intelligent agents. It can also benefit learners with experience in LLMs, RAG, or AI application development.

Context Engineering for AI Agents Course

About Context Engineering for AI Agents Course

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