When does class start/end?
Class hours may vary, please reach out to contact@ascendientlearning.com if you have any questions.
Applied Context Engineering for Agentic AI
Apply context-engineering techniques that make agentic AI reliable at scale. Context-window constraints cover the role of attention in transformers, quadratic scaling, the prefill and decoding steps,...
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$2,000USD
Duration
2 days
Course Code
GAI-2107
Available Formats
Classroom
Overview
Course Description
Apply context-engineering techniques that make agentic AI reliable at scale. Context-window constraints cover the role of attention in transformers, quadratic scaling, the prefill and decoding steps, KV-cache and VRAM optimization, and the failure modes that emerge at context limits. The move from prompt to context covers agentic loops, the context-snowball effect, the anatomy of an LLM context window, and the key context-engineering strategies. Context reduction, offloading, retrieval, isolation, and caching cover reversible and irreversible reduction, filtering, pruning, summarization, layered action spaces, tool-confusion mitigation, classic versus agentic RAG, two-step retrieval, hybrid retrieval, multi-agent functional separation, and cache-friendly architectures with cache-bursting anti-patterns. Hands-on labs produce a context-reduced agent pipeline, an offloaded action-space implementation, an agentic RAG harness, and a multi-agent isolation pattern with cache-friendly architecture. The course is designed for software developers, data scientists, and AI engineers with Python and LLM concept familiarity.
Skills Gained
By the end of this course, participants will be able to:
- Explain LLM context-window constraints plus optimizations
- Apply context-engineering strategies (reduction, offloading, retrieval, isolation, caching) to real workflows
- Configure agents for performance and reliability under context constraints
- Implement practical context management in agentic AI workflows
- Analyze context-related failure modes plus mitigations
- Build multi-agent systems with deliberate context separation and sharing
Who Can Benefit
This course is designed for:
- Software Developers
- Data Scientists
- AI Engineers
Prerequisites
Participants should enter this course with:
- Practical Python experience
- Familiarity with LLMs and agentic AI concepts
Organizational Objectives
This course assists organizations to:
- Reduce agent run cost by right-sizing context through deliberate engineering
- Improve agent reliability by removing context-related failure modes
- Build a shared context-engineering vocabulary across agentic-AI teams
- Establish patterns for cache-friendly agent architectures that compound across projects
Software
All attendees must have a modern web browser and an Internet connection.
Course Details
Course Details
Module 1 - LLMs and Context Windows
By the end of this module, you will be able to explain attention and quadratic scaling in transformers, distinguish prefill and decoding steps, optimize KV-cache and VRAM usage, and identify failure modes at context limits.
- Explain the role of attention in transformers
- Describe quadratic scaling and context window constraints
- Differentiate pre-fill and decoding steps in LLMs
- Optimize with KV-cache and manage VRAM limits
- Identify failure modes at context limits
- Summarize SOTA techniques for long contexts
- Hands-on Lab: Analyze context windows in LLMs
Module 2 - From Prompt to Context
By the end of this module, you will be able to differentiate context engineering from prompt engineering, recognize agentic loops and the context-snowball effect, deconstruct an LLM context window’s anatomy, and apply key context-engineering strategies.
- Compare context engineering and prompt engineering
- Observe agentic loops and the context snowball effect
- Deconstruct the anatomy of an LLM context window
- Apply key context engineering strategies
- Hands-on Lab: Build and analyze agentic context
Module 3 - Context Reduction
By the end of this module, you will be able to apply filtering, pruning, and summarization to reduce agent context, distinguish reversible from irreversible reduction, implement task-aware reduction, and trigger reduction at context-size thresholds.
- Define the purpose and techniques of context reduction
- Distinguish reversible and irreversible reduction
- Apply filtering, pruning, and summarization strategies
- Implement intelligent, task-aware reduction
- Use context-size triggers for reduction
- Hands-on Lab: Implement and compare context reduction techniques
Module 4 - Context Offloading
By the end of this module, you will be able to design layered action spaces that resist tool confusion, choose offloading targets for tool outputs and state, and use file system and runtime stores as offload destinations.
- Define the purpose and strategies for offloading
- Design layered action spaces and address tool confusion
- Select offloading targets: tool outputs, state, definitions
- Use offloading stores: file system, runtime state
- Hands-on Lab: Design and test context offloading
Module 5 - Context Retrieval
By the end of this module, you will be able to compare classic and agentic RAG patterns, build two-step retrieval pipelines, select retrieval targets across knowledge bases, web, and history, and design hybrid retrieval strategies.
- Compare classic and agentic RAG techniques
- Build two-step retrieval pipelines
- Select retrieval targets: knowledge base, web, history, toolset
- Design hybrid retrieval strategies
- Hands-on Lab: Build and evaluate retrieval pipelines
Module 6 - Context Isolation
By the end of this module, you will be able to apply context isolation patterns, address multi-agent system challenges, design functional separation of agent roles, and follow best practices for isolation.
- Define the purpose and patterns of context isolation
- Address multi-agent system challenges
- Design functional separation and agent roles
- Apply best practices for isolation
- Hands-on Lab: Implement and compare context isolation strategies
Module 7 - Context Caching
By the end of this module, you will be able to apply context caching to reduce cost and latency, recognize cache-bursting anti-patterns, design cache-friendly architectures, and evaluate context-engineering tradeoffs around caching.
- Explain the purpose and benefits of context caching
- Identify cache bursting anti-patterns
- Design cache-friendly architectures
- Evaluate trade-offs in context engineering for caching
- Hands-on Lab: Optimize context caching
Schedule
FAQ
Does the course schedule include a Lunchbreak?
Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.
What languages are used to deliver training?
Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.
What does GTR stand for?
GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.
How do I find an Ascendient Learning training location?
We have training locations across the United States and Canada - View a complete list of classroom training locations.
Which delivery formats are available?
At Ascendient Learning, we offer training that is Instructor-Led, Online, Virtual, and Self-Paced.
Does Ascendient Learning deliver group training?
Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.
What does vendor-authorized training mean?
As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.
Is the training too basic, or will you go deep into technology?
It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.
How up-to-date are your courses and support materials?
We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.
Are your instructors seasoned trainers who have deep knowledge of the training topic?
Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.
Do you provide hands-on training and exercises in an actual lab environment?
Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.
Will you customize the training for our company’s specific needs and goals?
We will work with you to identify training needs and areas of growth. We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.
How do I get started with certification?
Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.
Will I get access to content after I complete a course?
You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.
How do I request a W9 for Ascendient Learning?
View our filing status and how to request a W9.
Reviews
The format of the class was concise. I learned new skills to use at my workplace.
Great company -- easy to sign up and very organized. Loved my teacher and class overall.
You get detailed labs to guide you through the technical material giving you a hands on method of learning otherwise difficult material.
Instructor was great, course was mostly very good except for too much focus on pricing
the interface was super easy to use and the instructions to get ready for the course was also very simple and easy to understand.
