Mastering Context Design for Intelligent AI Agents

Learn how to design smarter, effective AI agents using the 6 essential context types: Instructions, Memory, Tools & more

Created byVivian Aranha, School of AI

Last updated 2/2026

English

English [Auto],

What you'll 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

10 sections • 48 lectures • 2h 38m total length

Certificate of Completion0:29
Introduction to Mastering Context Design for Intelligent AI Agents4:17
Explore how context design fuels intelligent AI agents, covering six context types, role setting, few-shot and zero-shot techniques, memory architectures, tool integration, and end-to-end workflows.

Role Definition – Who is the Agent?3:48
Objectives – What Are We Trying to Achieve?3:01
Requirements – How Should the Agent Operate?3:13
Learn to define operational requirements that govern an intelligent agent’s behavior, including constraints, formatting, tone, length, and do's and don'ts, to ensure clear, usable outputs.
Strategic Context vs Task-Only Instructions3:02
Prompt Engineering for Instructional Context3:02
Design instructional prompts by defining role, objective, requirements, and constraints, balancing clarity, structure, and length to achieve goal-aligned results from AI agents.
Clawdbot and the Shift to Agent-First Computing8:15
For years, artificial intelligence has been positioned as a conversational tool—answering questions, generating content, and offering recommendations. While impressive, most AI systems today remain fundamentally passive. They talk, but they don’t act.
This presentation explores a critical shift now underway: the transition from chat-based AI to agentic AI systems—AI that can reason, make decisions, and execute real-world actions. Using Clawdbot (also known as Moltbot) as a concrete example, this session introduces a new operating model for AI: one where agents serve as persistent, proactive operators rather than temporary conversational assistants.
Clawdbot represents a new class of AI systems. It is an open-source, self-hosted AI agent designed to live alongside users and teams, integrate with existing tools, maintain long-term context, and perform actions autonomously when instructed. Instead of navigating dozens of applications, dashboards, and workflows, users interact with a single intelligent agent through familiar messaging interfaces such as WhatsApp, Slack, or Telegram. The agent interprets intent, reasons using large language models, executes tasks across systems, and reports results back in natural language.
This presentation breaks down how this agentic model works in practice. Attendees will see how human intent flows into AI reasoning and ultimately results in real execution—such as managing emails, scheduling tasks, running scripts, coordinating workflows, or integrating with enterprise systems. The architecture behind these systems will be explained at a high level, highlighting how reasoning, memory, and execution are intentionally separated to enable flexibility, control, and governance.
Beyond the technology, the session focuses on why this shift matters. Agentic AI challenges the app-centric model of computing that has dominated for decades. Instead of humans adapting to software interfaces, software adapts to human goals through intelligent agents. This has profound implications for productivity, privacy, system design, and organizational workflows. AI moves from being a feature embedded in products to becoming a foundational layer of infrastructure.
The presentation also addresses the risks and responsibilities that come with powerful AI agents. Topics such as security, permissioning, governance, and human-in-the-loop control are discussed to ensure that autonomy is introduced safely and intentionally—especially in enterprise environments.
By the end of this session, attendees will leave with a clear understanding of what AI agents are, how systems like Clawdbot work, and why this paradigm represents one of the most important evolutions in AI adoption. More importantly, they will gain a new mental model for the future of computing—one where AI doesn’t just assist, but actively operates.

Why Examples Improve Accuracy and Alignment3:17
Positive and Negative Behavior Examples3:01
Design balanced positive and negative examples to train intelligent AI agents, establishing behavioral boundaries, improving reliability, and aligning tone, policy enforcement, and content filtering for biased or misleading content.
Response Examples for Output Calibration3:13
Learn to use response examples as calibration templates to align tone, structure, and depth, ensuring output consistency for conversational agents in production settings.
Designing Few-Shot and Zero-Shot Prompts2:52
Case Study: Prompt Recipes with Examples3:04

External Context: Industry, Strategy, and Models3:11
Equip AI agents with external context from industry vocabulary, company strategy, and operating models at runtime to ground responses like a domain expert.
Task Context: Step-by-Step Workflows3:08
Feeding agents with task context through step-by-step workflows keeps them organized and reliable across multi-stage tasks, using structured sequences, conditional logic, memory calls, and tool triggers.
Documents, Specs, Process Guides as Context3:11
Equip AI agents with static and semi-dynamic reference documents—such as product specs, company guidelines, and internal playbooks—to ground reasoning with document-based context and company knowledge.
Structured Data and Metadata Injection2:58
Context-Aware Agent Reasoning Demo3:08

Short-Term Memory: State, Progress, and History3:03
Explore short term memory in AI agents as the immediate past within a session, including recent exchanges, completed steps, and user preferences, bounded by the context window and memory compression.
Long-Term Memory: Semantic, Episodic, Procedural3:05
Architecting Memory: Tools, APIs, and Databases3:00
Common Patterns: Thin Memory, Thick Memory, Tool-Based Memory3:04
Explore three memory patterns for agents: thin memory, thick memory, and tool-based memory, and how ECC and hybrid memory support scalable, long-term or real-time systems.
Case Study: OpenAI, LangChain, and Scratchpad Memory Comparison3:04
Explore three real world agent memory approaches—OpenAI assistance API, long chain memory modules, and scratchpad memory—highlighting simplicity, control, persistence, and transparency to guide memory design.

What Are Tools in LLM Systems?3:27
Tool Descriptions: Function, Return, Usage2:57
Tool Parameters: Type, Defaults, and Requirements3:11
Calling Tools via Function Calling APIs3:47
Learn how function calling APIs let an LLM trigger real tools via structured JSON, enabling weather lookups and other tasks across platforms like OpenAI, Anthropic, and LangChain.
Demo: Multi-Tool Agent Setup with LangGraph or CrewAI3:21

Tool Invocation Layers and Orchestration4:13
Interpreting Results from Tools3:25
interpret tool results by formatting outputs clearly and structuring data to guide the model's reasoning, ensuring confidence scores, structured fields, and concise summaries drive accurate ai actions.
Chaining Results Across Agentic Steps3:27
Prompting with Prior Tool Results3:35
Example: Reasoning Agents with Tool Memory Replay3:21

Designing Context Strategies per Use Case3:11
Balancing Context Length with Cost3:13
Balance token cost and model performance by trimming prompts, summarizing tool outputs, archiving memory, and using retrieval and embeddings to keep relevance.
Modularizing and Reusing Context Blocks3:14
Debugging Contextual Failures3:24
Lab: Build a Multi-Context Agent for Document QA3:31
Build a modular multi-context document QA agent that ingests PDFs and text, uses tools like PDF parsers and chunkers, and delivers structured, memory-aware answers.

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

Description

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

Mastering Context Design for Intelligent AI Agents

What you'll learn

Explore related topics

Course content

Introduction to Mastering Context Design for Intelligent AI Agents2 lectures • 5min

Module 1: Introduction to Contextual AI Agents5 lectures • 16min

Module 2: Instructional Context – Setting the Stage6 lectures • 24min

Module 3: Example-Based Context – Showing, Not Just Telling5 lectures • 15min

Module 4: Knowledge Context – Domain, Tasks, and Workflows5 lectures • 16min

Module 5: Memory – Short-Term and Long-Term Recall5 lectures • 15min

Module 6: Tool Context – Parameters and Descriptions5 lectures • 17min

Module 7: Tool Results – Using Outputs as Context5 lectures • 18min

Module 8: Building Context-Rich Agent Workflows5 lectures • 17min

Module 9: Advanced Topics in Contextual Agent Design5 lectures • 15min

Requirements

Description

Who this course is for: