Agents Bootcamp: Context Engineering - Week 4 Welcome to Week 4! You’ve mastered agent fundamentals, built custom agents, and specialized in domain-specific applications. Now you’ll dive deep into context engineering - the critical discipline of designing systems that provide agents with the right information, tools, and context to accomplish complex tasks effectively. Context engineering is what separates basic chatbots from truly intelligent agents that can reason about complex problems and take meaningful actions.

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What’s context engineering?

Context engineering is the process of building dynamic systems to provide the right information and tools in the right format such that language models can plausibly accomplish complex tasks. Context engineering is the bridge between raw data and actionable intelligence. Context engineering encompasses:
  • Prompt Engineering: Crafting instructions that guide agent behavior and reasoning
  • Retrieval & RAG: Connecting agents to relevant, real-time information sources
  • Tool Use: Enabling agents to interact with external systems and APIs
  • Memory & State: Managing conversation history and maintaining context across interactions
  • Structured Outputs: Ensuring agents produce reliable, formatted responses
  • Information Architecture: Organizing knowledge for optimal agent access and reasoning

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Why context engineering matters

The difference between a helpful agent and a transformative one often comes down to context engineering: Without proper context engineering:
  • Agents hallucinate or provide outdated information
  • Responses are generic and lack domain-specific insight
  • Tool usage is inconsistent and unreliable
  • Complex tasks fail due to information gaps
With sophisticated context engineering:
  • Agents access current, relevant information dynamically
  • Responses are grounded in real data and domain expertise
  • Tool usage is strategic and purposeful
  • Complex workflows execute reliably with proper information flow

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Week 4 learning path

This week builds your expertise in the core components of context engineering:

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Days 16-17: Prompt and message engineering

Master the fundamentals of communication with language models through structured prompts and optimized user messages.

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Days 18-20: Retrieval systems

Implement sophisticated information retrieval systems using PostgreSQL, MongoDB, and Neo4j to provide agents with dynamic access to relevant data.

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Days 21-22: Advanced graph knowledge systems

Explore cutting-edge knowledge graph approaches using Dgraph for complex reasoning and relationship modeling.

Day 16: agent system prompts

Master prompt structure, iteration techniques, tool use optimization, and structured output generation for reliable agent behavior.

Day 17: agent user messages

Learn best practices for crafting user messages that elicit optimal agent responses and enable complex task completion.

Day 18: retrieval with postgresql

Build RAG systems with Supabase and PostgreSQL, implementing semantic search over structured product catalogs.

Day 19: retrieval with MongoDB

Implement document-based retrieval systems using MongoDB Atlas for unstructured data like product reviews and feedback.

Day 20 - GraphRAG with Neo4j

Explore graph-based retrieval augmented generation using Neo4j for complex relationship reasoning and knowledge discovery.

Day 21: dgraph data modeling

Learn advanced graph data modeling concepts with Dgraph, building sophisticated knowledge graphs from real-world data.

Day 22: dgraph querying

Master DQL (Dgraph Query Language) for complex graph queries and integrate Dgraph with your agents using multiple client libraries.

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The context engineering mindset

Effective context engineering requires thinking systematically about information flow:
  • Information architecture How should knowledge be structured for optimal agent access?
  • Retrieval strategy What information does the agent need, when, and in what format?
  • Tool orchestration How should agents coordinate multiple information sources and tools?
  • Quality assurance How do we ensure information accuracy and relevance?
  • Performance optimization How do we balance information completeness with response speed?

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Real-world applications

By the end of Week 4, you’ll be able to build agents that:
  • Customer Support Agents that dynamically retrieve product information, order history, and knowledge base articles
  • Research Assistants that synthesize information from multiple databases and external sources
  • Business Intelligence Agents that query complex data relationships and provide actionable insights
  • Content Creation Agents that access brand guidelines, style guides, and historical content for consistent output

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Prerequisites

  • Completion of Weeks 1-3 (agent fundamentals and domain specialization)
  • Access to Hypermode Pro for advanced integrations
  • Willingness to work with databases and data modeling concepts

Ready to Start Week 4?

Begin with Day 16: agent system prompts - master the foundation of agent communication and behavior guidance.
Transform your agents from conversational tools to intelligent systems that reason about complex problems with sophisticated context engineering.