# Source: https://docs.hypermode.com/agents/30-days-of-agents/context-engineering.md

# Week 4: Context Engineering - Building Intelligent Information Systems

> Master the art of context engineering - building dynamic systems that provide the right information and tools in the right format to enable agents to accomplish complex tasks effectively.

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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.

## 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

## 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* Agents hallucinate or provide outdated information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

## Week 4 learning path

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

### Days 16-17: Prompt and message engineering

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

### Days 18-20: Retrieval systems

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

### Days 21-22: Advanced graph knowledge systems

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

<CardGroup cols={2}>
  <Card title="Day 16: agent system prompts" href="/agents/30-days-of-agents/day-16">
    Master prompt structure, iteration techniques, tool use optimization, and structured output generation for reliable agent behavior.
  </Card>

  <Card title="Day 17: agent user messages" href="/agents/30-days-of-agents/day-17">
    Learn best practices for crafting user messages that elicit optimal agent
    responses and enable complex task completion.
  </Card>

  <Card title="Day 18: retrieval with postgresql" href="/agents/30-days-of-agents/day-18">
    Build RAG systems with Supabase and PostgreSQL, implementing semantic search
    over structured product catalogs.
  </Card>

  <Card title="Day 19: retrieval with MongoDB" href="/agents/30-days-of-agents/day-19">
    Implement document-based retrieval systems using MongoDB Atlas for
    unstructured data like product reviews and feedback.
  </Card>

  <Card title="Day 20 -  GraphRAG with Neo4j" href="/agents/30-days-of-agents/day-20">
    Explore graph-based retrieval augmented generation using Neo4j for complex
    relationship reasoning and knowledge discovery.
  </Card>

  <Card title="Day 21: dgraph data modeling" href="/agents/30-days-of-agents/day-21">
    Learn advanced graph data modeling concepts with Dgraph, building
    sophisticated knowledge graphs from real-world data.
  </Card>

  <Card title="Day 22: dgraph querying" href="/agents/30-days-of-agents/day-22">
    Master DQL (Dgraph Query Language) for complex graph queries and integrate Dgraph with your agents using multiple client libraries.
  </Card>
</CardGroup>

## 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?

## 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

## 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

<Card title="Ready to Start Week 4?" icon="arrow-right" href="/agents/30-days-of-agents/day-16">
  Begin with Day 16: agent system prompts - master the foundation of agent
  communication and behavior guidance.
</Card>

***

*Transform your agents from conversational tools to intelligent systems that
reason about complex problems with sophisticated context engineering.*