Context Engeineering Advanced Techniques
Not just the right context. RIght context, at the right time.
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๐ Context Engineering Advanced Techniques
The art and science of providing AI with just the right information at the right time ๐ง โก
This repository demonstrates advanced context engineering techniques for building reliable AI agents that can work on large-scale production projects. Based on the latest trends in AI development, these techniques go beyond simple prompting to create truly effective autonomous agents.
๐ Table of Contents
- ๐ฏ What is Context Engineering?
- ๐ The RITER Method
- ๐ ๏ธ Three Advanced Techniques
- โก Quick Start
- ๐๏ธ Project Structure
- ๐ฆ Prerequisites
- ๐ก Key Insights
๐ฏ What is Context Engineering?
Context Engineering is NOT just about:
- โ Simply providing static files to an agent
- โ Creating PRDs (Product Requirement Documents)
- โ Basic prompting techniques
It IS about:
- โ Providing the right information for the next step
- โ Dynamically updating context as the agent works
- โ Making long-horizon agents reliable
- โ Reducing compounding errors in multi-step tasks
๐ Why Now?
Context engineering emerged because:
- We want agents to DO things - Not just plan, but execute autonomously
- Tasks are becoming complex - From simple queries to booking entire trips
- Reliability matters - Even 1-2% error rates compound over multiple steps
- Production readiness - Real projects need real context, not just examples
๐ The RITER Method
Our framework for effective context engineering:
1. Retrieve ๐ฅ
- Fetch relevant information from internal systems
- Sources: Slack, Notion, Zendesk, GitHub, etc.
- Use MCP servers and web sources
2. Integrate ๐
- Add to chat history or agent state
- Use system prompts strategically
- Implement vector databases for RAG
3. Transform (Generate) ๐จ
- Agent generates response using tools
- Apply structured outputs and guardrails
- Execute actions based on context
4. Extract (Highlight) ๐
- Set up observability to track decisions
- Summarize key moments and decisions
- Extract only the most relevant details
5. Route (Transfer) ๐ค
- Update internal systems with results
- Pass context to next agent/step
- Maintain agent memory/scratchpad
๐ The cycle continues! The last step updates the first step for continuous improvement.
๐ ๏ธ Three Advanced Techniques
1. Asynchronous Context Synchronization ๐
What: Sync data sources in real-time using Airbyte to maintain up-to-date context.
Why: Large production projects have thousands of messages, decisions, and updates that agents need to understand.
Implementation:
- Set up Airbyte to sync Slack โ AstraDB
- Create MCP server for vector search
- Query historical conversations and decisions
# Example: Query project context
results = await query_astradb(
collection_name="saasdev",
query="github app deployment",
num_results=10
)
2. Self-Maintained Agent Workspace ๐
What: Agents maintain their own working memory throughout task execution.
Why: Keeps track of decisions, progress, and context without overwhelming the main prompt.
Implementation:
- Create workflow files in
.cursor/rules/ - Define task workflows with context retrieval
- Agent updates its own scratchpad
# Example workflow structure
1. Read task from Notion
2. Pull related Slack messages
3. Check previous decisions
4. Update scratchpad with progress
5. Execute development tasks
3. Multi-Agent Context Compression ๐๏ธ
What: Pass only key decisions and moments between agents, not entire conversations.
Why: Prevents information loss and keeps agents aligned on complex tasks.
Implementation:
class SendMessageWithContext(SendMessage):
# Extends standard messaging with:
# - key_moments: Critical decisions made
# - decisions: Specific choices affecting next steps
โก Quick Start
1. Clone the Repository
git clone https://github.com/yourusername/context-engineering-advanced-techniques.git
cd context-engineering-advanced-techniques
2. Set Up Environment
# Create virtual environment
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
3. Configure Credentials
Create a .env file:
# AstraDB credentials
ASTRA_DB_API_ENDPOINT=https://your-database-id-region.apps.astra.datastax.com
ASTRA_DB_APPLICATION_TOKEN=your-application-token-here
# OpenAI (for embeddings)
OPENAI_API_KEY=your-openai-api-key
4. Run Examples
Astra MCP Server:
cd astra-mcp-server
python server.py
Multi-Agent Example:
cd multi-agent-example
python send_message_with_context.py
๐๏ธ Project Structure
context-engineering-advanced-techniques/
โโโ ๐ astra-mcp-server/ # MCP server for vector search
โ โโโ server.py # FastMCP server implementation
โ โโโ README.md # Server documentation
โ โโโ CURSOR_SETUP.md # Cursor IDE configuration guide
โ
โโโ ๐ multi-agent-example/ # Context-aware multi-agent demo
โ โโโ send_message_with_context.py # Enhanced agent communication
โ
โโโ requirements.txt # Python dependencies
โโโ README.md # This file
๐ฆ Prerequisites
- Python 3.7+ ๐
- AstraDB account (free tier available) ๐
- OpenAI API key (for embeddings) ๐
- Airbyte (optional, for data synchronization) ๐
- Cursor IDE (optional, for MCP integration) ๐ป
๐ก Key Insights
๐ฏ Remember: It's About Timing
- Context must be dynamically updated as agents work
- The right information at the right time is crucial
- Static context leads to unreliable agents
๐ Why This Matters
- Exponential task complexity - AI capabilities are growing rapidly
- Compounding errors - Small mistakes multiply over many steps
- Production readiness - Real projects need real context
๐ฎ Future-Proof Your Agents
Even as models get larger and cheaper:
- Relevant context will always improve accuracy
- Latency and costs still matter
- Precision beats volume
๐ Best Practices
- Start broad, then narrow - Use semantic search effectively
- Observe everything - Set up proper observability
- Extract key decisions - Don't pass entire conversations
- Update continuously - Context should evolve with the task
๐ Learn More
- ๐ฅ Watch the full tutorial video
- ๐ Read about Agency Swarm
- ๐ง Explore FastMCP
- ๐ AstraDB Documentation
๐ค Contributing
Feel free to open issues or submit PRs to improve these examples!
๐ License
This project is open source and available under the MIT License.
Remember: Context Engineering is not just a techniqueโit's the key to making AI agents truly reliable and production-ready! ๐
