Context Engineering for Multi-Agent LLM Code Assistants
Context Engineering for Multi-Agent LLM Code Assistants
Using Elicit, NotebookLM, ChatGPT, and Claude Code
description Abstract
Large Language Models (LLMs) have shown promise in automating code generation, yet they struggle with complex, multi-file projects due to context limitations. We propose a novel context engineering workflow combining multiple AI components: an Intent Translator (GPT-5), Elicit-powered semantic literature retrieval, NotebookLM-based document synthesis, and a Claude Code multi-agent system. Our approach leverages intent clarification, retrieval-augmented generation, and specialized sub-agents to significantly improve accuracy and reliability of code assistants in real-world repositories.
error_outline Problem Statement
- LLMs struggle with complex, multi-file projects due to context limitations
- Single-agent approaches often produce incomplete or incorrect solutions
- Knowledge gaps when confronted with unfamiliar APIs or frameworks
- Static context files cannot capture all relevant details for every possible task
lightbulb Proposed Solution
- Novel context engineering workflow combining multiple AI components
- Intent clarification, retrieval-augmented generation, and specialized sub-agents
- Orchestrated via Claude's agent framework with role decomposition
- Targeted context injection for better adherence to project context
integration_instructions Key Components
translate Intent Translator (GPT-5)
Clarifies user requirements and translates them into structured task specifications for the multi-agent system.
search Elicit Semantic Retrieval
Performs semantic search over academic papers, documentation, and Q&A resources to inject domain knowledge.
summarize NotebookLM Synthesis
Creates concise summaries of retrieved materials and answers follow-up questions for detailed understanding.
groups Claude Code Multi-Agent
Orchestrates specialized sub-agents (planner, coder, tester, reviewer) with vector database for code context.
insights Results & Performance
compare Comparison with Other Frameworks
| Framework | Approach | Success Rate | Key Advantage |
|---|---|---|---|
| Our System | Context engineering + multi-agent | 68.2% | Targeted context injection |
| CodePlan | Multi-step planning | 45.3% | Structured approach |
| MASAI | Modular architecture | 28.3% | Specialized sub-agents |
| HyperAgent | Team of agents | 52.7% | Human-like workflow |
rocket_launch Implications & Future Work
- Production-ready deployment with CI/CD integration
- Context management strategies for large-scale projects
- Cost optimization for multi-agent systems
- Extension to other software engineering domains
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