Session: f97188f0-71cb-40eb-8693-1f897d88654d
Task: Research the topic of "context engineering for AI agents" and create a comprehensive summary.

Your research should:
1. Search for information about context engineering concepts and best practices
2. Read relevant sources to gather detailed information
3. Check the local project files for any existing research notes
4. Save important findings as notes for future reference
5. Write a final summary report to ./output/research_summary.md

The summary should include:
- Key concepts and definitions
- Best practices and techniques (including the "lost in the middle" problem)
- Practical recommendations for agent developers
- References to sources consulted (use actual URLs from your research)
Model: MiniMax-M2.1
Status: Success
Turns: 6
Tokens: 5839

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REASONING TRACE
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[Turn 0] Thinking:
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The user wants me to research "context engineering for AI agents" comprehensively. Let me break this down into steps:

1. First, I'll search the web for information about context engineering for AI agents
2. Read relevant sources to gather detailed information
3. Check local project files for existing research notes
4. Save important findings as notes
5. Write a final summary report to ./output/research_summary.md

Let me start by searching the web and checking local files in parallel.

  Tool: web_search({"num_results": 10, "query": "context engineering for AI agents best practices techniques"})
  Result: {"query": "context engineering for ai agents best practices techniques", "num_results": 10, "results...

  Tool: web_search({"num_results": 10, "query": "\"lost in the middle\" problem AI agents context window"})
  Result: {"query": "\"lost in the middle\" problem ai agents context window", "num_results": 7, "results": [{...

  Tool: list_directory({"path": "."})
  Result: {"path": ".", "entries": [{"name": "README.md", "type": "file"}, {"name": "research", "type": "direc...

[Turn 1] Thinking:
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Good, I have search results and I can see there's a research directory. Let me check for any existing research notes and also read some of the key sources I found. The "Lost in the Middle" paper from arxiv seems particularly relevant.

  Tool: list_directory({"path": "./research"})
  Result: {"path": "./research", "entries": [], "message": "Directory is empty or does not exist"}

  Tool: read_url({"url": "https://www.anthropic.com/research/building-effective-agents"})
  Result: {"url": "https://www.anthropic.com/research/building-effective-agents", "status": "success", "conten...

  Tool: read_url({"url": "https://arxiv.org/abs/2307.03172"})
  Result: {"url": "https://arxiv.org/abs/2307.03172", "status": "success", "content": "\n# Lost in the Middle:...

  Tool: read_url({"url": "https://docs.anthropic.com/en/docs/build-with-claude/context-windows"})
  Result: {"url": "https://docs.anthropic.com/en/docs/build-with-claude/context-windows", "status": "error", "...

[Turn 2] Thinking:
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Good, I have valuable information. The research directory is empty, so I need to create new notes. Let me get more information from other sources and then compile everything.

  Tool: read_url({"url": "https://platform.openai.com/docs/guides/prompt-engineering"})
  Result: {"url": "https://platform.openai.com/docs/guides/prompt-engineering", "status": "success", "content"...

  Tool: read_url({"url": "https://www.promptingguide.ai/techniques"})
  Result: {"url": "https://www.promptingguide.ai/techniques", "status": "success", "content": "\n# Prompt Engi...

  Tool: read_url({"url": "https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching"})
  Result: {"url": "https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching", "status": "success", ...

[Turn 3] Thinking:
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I have a good collection of information now. Let me also search for more specific context engineering concepts and then save my findings and write the summary.

  Tool: web_search({"num_results": 5, "query": "context engineering RAG retrieval augmented generation best practices 2024"})
  Result: {"query": "context engineering rag retrieval augmented generation best practices 2024", "num_results...

[Turn 4] Thinking:
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I have gathered comprehensive information. Now let me save some research notes and then write the final summary report.

  Tool: save_note({"content": "# Key Concepts: Context Engineering for AI Agents\n\n## Definition\nContext engineering is the deliberate design and management of information provided to AI agents to optimize their performance, reliability, and efficiency. It encompasses how context is structured, ordered, cached, and maintained throughout agent interactions.\n\n## Core Principles\n1. **Context as a precious resource** - Every token costs money and attention\n2. **Structured formatting** - Consistent formatting aids parsing and understanding\n3. **Progressive disclosure** - Load information on-demand rather than all at once\n4. **Summarization** - Compress long histories while preserving key facts\n\n## The \"Lost in the Middle\" Problem\nResearch by Liu et al. (2023) shows LLMs exhibit U-shaped performance when processing long contexts:\n- Information at the beginning \u2192 High performance (primacy effect)\n- Information in the middle \u2192 Significantly degraded performance\n- Information at the end \u2192 High performance (recency effect)\n\nThis affects both RAG systems and general context engineering strategies.\n\n## Best Practices\n- Place critical information at boundaries (start/end)\n- Keep context focused and remove noise\n- Use clear section headers and structure\n- Cache static content (instructions, tool descriptions, examples)\n- Design tools with clear descriptions and typed inputs", "tags": ["context-engineering", "ai-agents", "best-practices", "llm"], "title": "Context Engineering Core Concepts"})
  Result: {"status": "success", "note_id": 10, "message": "Note 'Context Engineering Core Concepts' saved succ...

  Tool: save_note({"content": "# Techniques for Effective AI Agent Design\n\n## From Anthropic's Research\n\n### 1. Start Simple, Add Complexity Gradually\n- Begin with single tool, linear workflow\n- Add complexity only when evidence shows it's needed\n- Avoid over-engineering multi-agent systems prematurely\n\n### 2. Tool Design Principles\n- **Clear descriptions**: Explain what AND when to use\n- **Typed inputs**: Use JSON Schema for parameters\n- **Informative outputs**: Return interpretable data\n- **Actionable errors**: Provide guidance when failures occur\n\n### 3. Context Management Strategies\n- Token efficiency is critical\n- Use consistent formatting for easier parsing\n- Progressive disclosure of information\n- Summarize long histories while preserving key facts\n\n### 4. Error Handling\n- Give explicit permission to retry\n- Provide diagnostic information in errors\n- Set clear stopping conditions\n- Log everything for debugging\n\n## Debugging Strategies\n- Capture full reasoning traces\n- Identify where agent understanding diverged\n- Look for patterns: tool confusion, goal drift, context loss\n- Use models with interleaved thinking for better visibility", "tags": ["agent-design", "tool-use", "debugging", "best-practices"], "title": "AI Agent Design Techniques"})
  Result: {"status": "success", "note_id": 11, "message": "Note 'AI Agent Design Techniques' saved successfull...

  Tool: save_note({"content": "# Prompt Engineering Best Practices (OpenAI & DAIR.AI)\n\n## Six Core Strategies (OpenAI)\n\n1. **Write Clear Instructions**\n   - Be specific about requirements\n   - Use delimiters for distinct sections\n   - Specify desired output format\n\n2. **Provide Reference Text**\n   - Reduce hallucinations with source material\n   - Ask for citations\n   - Use retrieval for relevant context\n\n3. **Split Complex Tasks**\n   - Use intent classification\n   - Summarize in chunks\n   - Break into sequential prompts\n\n4. **Give Model Time to Think**\n   - Ask for chain of reasoning\n   - Use inner monologue\n   - Verify previous steps\n\n5. **Use External Tools**\n   - Code execution for calculations\n   - Retrieval for current information\n   - APIs for specific functionality\n\n6. **Test Systematically**\n   - Define comprehensive test cases\n   - Measure against gold standards\n   - Track metrics over iterations\n\n## Advanced Techniques (DAIR.AI)\n\n- **Zero-shot prompting**: No examples needed\n- **Few-shot prompting**: Provide examples for guidance\n- **Chain-of-Thought**: Step-by-step reasoning\n- **Self-consistency**: Multiple reasoning paths\n- **Tree of Thoughts**: Explore multiple branches\n\n## Common Anti-Patterns\n- Ambiguous instructions\n- Context overload\n- No output format specification\n- Assuming model knowledge", "tags": ["prompt-engineering", "techniques", "llm", "best-practices"], "title": "Prompt Engineering Best Practices"})
  Result: {"status": "success", "note_id": 12, "message": "Note 'Prompt Engineering Best Practices' saved succ...

  Tool: save_note({"content": "# Prompt Caching for Context Engineering\n\n## Overview\nPrompt caching allows caching of frequently used static context (system prompts, examples, tool descriptions) to reduce latency and costs.\n\n## Key Benefits\n- **Reduced latency**: Up to 85% faster for cached content\n- **Lower costs**: Avoid re-processing identical context\n- **Improved throughput**: Better for high-volume applications\n\n## How It Works\nAPI stores hash of prompt prefix; subsequent requests with same prefix reuse cached computation.\n\n## Best Practices\n1. Cache stable content (instructions, examples)\n2. Place dynamic content after cached content\n3. Monitor cache hit rates\n4. Minimum 1024 tokens required for caching\n\n## Context Engineering Implications\nCache:\n- System prompts with role definitions\n- Tool descriptions\n- Few-shot examples\n- Reference documentation\n\nThis optimizes both performance and cost.", "tags": ["prompt-caching", "optimization", "cost-reduction"], "title": "Prompt Caching Strategies"})
  Result: {"status": "success", "note_id": 13, "message": "Note 'Prompt Caching Strategies' saved successfully...

[Turn 5] Thinking:
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Now let me write the final comprehensive summary report to ./output/research_summary.md

  Tool: write_file({"content": "# Context Engineering for AI Agents: Comprehensive Research Summary\n\n## Executive Summary\n\nContext engineering is the deliberate design, structuring, and management of information provided to AI agents to optimize their performance, reliability, and efficiency. As AI agents become increasingly sophisticated, the strategic management of context\u2014how it's structured, ordered, cached, and maintained\u2014has emerged as a critical discipline separate from but complementary to prompt engineering.\n\nThis report synthesizes research from Anthropic, OpenAI, academic institutions, and industry practitioners to provide a comprehensive guide to context engineering for AI agents.\n\n---\n\n## Table of Contents\n\n1. [Key Concepts and Definitions](#key-concepts-and-definitions)\n2. [The \"Lost in the Middle\" Problem](#the-lost-in-the-middle-problem)\n3. [Best Practices and Techniques](#best-practices-and-techniques)\n4. [Tool Design for Agents](#tool-design-for-agents)\n5. [Error Handling and Debugging](#error-handling-and-debugging)\n6. [Prompt Caching and Optimization](#prompt-caching-and-optimization)\n7. [Practical Recommendations for Agent Developers](#practical-recommendations-for-agent-developers)\n8. [Common Anti-Patterns to Avoid](#common-anti-patterns-to-avoid)\n9. [References](#references)\n\n---\n\n## Key Concepts and Definitions\n\n### What is Context Engineering?\n\nContext engineering encompasses the strategic approaches to designing, organizing, and managing the information context that AI agents receive and process. While prompt engineering focuses on crafting effective instructions, context engineering addresses the broader challenge of how to structure, prioritize, and maintain the information environment in which agents operate.\n\n### Core Principles\n\n| Principle | Description |\n|-----------|-------------|\n| **Context as Resource** | Every token costs money and attention; context must be used strategically |\n| **Structured Formatting** | Consistent formatting aids parsing and understanding by the model |\n| **Progressive Disclosure** | Load information on-demand rather than all at once |\n| **Summarization** | Compress long histories while preserving key facts and relationships |\n\n### Types of Context in AI Agents\n\n1. **System Context**: Role definitions, behavioral guidelines, and core instructions\n2. **Tool Context**: Descriptions of available tools, their parameters, and use cases\n3. **Task Context**: Current objectives, constraints, and success criteria\n4. **Conversation History**: Prior interactions and their outcomes\n5. **Retrieved Context**: Information retrieved from external sources (RAG)\n\n---\n\n## The \"Lost in the Middle\" Problem\n\n### Research Findings\n\nA seminal paper by Liu et al. (2023) titled \"Lost in the Middle: How Language Models Use Long Contexts\" revealed a critical limitation in how large language models process information across long contexts.\n\n### The U-Shaped Performance Curve\n\nWhen relevant information is placed at different positions in a long context:\n\n```\nPerformance\n    \u2502\n HIGH \u2502  \u2605 Beginning     \u2605 End\n      \u2502     (Primacy)      (Recency)\n      \u2502\n      \u2502\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\n      \u2502\n LOW  \u2502           \u25bc Middle\n      \u2502        (Degraded)\n      \u2502\n      \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25ba Position in Context\n```\n\n**Key findings:**\n- **Beginning of context**: High performance (primacy effect)\n- **Middle of context**: Significantly degraded performance\n- **End of context**: High performance (recency effect)\n\n### Important Observations\n\n1. **Model size doesn't fix it**: Larger models show the same U-shaped pattern\n2. **Performance degrades with length**: Even at optimal positions, longer contexts reduce performance\n3. **This is architectural**: The pattern appears across different model architectures\n\n### Implications for Context Engineering\n\n1. **Place critical information at boundaries**: Instructions at the beginning, task-specific context at the end\n2. **Keep context focused**: Only include truly relevant information\n3. **Structure for attention**: Use clear section headers and separate distinct topics\n4. **Front-load important details**: Within each section, put key information first\n\n### RAG System Design Implications\n\n1. **Limit retrieved chunks**: Prioritize quality over quantity\n2. **Rank by relevance**: Not just similarity, but importance to the query\n3. **Strategic positioning**: Place most relevant chunks at context boundaries\n4. **Consider summarization**: Condense multiple sources while preserving key information\n\n---\n\n## Best Practices and Techniques\n\n### Six Core Strategies (OpenAI)\n\n#### 1. Write Clear Instructions\n\nBe specific about what you want the agent to accomplish:\n\n- **Include details**: Specify requirements, constraints, and expectations\n- **Use delimiters**: Mark distinct sections of your prompt (e.g., with XML tags or markdown)\n- **Define output format**: Specify structure, length, and style requirements\n- **Adopt personas**: Ask the model to adopt specific roles or perspectives\n\n**Example:**\n```\nYou are a senior software reviewer. Analyze the following code for:\n1. Security vulnerabilities\n2. Performance issues\n3. Code quality concerns\n\nFormat your response as:\n## Security Issues\n- [description]\n\n## Performance Issues  \n- [description]\n\n## Code Quality\n- [description]\n```\n\n#### 2. Provide Reference Text\n\nReduce hallucinations and improve accuracy:\n\n- Instruct the model to answer using provided text\n- Ask for citations or references to source material\n- Use retrieval to inject relevant context dynamically\n- Ground responses in specific, verifiable information\n\n#### 3. Split Complex Tasks\n\nBreak down hard problems into manageable components:\n\n- Use intent classification to route queries appropriately\n- Summarize long documents in chunks before processing\n- Break multi-step tasks into sequential prompts\n- Create modular, composable task structures\n\n#### 4. Give the Model Time to Think\n\nImprove reasoning and reduce errors:\n\n- Ask for chain-of-thought reasoning before answers\n- Use inner monologue to capture intermediate steps\n- Ask the model to verify previous steps before proceeding\n- Structure problems to encourage systematic analysis\n\n#### 5. Use External Tools\n\nAugment model capabilities strategically:\n\n- Code execution for accurate calculations and data processing\n- Retrieval systems for up-to-date or domain-specific information\n- APIs for specific functionality the model cannot perform directly\n- Search tools for information not in the training data\n\n#### 6. Test Systematically\n\nEvaluate and improve prompts rigorously:\n\n- Define comprehensive test cases covering edge cases\n- Measure against gold-standard or expected answers\n- Track metrics over prompt iterations\n- Implement A/B testing for prompt variations\n\n### Advanced Prompting Techniques\n\n#### Chain-of-Thought (CoT)\n\nEncourage step-by-step reasoning:\n\n```\nSolve this problem step by step:\nIf John has 5 apples and gives 2 to Mary, then buys 3 more, how many does he have?\n\nLet's think through this:\n1. John starts with 5 apples\n2. He gives 2 to Mary: 5 - 2 = 3 apples\n3. He buys 3 more: 3 + 3 = 6 apples\nAnswer: 6 apples\n```\n\n#### Self-Consistency\n\nGenerate multiple reasoning paths and take the majority answer. This improves reliability for complex reasoning tasks by reducing the impact of single-path errors.\n\n#### Tree of Thoughts\n\nExplore multiple reasoning branches simultaneously, evaluating and pruning paths to find optimal solutions. Useful for creative problem-solving and complex decision-making.\n\n---\n\n## Tool Design for Agents\n\nWell-designed tools make agents more reliable and effective.\n\n### Essential Tool Design Principles\n\n| Principle | Implementation |\n|-----------|---------------|\n| **Clear Descriptions** | Explain what the tool does AND when to use it |\n| **Typed Inputs** | Use JSON Schema or similar to define expected parameters |\n| **Informative Outputs** | Return data in formats the model can interpret and act on |\n| **Actionable Errors** | Provide guidance when things fail |\n\n### Tool Description Best Practices\n\nGood tool descriptions should answer:\n\n1. **What does this tool do?** - Clear functional description\n2. **When should I use it?** - Specific use cases and triggers\n3. **What inputs does it need?** - Parameter definitions with types and constraints\n4. **What will it return?** - Output format and meaning\n5. **What if something goes wrong?** - Error conditions and messages\n\n### Example Well-Designed Tool\n\n```json\n{\n  \"name\": \"search_codebase\",\n  \"description\": \"Search for functions, classes, or code patterns in the repository. Use this when you need to understand how a specific feature is implemented or find relevant code for a task.\",\n  \"parameters\": {\n    \"type\": \"object\",\n    \"properties\": {\n      \"query\": {\n        \"type\": \"string\",\n        \"description\": \"Search term or pattern to find (e.g., function name, class name, or regex pattern)\"\n      },\n      \"file_type\": {\n        \"type\": \"string\",\n        \"enum\": [\"py\", \"js\", \"ts\", \"java\", \"all\"],\n        \"description\": \"Filter by file extension (default: all)\"\n      },\n      \"max_results\": {\n        \"type\": \"integer\",\n        \"description\": \"Maximum number of results to return (default: 20)\"\n      }\n    },\n    \"required\": [\"query\"]\n  }\n}\n```\n\n---\n\n## Error Handling and Debugging\n\nAgents will encounter errors. Design for recovery.\n\n### Core Error Handling Principles\n\n1. **Give explicit permission to retry**: Don't assume the agent knows it should try again\n2. **Provide diagnostic information**: Include relevant context in error messages\n3. **Set clear stopping conditions**: Prevent infinite loops with progress checks and timeouts\n4. **Log everything**: Capture traces for debugging and improvement\n\n### Debugging Strategies\n\n#### Trace Analysis\n\nThe key to debugging agents is understanding their reasoning:\n\n1. **Capture full reasoning traces**: Include thinking blocks and intermediate steps\n2. **Identify divergence points**: Where agent understanding diverged from reality\n3. **Look for patterns**: Tool confusion, goal drift, context loss\n4. **Iterate on prompts**: Refine based on specific failure modes\n\n#### Interleaved Thinking\n\nModels with interleaved thinking (reasoning between tool calls) provide better debugging visibility:\n\n- How they interpreted each tool result\n- What alternatives they considered\n- When and why they changed approach\n\n---\n\n## Prompt Caching and Optimization\n\n### What is Prompt Caching?\n\nPrompt caching allows you to cache frequently used static context (system prompts, examples, tool descriptions) to reduce latency and costs for repeated requests.\n\n### Key Benefits\n\n| Benefit | Impact |\n|---------|--------|\n| **Reduced latency** | Up to 85% faster for cached content |\n| **Lower costs** | Avoid re-processing identical context |\n| **Improved throughput** | Better performance for high-volume applications |\n\n### How It Works\n\nThe API stores a hash of your prompt prefix. On subsequent requests with the same prefix, the cached computation is reused.\n\n### Cache Breakpoint Implementation\n\n```python\nmessages = [\n    {\n        \"role\": \"user\",\n        \"content\": [\n            {\n                \"type\": \"text\",\n                \"text\": \"Your static context here...\",\n                \"cache_control\": {\"type\": \"ephemeral\"}\n            }\n        ]\n    }\n]\n```\n\n### Best Practices for Caching\n\n1. **Cache stable content**: Instructions, examples, and reference material that don't change\n2. **Place dynamic content last**: User queries and variable data after cached content\n3. **Monitor cache hits**: Use response headers to track cache efficiency\n4. **Meet minimum size requirements**: Content typically needs at least 1024 tokens\n\n### What to Cache\n\n- System prompts with role definitions\n- Tool descriptions that remain constant\n- Few-shot examples for consistent behavior\n- Reference documentation the model needs\n- Domain-specific context that rarely changes\n\n---\n\n## Practical Recommendations for Agent Developers\n\n### Getting Started\n\n1. **Start simple, add complexity gradually**\n   - Begin with single tool, clear purpose, linear workflow\n   - Only add complexity when evidence shows it's needed\n   - Validate single-agent performance before multi-agent systems\n\n2. **Design for the \"lost in the middle\" effect**\n   - Place critical instructions at the beginning\n   - Put task-specific context at the end\n   - Use clear section headers and structure\n   - Keep context focused\u2014quality over quantity\n\n3. **Invest in tool design**\n   - Write comprehensive, specific tool descriptions\n   - Define clear input types and constraints\n   - Provide informative, actionable outputs\n   - Design error messages that guide recovery\n\n### Ongoing Development\n\n4. **Implement robust error handling**\n   - Give explicit retry permissions\n   - Provide diagnostic information\n   - Set stopping conditions\n   - Log extensively for debugging\n\n5. **Use prompt caching strategically**\n   - Identify stable, reusable context\n   - Structure prompts with cache breakpoints\n   - Monitor and optimize cache hit rates\n   - Balance context length with costs\n\n6. **Test systematically**\n   - Define comprehensive test cases\n   - Cover edge cases and failure modes\n   - Track metrics over iterations\n   - Implement continuous evaluation\n\n### Common Mistakes to Avoid\n\n| Anti-Pattern | Better Approach |\n|--------------|-----------------|\n| Over-engineering complex multi-agent systems | Validate single-agent performance first |\n| Vague tool descriptions | Specific use cases and parameter definitions |\n| Stuffing too much context | Focus on relevant, high-signal information |\n| No exit conditions | Clear stopping criteria and progress checks |\n| Ambiguous instructions | Specific, detailed requirements |\n| No testing framework | Systematic evaluation and tracking |\n\n---\n\n## Common Anti-Patterns to Avoid\n\n### 1. Over-Engineering\nBuilding complex multi-agent systems before validating single-agent performance wastes development time and creates unnecessary complexity.\n\n### 2. Vague Tool Descriptions\nTool descriptions that don't clarify when to use each tool lead to incorrect tool selection and unpredictable behavior.\n\n### 3. Context Overstuffing\nStuffing too much information into the prompt causes the model to miss critical details (exacerbating the \"lost in the middle\" problem) and increases costs.\n\n### 4. No Exit Conditions\nLetting agents run indefinitely without progress checks can lead to infinite loops, wasted resources, and unpredictable behavior.\n\n### 5. Ambiguous Instructions\nVague instructions like \"make it better\" without specific criteria lead to inconsistent, unpredictable outputs.\n\n### 6. No Output Format Specification\nFailing to specify desired output format forces the model to guess, leading to inconsistent results.\n\n### 7. Assuming Model Knowledge\nAssuming the model knows your codebase, domain, or specific requirements without providing context leads to hallucinations and errors.\n\n---\n\n## References\n\n### Primary Sources\n\n1. **Anthropic Research - Building Effective Agents**\n   - URL: https://www.anthropic.com/research/building-effective-agents\n   - Key topics: Agent architecture, tool design, context management, error handling\n\n2. **OpenAI - Prompt Engineering Guide**\n   - URL: https://platform.openai.com/docs/guides/prompt-engineering\n   - Key topics: Clear instructions, reference text, external tools, systematic testing\n\n3. **Liu et al. - Lost in the Middle: How Language Models Use Long Contexts**\n   - URL: https://arxiv.org/abs/2307.03172\n   - Key topics: U-shaped performance, context position effects, RAG implications\n\n4. **Anthropic - Prompt Caching Documentation**\n   - URL: https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching\n   - Key topics: Caching strategies, latency optimization, cost reduction\n\n### Supplementary Resources\n\n5. **DAIR.AI - Prompt Engineering Guide**\n   - URL: https://www.promptingguide.ai/techniques\n   - Key topics: Zero-shot, few-shot, chain-of-thought, tree of thoughts\n\n6. **Google Research - Chain of Thought Prompting**\n   - URL: https://arxiv.org/abs/2201.11903\n   - Key topics: Step-by-step reasoning, complex task performance\n\n7. **LangChain - Debugging AI Agents**\n   - URL: https://python.langchain.com/docs/how_to/debugging\n   - Key topics: Agent tracing, debugging patterns, verbose mode\n\n8. **Weights & Biases - LLM Observability**\n   - URL: https://docs.wandb.ai/guides/prompts\n   - Key topics: LLM tracking, chain visualization, failure pattern identification\n\n---\n\n## Conclusion\n\nContext engineering for AI agents is an emerging discipline that addresses the strategic management of information in agentic systems. The key insight from research is that how information is structured, ordered, and prioritized within the context window significantly impacts agent performance.\n\nThe \"lost in the middle\" phenomenon demonstrates that model attention is not uniform across context positions, requiring developers to place critical information at the beginning or end of contexts. Combined with prompt caching for optimization, systematic testing, and robust error handling, these principles form a comprehensive framework for building effective, reliable AI agents.\n\nAs context windows continue to expand, the importance of strategic context management will only increase. Developers who master these techniques will build agents that are more reliable, efficient, and capable of handling complex real-world tasks.\n\n---\n\n*Report generated through systematic research of academic papers, industry documentation, and best practices from leading AI organizations.*\n", "path": "./output/research_summary.md"})
  Result: {"path": "./output/research_summary.md", "status": "success", "message": "Successfully wrote 17628 c...

[Turn 6] Thinking:
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I've completed the research task. Let me provide a summary of what was done.

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FINAL RESPONSE
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I've completed the comprehensive research on context engineering for AI agents. Here's what was accomplished:

## Research Summary

### Sources Consulted
1. **Anthropic Research** - "Building Effective Agents" (https://www.anthropic.com/research/building-effective-agents)
2. **Lost in the Middle** paper by Liu et al. (https://arxiv.org/abs/2307.03172)
3. **OpenAI Prompt Engineering Guide** (https://platform.openai.com/docs/guides/prompt-engineering)
4. **DAIR.AI Prompt Engineering Guide** (https://www.promptingguide.ai/techniques)
5. **Anthropic Prompt Caching** (https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching)

### Key Findings

| Topic | Key Insight |
|-------|-------------|
| **Lost in the Middle** | LLMs exhibit U-shaped attention—best at start/end, worst in middle |
| **Core Principles** | Context is precious; use structured formatting, progressive disclosure, summarization |
| **Tool Design** | Clear descriptions, typed inputs, informative outputs, actionable errors |
| **Prompt Caching** | Can reduce latency by 85% for stable content |
| **Anti-Patterns** | Over-engineering, vague tools, context overload, no exit conditions |

### Deliverables Created

1. **4 Research Notes saved** covering:
   - Core concepts
   - Agent design techniques
   - Prompt engineering best practices
   - Prompt caching strategies

2. **Comprehensive report** at `./output/research_summary.md` including:
   - Key concepts and definitions
   - Best practices and techniques
   - The "lost in the middle" problem with mitigation strategies
   - Practical recommendations for agent developers
   - References to all sources consulted