Build a Context-Managed AI Assistant
From a naive chatbot to a production-grade context-engineered assistant, implementing all four WSCI strategies.
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Step 1: Environment Setup
Install the Anthropic SDK and set up a project structure for our context-managed assistant.
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Step 2: System Prompt Design
A well-designed system prompt is the foundation. Keep it focused. Every token matters.
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Step 3: Conversation History (Sliding Window)
Implement a sliding window that keeps recent messages within a token budget.
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Step 4: Memory System
Implement a Write strategy: persistent memory that survives across sessions.
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Step 5: RAG Context Selection
Implement a Select strategy: retrieve only relevant emails/documents.
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Step 6: Dynamic Tool Routing
Only expose tools relevant to the current query. Fewer tools = better selection accuracy.
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Step 7: Context Compression
Implement auto-compact: when context reaches a threshold, summarize the conversation.
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Always preserve: (1) system prompt, (2) user constraints like "no morning meetings", (3) unresolved tasks. Brevity bias will drop these if you're not careful.
Step 8: Sub-Agent Orchestration
Isolate complex tasks into focused sub-agents, each with their own clean context.
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Production Checklist:
- Token budget monitoring on every API call
- Memory persistence with versioning
- RAG retrieval with relevance scoring
- Dynamic tool routing (never expose all tools)
- Auto-compact with critical info preservation
- Sub-agent isolation for multi-step tasks