Context Economics
Skill 5: Context Economics and Optimization
The economic foundation that makes production agentic AI financially viable.
Overview
Skill 5 represents the critical competency for managing the most valuable and expensive resource in agentic AI systems: context. Every token included in a prompt consumes computational resources, increases latency, and incurs direct costs. The 2026 AI strategist must act as a "context economist," mastering sophisticated caching, compression, and optimization strategies.
The Three Sub-Skills
| Sub-Skill | Focus Area | Key Concepts |
|---|---|---|
| 5.1 Prefix Caching | Leveraging computational reuse in inference engines | KV cache, prefix caching, workflow-aware eviction |
| 5.2 Context Compaction | Reducing context size while preserving information | Hierarchical summarization, sliding windows, semantic compression |
| 5.3 Plan Caching | Caching and reusing reasoning structures | Abstract plan caching, plan similarity, dynamic adaptation |
5.1 Prefix Caching and KV Cache Management
Prefix Caching Fundamentals
- Core Principle: Reusing KV cache from previous computations for shared prompt prefixes
- Benefits: 50-90% reduction in TTFT for cached prefixes, proportional cost savings
- Use Cases: RAG systems with stable contexts, agents with fixed system prompts
Cache-Friendly Prompt Design
Design Pattern:
[System Prompt - Static]
[RAG Context - Semi-Static]
[Conversation History - Dynamic]
[Current User Query - Unique]
Place static, expensive content at the beginning (prefix) and dynamic content at the end.
Platform Caching Implementations
- Anthropic Prompt Caching: 90% cost reduction for cached tokens, 5-minute TTL
- OpenAI Prompt Caching: Automatic caching, 50% cost reduction
- Gemini Context Caching: Explicit cache API, up to 32K tokens
5.2 Context Compaction and Summarization
Hierarchical Summarization
Multi-level summarization with dynamic granularity:
- Per-turn summaries: Brief summary of each exchange
- Per-session summaries: Summary of entire conversation
- Per-user summaries: Long-term profile and patterns
Compression ratios: 5:1 to 20:1 depending on granularity.
Sliding Window with Summarization
Pattern:
[Summarized History: Turns 1-50]
[Detailed History: Turns 51-60]
[Current Turn: 61]
Maintains detailed recent history and summarized older history.
Semantic Compression Techniques
- Entity extraction: Identify and preserve key entities
- Coreference resolution: Replace repeated references with compact representations
- Information-theoretic compression: Entropy-based methods for high-information content
5.3 Agentic Plan Caching
Abstract Plan Reuse
- Core Principle: Cache entire reasoning plans for reuse on similar requests
- Pattern: "Book a flight" and "book a hotel" follow similar abstract plans
- Benefits: 40-60% reduction in latency and cost for routine tasks
Plan Similarity Detection
- Plan embeddings: Encode plans as vectors capturing semantic structure
- Similarity metrics: Define thresholds for when plans are "similar enough"
- Retrieval systems: Efficiently search plan cache for similar plans
Dynamic Plan Adaptation
- Parameter substitution: Replace placeholders with actual values
- Plan validation: Verify adapted plan is valid for current context
- Correction and refinement: Adjust if validation fails
Real-World Cost Savings
| Scenario | Naive Cost | Optimized Cost | Savings |
|---|---|---|---|
| Customer Service (10K convos/day) | $5,000/day | $800/day | 84% |
| Code Generation (50K token context) | $0.50/request | $0.05/request | 90% |
| Research Assistant (document analysis) | $2.00/query | $0.30/query | 85% |
| Workflow Automation (1K bookings/day) | $1,000/day | $450/day | 55% |
Transferable Competencies
Mastering Skill 5 requires proficiency in:
- Caching Theory: Cache hierarchies, eviction policies, hit rate optimization
- Computational Economics: Cost modeling, resource allocation, optimization
- Information Theory: Compression, entropy, information preservation
- Natural Language Processing: Summarization, entity extraction, semantic analysis
- Workflow Analysis: Graph analysis, pattern recognition, predictive modeling
- Performance Engineering: Profiling, bottleneck identification, optimization
Common Pitfalls
- Ignoring caching: Not leveraging platform caching features
- Poor prompt structure: Dynamic content before static content
- Over-compression: Aggressive summarization losing critical information
- Static eviction policies: Using LRU without considering workflow patterns
- No cost tracking: Not measuring economic impact of optimizations
- Premature optimization: Optimizing before understanding usage patterns
- Cache invalidation failures: Not properly invalidating stale content
- Ignoring platform differences: Not adapting to platform-specific implementations
Key Technologies
Platform Caching
- Anthropic Prompt Caching (90% cost reduction, 1024+ token minimum)
- OpenAI Prompt Caching (automatic, 50% cost reduction)
- Gemini Context Caching (explicit API, up to 32K tokens)
Research & Tools
- KVFlow (workflow-aware KV cache management)
- Agentic Plan Caching (APC)
- LangChain Memory (context management utilities)
- LlamaIndex (RAG with caching support)
The Bottom Line
Skill 5 is the economic foundation that makes production agentic AI financially viable. Context is expensive—every token costs money and time. Mastering context economics through caching, compression, and optimization is the difference between a costly prototype and a profitable product.
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