Learning Headroom
This course teaches how headroom systems keep long-running AI agents inside their context-window budget without losing the information they need. Instead of treating context management as a black box, it walks through the actual mechanics: token economics, prefix caching, content-aware compression, and the routing logic that decides what gets kept, summarized, or discarded across repeated agent wakes.
You move through a prerequisite path of short, focused concepts, from context-window economics and prompt-layer anatomy through prefix caching and cache-safe forwarding, into compression engines like SmartCrusher and CCR, and finally into configuration, simulation, and end-to-end tuning. Each concept is taught and quizzed one at a time by an AI tutor, so you build a working mental model before combining pieces into a full tuning playbook.
It suits engineers already responsible for agent architecture or LLM infrastructure who want to move past guesswork and reason precisely about cache hit rates, compression tradeoffs, and safe configuration changes. After finishing, you should be able to diagnose headroom problems from observability metrics and design a tuning strategy for your own system.
زمان تقریبی یادگیری: 19 ساعت
آنچه یاد میگیرید
- Explain how token measurement and compression metrics quantify headroom pressure across repeated agent wakes
- Design a stable-prefix and live-zone layout that preserves byte-identical prefix caching across turns
- Configure SmartCrusher eligibility gates, retention strategy, and relevance scoring to compress safely
- Route instruction-bearing content through CCR's reversible compression architecture without losing retrievability
- Compare configuration choices using simulation-driven tuning and observability metrics
- Build an end-to-end headroom tuning playbook, including a repeated-wake digest strategy
سرفصلهای دوره
- Headroom and Context-Window Economics
- Agent Message Anatomy
- Token Measurement and Compression Metrics
- Repeated Wakes and Prompt Layers
- Prefix Caching Fundamentals
- Byte-Identical Prefix Requirements
- Stable-Prefix and Live-Zone Layout
- CacheAligner as an Observability Detector
- Cache-Safe Multi-Turn Forwarding
- Headroom Pipeline and Integration Boundaries
- Content Detection and Content Routing
- Live-Zone-Only Compression
- Cache Mode and Token Mode Tradeoffs
- SmartCrusher Eligibility Gates
- SmartCrusher Retention Strategy
- Deduplication, Similarity, and Change Points
- Relevance Scoring for Retention
- Tuning SmartCrusher Safely
- Specialized Compressors and Preservation Rules
- Instruction-Bearing Content Routing
- CCR Reversible Compression Architecture
- CCR Lifetime and Retrieval Failure Handling
- Compression Strategy Metadata and Routing Authority
- Headroom Configuration Scopes and Precedence
- Context Budget and Output Buffer Tuning
- Simulation-Driven Configuration Comparison
- Observability Metrics and Tuning Decisions
- Designing a Repeated-Wake Digest Strategy
- End-to-End Headroom Tuning Playbook
شروع این دوره در تیچمی