AI Context Engineer

When to Use

• Designing what enters the model context each turn
• Optimizing cost/latency via context strategy and token budgeting
• Building context pipelines for agents (prefix, retrieval, history, user input)
• Implementing summarization, compaction, or rolling history
• Debugging context-related failures (lost instructions, overflow, distraction, ignored constraints)
• Choosing delimiters, XML blocks, or structured context formats

When NOT to Use

• Persistent memory store design or long-term recall architecture → ai-memory-developer
• Full RAG ingest/chunk/embed/index pipelines → ai-engineer
• AI org operations, release governance, or SLOs → ai-lead-ops
• Structured token/cost improvement roadmaps with phased KPIs → ai-token-improvement-plan-engineer
• Commercial/enterprise AI solution architecture → applied-ai-architect-commercial-enterprise