Emergent Directions for Agentic AI

Large Action Models (LAMs) 
 Models designed to reason about actions, workflows, and consequences. Used for: 
 ●       Browser automation 
 ●       Enterprise workflows 
 ●       Multi-step operations 
 ●       Transaction execution 
 
 Model Specialization 
 
 
 
 
 
 Role 
 
 
 Best Model Type 
 
 
 
 
 Parsing 
 
 
 Small / Fast 
 
 
 
 
 Drafting 
 
 
 Small / Medium 
 
 
 
 
 Planning 
 
 
 Medium / Large 
 
 
 
 
 Verification 
 
 
 Large / Low-temp 
 
 
 
 
 Compliance 
 
 
 Large / Deterministic 
 
 
 
 
 Long Context 
 
 
 Long-context specialist 
 
 
 
 
 
 Context Engineering 
 Context engineering is now widely treated as the successor discipline to prompt engineering. It is the deliberate design of everything a model sees on every inference call: system prompt, user input, retrieved documents, conversation history, tool definitions, and long-term memory. The shift happened for three reasons: agents fail on state-management, not phrasing; context windows grew past 1M tokens, making naive "stuff everything in" both expensive and worse (the "lost in the middle" effect); and "context rot",  degraded performance as context grows became a measurable, actively-managed phenomenon rather than a curiosity. 
 The Four-Layer Context Stack 
 
 
 
 
 
 Layer 
 
 
 Contents 
 
 
 Reference Cadence 
 
 
 
 
 System context 
 
 
 Instructions, persona, output schema, tool definitions 
 
 
 Per deployment 
 
 
 
 
 Persistent context 
 
 
 Memory of prior sessions, user preferences, learned facts 
 
 
 Per session / weekly 
 
 
 
 
 Retrieved context 
 
 
 RAG hits, search results, DB lookups, document chunks 
 
 
 Per turn 
 
 
 
 
 Workflow / state context 
 
 
 Prior agent decisions, current plan, tool outputs so far 
 
 
 Per step