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 |
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