Source:
https://www.motivenotes.ai/p/what-makes-5-of-ai-agents-actually
Selected points:
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Most founders think they’re building AI products. They’re actually building context selection systems.
'The base models are the soil; context is the seed.'
Context engineering considered as LLM-native feature engineering:
- Selective context pruning = feature selection
- Context validation = schema/type/recency checks
- 'Context observability' = trace which inputs improved/worsened output quality
- Embedding augmentation with metadata = typed features + conditions
Goal: Treat context like a versioned, auditable, testable artifact, not a string blob.
Important:
- You must trace which inputs led to which outputs (lineage)
- You must respect row level, role based access (policy gating)
- If two employees ask the same question, but have different permissions, the model output should differ.
AI chat conversation works when it removes a learning curve.