Definition
What is Structured Context?
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Context delivered to AI models as organized, typed records with named fields rather than raw prose or unformatted text.
Structured context improves AI accuracy by reducing token waste, exploiting model attention patterns, and enabling precise retrieval. Research shows format choice alone can swing LLM accuracy by up to 40%. Wire automatically transforms uploaded files into structured, typed records that agents can query efficiently.
Further reading
Articles about Structured Context
Tool-based agent memory: why 2026 benchmarks favor it
Tool-based agent memory exposes store, retrieve, and navigate as callable MCP tools. 2026 benchmarks from Mem0, Memanto, and Wire show why the pattern wins.
Why AI customer support replies sound generic
AI support replies sound generic because teams treat brand voice as a prompt problem. Context engineering fixes it by selecting the right exemplars.
TOON vs JSON: why smaller doesn't mean cheaper for LLMs
TOON looks more compact than JSON, but a 9,649-test study found it cost LLMs 38% more tokens. The reason: model training distribution beats format size.
Provenance is a context engineering primitive, not a trust score
Retrieval provenance for AI agents isn't an audit log or a trust verdict. It's structural metadata (source, position, time, edges) agents use to plan.
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