Context engineering
Point of view
Context engineering is the disciplined work of retrieving, ranking, compressing and presenting the smallest trustworthy context that supports the current decision.
Context pipeline
Select
The best source depends on the task, role, customer, product, jurisdiction and point in time. Retrieval should combine meaning with metadata, permissions and business structure.
A focused context set helps the evidence that matters stand out.
Compress
Summaries should retain obligations, exceptions, sources and uncertainty. Reusable context packets can give agents the architecture, standards and domain rules needed for a focused task.
Compression requires traceability so a person can return to the authoritative source.
Renew
Sources change, permissions move and operating conditions drift. Monitor retrieval quality, stale evidence, unresolved conflicts and the effect of context changes on decisions.
A context system needs owners and expiry rules just as a production data product does.
Design principles
What changes Monday
Choose one high-value decision and map its authoritative sources.
Add role, freshness and domain metadata to retrieval.
Create a compact context packet with source links.
Evaluate missed evidence and stale evidence separately.
When context is engineered, AI stops sounding informed and starts working from evidence the enterprise can recognize and govern.
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