The problem
Agents often plan with context captured minutes, hours, or days before the source changed. Larger context windows do not solve stale source truth.
Symptoms
Signals that the issue is happening in production, not just in a benchmark.
The agent responses from a prior product plan after the live policy changed.
A support bot cites a ticket state that has already been resolved.
A coding agent uses generated types or file context from the wrong branch.
Teams cache retrieval for speed but cannot prove the cache was invalidated after source changes.
How KyroDB solves
KyroDB solves this at the runtime boundary before prompt assembly.
KyroDB models freshness ownership as write-through, event-feed, snapshot-bundle, or best-effort.
Strict mode blocks stale or unprovable context instead of silently reusing it.
Balanced mode can reuse context while preserving warnings and proof boundaries.
Change events let applications invalidate scoped context when business knowledge moves.
Implementation
Practical steps for teams already using an agent backend, vector store, or RAG pipeline.
- 01
Choose the strongest freshness ownership mode your source system supports.
- 02
Make tenant, namespace, filters, and policy part of the reuse key.
- 03
Emit change events for documents, policies, tickets, entitlements, and repo state.
- 04
Inspect ContextPacket status, warnings, omissions, and trace evidence before building the prompt.
When not to use it
If your corpus is immutable and public, basic retrieval plus periodic re-indexing may be enough.
What is strict freshness mode?
Strict freshness mode means the runtime must not serve context unless it can prove the source boundary is fresh enough for the requested scope.
Do embeddings need to be regenerated for every change?
Not always. The runtime needs a trustworthy freshness signal for the source and retrieval boundary; regeneration strategy depends on the source and connector.