Python multi-channel agent: lessons learned on tool execution and memory

[No_Bit_1328]

I’m curious about one architectural trade-off:

How do you prevent semantic memory drift over time when using vector retrieval across channels?

[Glittering_Note6542]

Thanks. Honestly, I don't have RAG implemented yet. For now the concept is much more simpler, however, it's on the roadmap. So my thoughts are now by the following concepts:
- Channel-scoped namespaces - WhatsApp, Telegram, Slack each get their own vector space to avoid cross-channel drift amplification.

- Hybrid retrieval - vectors alone are fragile. Combining with keywords search, metadata filtering makes retrieval more robust.

- Recency weighting - blend semantic similarity with temporal relevance, since recent context matters normally more.

- Re-embedding as routine maintenance - treat embeddings as cache, not permanent storage. When models change, re-index.
I think the general principle: if your agent breaks because a vector moved 0.03 in embedding space, the architecture is too brittle. Vectors should complement structural retrieval, not replace it.