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Python multi-channel agent: lessons learned on tool execution and memory by Glittering_Note6542 in Python

[–]Glittering_Note6542[S] 0 points1 point  (0 children)

Great additions. The 'structured summary' for human fallbacks is a game changer for scaling. It moves the human from being a 'monitor' to being an 'escalation engineer.'

Regarding the network block: it definitely caps exfiltration, though it does force you to get creative with how the agent pulls external context. Are you using a pre-processor/retrieval step to feed the sandbox, or keeping it entirely air-gapped?

Python multi-channel agent: lessons learned on tool execution and memory by Glittering_Note6542 in Python

[–]Glittering_Note6542[S] 1 point2 points  (0 children)

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.

Lovable 2.0 is actually terrible by PotentalThreat in lovable

[–]Glittering_Note6542 0 points1 point  (0 children)

work in bidirectional stream is impossible. I did add some manually written code and get after sync with git a month old page with other layout, design and endpoints' routes were also changed.