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  • I don't understand. If you're already building the rules, why have a clanker summarize and guess why the result happened when you can display the rules that passed/failed?
    by nickphx
  • For that there's Python: with 30 lines you can automate this task. If you want I can send you the script I use. DM me if interested.
    by lfdo0870
  • This is a really interesting concept. Could be quite useful for agents. I worked on something similar, but more abstract; you managed to take it to the practical level.
    by awfm9
  • I was expecting to see some Jev based influence on RETE/CEP rule handling ... the Typesafe.ai team have a lot more in the pipeline this use case is just getting started
    by jimmySixDOF
  • I've used rete based CEP tool in the past for utility operations event management. We could tag the rule used to the event or event aggregate, but it was on a single node basis and a basic aggregate. If there was more coverage in understanding it would be a big help to operators. Based on say a set of outage events, can it further understand if it weather event, equipment failure, scheduled maintenance, etc. Linking it to possible operating procedures based on a broader understanding of the state would be very useful.
    by lunatuna
  • It's been a while since I used a Rete rule engine, back in my days as math nerd at a mortgage bank (ooof we survived the 08 meltdown by the skin of our teeth) we used an internal fork of Drools and paired it with a merkle tree datasctructure to store lending decisions.... I knew blockchain was gonna be a thing when the genesis block referenced the bailout... I kick myself daily cause should've bought more bitcoin.
    by chews
  • The distinction you’ve drawn (the decision is made by the deterministic engine; the LLM merely explains it) matches something I’ve seen in another field. I was testing the agent’s memory: the schema of a vehicle changes mid-run, and the agent must make a call appropriate to the new schema at the end. Results: - A 20-line ‘the latest definition wins’ rule: 40/40 - Providing the entire history to the model: 38/40 - Retrieval-based memory: Mem0 (open-source) 3/15, TF-IDF + recency 1/40

    When presented with the correct information, the model used it without issue. What it couldn’t reliably do was decide which information was still valid.

    I’m curious about versioning on your end. When a rule changes, what happens to decisions made under the old rule? If someone asks six months later, “Why was this application rejected?”, does the explanation refer to the rule version at the time of the decision, or to the current one? Does the RAG side know that a policy passage has become invalid, or could it retrieve the old document to explain a new decision?

    by keparlak

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