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Agentes LLM: pruebas de email con límites claros

Los agentes LLM pueden ayudar a investigar por qué falla una prueba de email, pero hay una diferencia importante entre asistir una ejecución y controlar una ejecución. Si el modelo puede hacer cualquier cosa, el resultado será dificil de reproducir: una corrida pasa, la siguiente usa otro buzón, y el equipo termina discutiendo con una explicación bonita en lugar de revisar evidencia. La solución…

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Attach an API spec to a Jira workflow validator: what the CogniRunner AI rule never reads

Attach an API spec to a Jira workflow validator: what the CogniRunner AI rule never reads Key takeaways END STATE: a validator that reads your own schema before deciding, with the verdict provably…

  • Attach JSON schema, API spec, or field-mapping table to Jira workflow validator via CogniRunner AI.
  • Confirm verdict changes by running input with and without added document.
  • Prompt ceiling limits substring to 30,000 characters, cutting mid-string.

span-01 vs mercury-decide: same score, opposite failures

span-01 vs mercury-decide: same score, opposite failures Last time I tested a "decision model" — a model that takes a plain-language question about a text and answers with a probability — as a gate…

  • Both span-01 and mercury-decide scored equally (F1 0.93) in English word detection test.
  • Span-01 provided consistent probabilities, mercury-decide varied based on instruction clarity.
  • Span-01 showed stability over days, mercury-decide exhibited daily score fluctuations.

Give a Jira workflow rule a memory that survives, without blowing the prompt budget

Give a Jira workflow rule a memory that survives, without blowing the prompt budget Key takeaways END STATE: a workflow rule that accumulates lessons about YOUR instance across runs, injected under a…

  • Stand up a local harness to run the module without Forge
  • Save lessons with unique Jira instance details
  • Measure Jaccard similarity to avoid merging similar lessons

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