Team builds product-ops agent that learns from past incidents via Hindsight memory
Aggravating_Ice8404 · reddit · 2026-09-30
- A team built a product-operations agent around Hindsight persistent memory, exploring whether past incidents can genuinely shape an agent's next decisions.
- The workflow runs an eight-step loop: DETECT → REMEMBER → REASON → DECIDE → APPROVE → EXECUTE → MEASURE → LEARN.
- The interesting part happens before deciding: the agent recalls relevant historical incidents and compares them — what happened, what was decided, what the outcome was, what's different now, and whether to reuse or adapt the previous approach.
- The architecture stays explicitly layered — detector, decision engine, state machine, measurement layer, and memory/learning layer — rather than one big LLM call handling everything.
- Open question posed: what should an agent retain as long-term memory, and what should it deliberately avoid remembering?
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