Stop Paying Rent on Agent Mistakes: A Lifecycle Memory System
madsthines · reddit · 2026-08-03
The author argues that "self-healing" in agents is essentially a memory loop you forgot to build. If an agent hits a silent error (like an empty array from RLS filtering) and draws the wrong conclusion, it will repeat the mistake in future sessions, continuously wasting compute.
The post shares a lightweight solution requiring no fine-tuning or embeddings, built on three verbs mapped to existing agent lifecycle hooks:
- Read: Load relevant past lessons on SessionStart.
- Fail: When a tool call errors, inject matching lessons before retrying.
- Write: Write a short retrospective on Stop.
To prevent the agent from entrenching false conclusions (a "superstition with a database"), the system includes strict guardrails: lessons are advisory only, promotion requires recurrence (seencount >= 3), everything expires in 90 days, contradictions are surfaced rather than overwritten, and a privacy pre-flight drops secrets. The matcher deliberately uses literal substring matching for deterministic, zero-dependency, and zero-inference-cost behavior.
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