self-improving-systems

Self-Improving Systems

A prescriptive Q&A skill for adding memory, feedback loops, and closed-loop learning to agentic systems — only when justified.

Headline message: most agents shouldn't have persistent memory.

Memory is a liability surface (drift, poisoning, debugging difficulty, GDPR/HIPAA exposure). Persistent memory is the second move, not the first. The skill's job is to filter ruthlessly so the user doesn't ship a mem0/Letta build for a problem that a 200-line conversation summary would solve.

The first 2 stages of the Q&A flow exist to stop most users from over-engineering. By the end of stage 2, ~60% of users will discover they want a state cache (or stateless RAG), not memory + learning. That's the win.

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Quick Start

User just asks:

"Add memory to my agent"
"My agent keeps forgetting things — give it context management"
"Make my marketing agent learn from past campaigns"
"Should I use mem0 or Letta?"
"How do I set up closed-loop learning for my finance agent?"
"Build a self-improving HAZOP system"

Skill response (every time, in this order):

1. Stop. Apply the cache-vs-learning frame (Stage 1).
2. Run the 6-question need-memory rubric (Stage 2). <4 yes → exit the skill, recommend stateless + RAG.
3. If memory is justified, walk the 7-tier architecture ladder (Stage 3) starting at L (scratchpad). Escalate only when forced by a concrete justification.
4. Force the user to design a feedback signal (Stage 4). No signal = state cache, full stop.
5. Wire the closed loop with explicit human gates (Stage 5).
6. Build the eval harness (Stage 6) — golden set, regression, drift alarms.
7. Walk the 8-risk checklist (Stage 7).
8. Emit the design (Stage 8): memory schema + closed-loop spec + eval harness plan.

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Critical Rules

1. Default position: scratchpad-only

Ship a stateless agent first. Add a scratchpad (Reflexion-style verbal self-correction) within a single run. Discard it after. This already gets you most of the gain on most tasks. Anything more must be earned.