Self-improving agents
Agents that author and refine their own capabilities — writing new agent-skills from experience and improving them through use, rather than waiting for a human to update them. The “growth” axis of the agent landscape: capability that compounds from the agent’s own runs.
Distinct from neighbours
- vs. durable-agents — durability is persisting state across time; self-improvement is changing the agent’s own skill/knowledge set over time. Often paired (you need persistence to accumulate improvements) but not the same.
- vs. agent-skills — skills are the format; this is the agent generating and editing them autonomously.
Instances
- hermes-agent — a “closed learning loop”: autonomously creates skills after complex tasks and refines them in use; agent-curated memory that builds “a deepening model of you.”
- adk — “self-generating meta-skills” (an agent that writes its own skills) adk-agents-with-skills.
- gstack — the Reflect step of its sprint codifies learnings for reuse.
- zouroboros — daily introspection cycles (a “Health Council”) audit capability gaps, prescribe fixes, and evolve procedures — but gate each procedure change through a three-model consensus vote, a concrete answer to the drift/quality risk below.
The bridge to research-wiki
This is compound-engineering (cross-wiki: “each unit of work makes the next easier”) moved inside the agent — and it echoes the gbrain (cross-wiki) thesis of an LLM accumulating a compounding personal knowledge base. The open risk is drift/quality: self-authored skills can encode mistakes at scale, so this leans on the same review/eval discipline as the agentic-coding-harness (no published longitudinal measure of whether self-improvement stays net- positive — an open question).
Related
hermes-agent · adk · gstack · agent-skills · durable-agents · compound-engineering