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.” hermes-learn-skill-capture makes the skill-authoring side concrete and names an autonomy spectrum: hand-write (you) → /learn (agent authors a SKILL.md from a doc/URL/past conversation you point it at) → skill_manage auto (the agent captures its own procedural memory after a 5+ tool-call workflow, unprompted) → Hub install (community). A write_approval gate can route agent-written skills to a review queue — though it’s off by default (the agent writes skills independently), which is exactly the drift/quality knob the open question below turns on.
• 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.
• autoharness — moves up from authoring one skill to managing the whole library: distils Claude Code sessions into skills, consolidates similar ones instead of accumulating duplicates, and archives by invocation rate. Its drift control is scoping (it only edits skills it authored) + pre-deploy validation, with real usage/adherence as the feedback signal — a fourth design point on the drift question alongside zouroboros’s vote, autonovel‘s LLM-judge, and autoresearch‘s objective metric.

The academic frame

The vendor READMEs above now have a survey behind them. self-evolving-agents-survey (Fang et al., arXiv:2508.07407) defines self-evolving agents as those that “automatically enhance their capabilities through interaction data and environmental feedback, rather than relying on static manually-crafted configurations” — bridging “the static capabilities of foundation models with the continuous adaptability required by lifelong agentic systems.” Its useful move is to refuse a single definition: it models the field as a feedback loop over four parts (System Inputs, the Agent System, the Environment, and Optimizers), and notes that different techniques evolve different components. So “self-improvement” is a family scoped by what changes — a config, a skill, a tool set, a policy — not one mechanism. It also treats evaluation, safety, and ethics as central to reliability, which lifts the drift risk below from a hunch to a named research problem.

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 · hermes-learn-skill-capture · adk · gstack · agent-skills · durable-agents · compound-engineering · self-evolving-agents-survey · loop-engineering