“Self-improving AI” is usually marketing. Hermes makes it practical: it learns the things that actually change future work.
Quick answer#
Hermes is self-improving because every serious task can leave behind better operating context: a durable memory, a corrected preference, a reusable skill, or a searchable session trail. It does not need to secretly retrain a model after every chat. Instead, it turns repeated human steering into structured context that future runs can use before acting. The strongest loop is simple: do the task, verify it, save the durable lesson, and update or create a Hermes skill when the workflow is repeatable.
What self-improvement means in Hermes#
Hermes improves at the agent layer. That means it gets better at choosing the right workflow, loading the right skill, respecting project conventions, and avoiding mistakes the user already corrected.
This is different from model fine-tuning. The base model may still be Claude, GPT, a local model, or a routed provider. Hermes improves by wrapping that model in memory, tools, skills, session recall, cron, Kanban, and verification habits.
The improvement loop#
A good Hermes run creates a feedback loop:
- Understand the task and inspect the real environment.
- Act with tools instead of guessing.
- Verify the result with tests, browser checks, or command output.
- Save durable facts as memory.
- Convert reusable procedures into skills.
- Load those skills automatically next time.
That is why persistent memory, the Hermes memory system, and the skills hub belong in the same operating model.
Example: a repeated deployment workflow#
Suppose Hermes learns that a project deploys by pushing to GitHub, not by calling a hosting CLI directly. The next time the user asks for a landing-page fix, Hermes should inspect the repo, edit the right file, run lint and build, commit, and push. The user does not need to repeat the deployment preference every session.
If the deployment has a tricky checklist, that becomes a skill. The skill can include commands, environment paths, known failures, and verification steps. The memory stores the durable project preference; the skill stores the procedure.
Where reinforcement learning fits#
The Hermes Agent reinforcement learning article covers the Atropos RL angle. That is useful research and long-term capability work, but most users do not need RL to benefit from self-improvement today.
For day-to-day work, the reliable path is structured memory plus skills. You can get compounding returns from one week of real usage without training a model.
What to save and what not to save#
Save facts that stay useful:
- User communication preferences.
- Stable environment paths.
- Project deployment conventions.
- Tool quirks and integration gotchas.
- Corrections the user should not repeat.
Do not save temporary task progress, PR numbers, stale run logs, raw secrets, or anything that will expire quickly. If the lesson is a procedure, make it a skill instead.
How adaptive reasoning helps#
Self-improvement is not only memory. Hermes can also improve execution quality by choosing how much reasoning to spend. A small formatting edit should not consume the same budget as a multi-repo debugging task. The adaptive reasoning effort guide explains how Hermes can control thinking depth and token use.
Failure modes#
Self-improving agents fail when they save too much, save the wrong thing, or never verify the outcome. Watch for these symptoms:
- The agent cites stale facts after a project changed.
- Skills contain commands that no longer work.
- Memory stores temporary run logs instead of stable preferences.
- The agent claims success without testing.
Fix the loop by removing stale memories, patching skills immediately, and requiring verification before completion.
How to start#
Install Hermes with the setup guide. Run one real task. After it succeeds, save exactly one durable preference and one reusable skill if the workflow is likely to recur. Then, a week later, ask Hermes to do the same class of task and check whether it needs less steering.
That is self-improvement you can actually measure.
Install path#
To test self-improvement, follow install Hermes Agent and run a workflow you expect to repeat. After the first successful run, save one durable memory, create or patch one skill, then repeat the task later. The benchmark is not a synthetic score; it is whether Hermes needs less steering and fewer corrections. The same loop works for code review, research reports, launch checklists, and messaging automations.
A good first experiment is a weekly report. Run it manually, verify sources, create a skill with the exact checklist, and only then schedule it. If the report improves on the second run because Hermes remembered your preferred format and loaded the checklist automatically, the self-improvement loop is working.