Self-improving AI agents: continuous learning for automations that adapt to your feedback

A self-improving AI agent learns from feedback, so a mistake you corrected once stays corrected. The loop has four steps: the agent runs, the result is checked, you correct what missed, and the correction is kept for the next run. Done well, an automation adapts continuously to what you want, and every change stays visible and reversible.

What is a self-improving AI agent?

An agent that changes how it works based on how its past work was judged. The research version of this idea is Reflexion (Shinn et al., 2023): agents "verbally reflect on task feedback signals" and "maintain their own reflective text in an episodic memory buffer." With that loop, the paper reports 91% pass@1 on the HumanEval coding benchmark.

For an automation at work, the feedback comes from two places: a check against what done means, and the person who reads the result. Self-improvement is the habit of turning both into something that lasts.

How does an AI agent feedback loop work?

Step	What happens	What it needs
Run	The agent does the job	A clear brief
Check	The result is held to a definition of done	A checklist of facts a reviewer can verify
Correct	The gaps go back, from the check and from you	A way to comment on the actual result
Remember	The correction is kept where the next run will find it	A memory scoped to that agent and job, or a change to the job itself

Skip any step and the loop breaks. An agent with no check cannot tell a good run from a bad one. A correction with no memory has to be given again next week.

Why do automations need to keep adapting?

Because the world they run in keeps changing:

• Sources change. A site redesigns its page, an API adds a field, a report moves.
• Preferences sharpen. You see the first ten runs and realise what you actually want.
• The job grows. A weekly summary becomes a summary plus three recommendations.

An automation that cannot take these changes in plain words gets rebuilt or abandoned. In Stack Overflow's 2025 developer survey, the biggest single frustration with AI tools, cited by 66% of developers, was "AI solutions that are almost right, but not quite." Almost right is what an automation looks like before its feedback loop has run a few times.

Where does self-improvement go wrong?

Failure	What happens	The guard
Learning the wrong lesson	One odd correction becomes a rule for everything	Scope each correction to the agent and job it was given for
Rules that contradict	Two notes say opposite things, and the agent picks one at random	Merge a new correction into the one it repeats
Silent drift	The automation changes and nobody knows when or why	Save every change as a version, with a note on what changed
No way back	A change makes things worse and the old version is gone	Keep every version, and restore with one step
Judging too early	A change looks better after two runs by luck	Compare versions only after enough runs, such as 10 each

The common thread: self-improvement should be visible. An agent that quietly rewrites its own rules is hard to trust; one that proposes a change you can read, accept and roll back is easy to.

How do you update an automation without breaking it?

1. Describe the change in plain words. "Start each line with the post's title" is enough.
2. Make it a new version. Keep the old one, so you can compare and go back.
3. Add a check for it. If you never want to correct it again, add it as a checklist line.
4. Watch the pass rate. Let the new version run before judging it.
5. Restore if it got worse. Roll back, then try a different change.

How does Kavela keep automations learning?

In Kavela, the feedback loop is built into how work runs, and every step of it stays in your hands.

• Every run is checked. A second agent checks each workflow run against your checklist, line by line, and sends the gaps back. A checklist line that fails in two runs of the same version makes Kav propose a change to the workflow.
• Your feedback is remembered, in scope. Your approvals, your rejections and the reasons you give are kept as memory for the agent and job they were given for, and load when a similar job comes up. A correction for one audience never quietly becomes a rule for all your writing. A new memory that repeats an old one updates it, so rules don't pile up and contradict.
• Repeated corrections become lessons. When Kav sees the same correction more than once, it teaches the agent: a lesson it recalls on similar work, or a standing rule it follows on every job.
• Changes are plain words and versions. Tell Kav what to change, or edit the workflow's words directly. Each change is a new version with its own pass rate, Kavela compares two versions once each has 10 checked runs, and any earlier version can be restored.
• Nothing changes behind your back. A change Kav suggests on its own, or one an unattended run wants to make, comes to you as a proposal to accept or dismiss, unless you set the workspace to Autonomous.
• Comments go straight back. A comment on a result wakes the agent with your note, so it can try again while the context is fresh.

Proactive AI agents covers where this loop fits among the other things a proactive agent needs, and AI agents vs workflows covers why a workflow is the thing that keeps a correction.

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