What Are Self-Improving AI Agents?
Two very different meanings, and the one that actually ships in production.
A self-improving agent is one that gets better over time from its own experience. The term covers two very different things: genuine autonomous self-improvement, where an agent modifies its own prompts, tools or policies without human involvement; and the practical, human-governed improvement loop, where evaluations and feedback drive the changes. The second is what production teams actually run today, and it is far safer.
Autonomous self-improvement vs the eval-driven improvement loop
Autonomous self-improvement is the research-flavoured version: an agent rewrites its own instructions or builds its own tools to get better, unattended. The eval-driven loop is the production version: the agent's failures are captured, scored against a dataset, and used to drive prompt, tool or model changes that a team ships through evals. Both make the agent better over time; only one does it with a human and a measurement in the loop.
Is autonomous self-improvement safe in production?
Largely not yet. An agent that changes its own behaviour unattended can optimise for the wrong thing, drift outside policy, or degrade in ways nobody notices until a customer does, and with no version history or eval gate, there is nothing to catch or roll back. The safer pattern keeps improvement governed: the agent surfaces what to fix, but changes pass through evaluation and human review before they take effect. That is self-improvement you can actually trust.
How do you build a (safely) self-improving agent today?
Run the eval-driven loop with a human in the loop. Capture production failures, turn them into eval cases, let the system (or a person) propose fixes, gate those fixes on the eval suite, and ship only what passes. Add human review on the consequential changes. The agent improves continuously from real experience, the defining promise of a self-improving agent, but every improvement is measured and reversible rather than autonomous and opaque.
Where Prefactor fits in the improvement loop
Prefactor runs the evaluation side of this loop. It records every run of your agents, and judges score each completed run against criteria drawn from your company's own knowledge and context, so the team can see where the agent needs to improve. When an agent reports a changed prompt or configuration, Prefactor records a new version, so later verdicts show whether the change helped. Suggesting improvements to the agent directly, Active Agent Improvement, is Coming Soon.
Build an eval-driven improvement loop with Prefactor
Prefactor evaluates agents using your company's own knowledge of what good looks like, so you know they're doing the job and where they need to improve. It records every run, judges score each one against the criteria your team sets, and runtime policies act on what they find.
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