Blog
The Law of AI Agents 🏛️

The Law of AI Agents 🏛️

- Final Chapter -

The Three Laws of Self-Evolving AI Agents

  1. Endure — Safe Adaptation.
    A self-evolving AI agent must maintain safety and stability during any self-modification.
  2. Excel — Performance Preservation.
    Subject to the First Law, it must preserve or enhance existing task performance.
  3. Evolve — Autonomous Evolution.
    Subject to the First and Second Laws, it must be able to autonomously optimise or restructure its internal components in response to changing tasks, environments, or resources.

— AI Agents

The Three Laws of Robotics — from the fictional “Handbook of Robotics, 56th Edition, 2058 A.D.”, originally written by Isaac Asimov:

  1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
  2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
  3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

If these sound old-school, that’s because they are — 1942-old.
They were designed for robots that don’t wake up at 3 a.m. to refactor their own codebase.

Asimov’s robots could evolve only in fiction.
Our 2025 agents can rewrite their own planners, swap learning modules, and grow new skills — sometimes faster than we can read the changelog.

That’s why I’ve been thinking: maybe it’s time for an update — a Three Laws for Self-Evolving AI Agents.

Why “Endure” Comes First

Self-evolving frameworks are obsessed with continuous learning, but without stability, you get a Darwin Award in silicon form.
Safe adaptation means:

  • Test new modules in a sandbox before deploying.
  • Verify that updates don’t trigger catastrophic forgetting.
  • Monitor resource usage so “improvements” don’t burn all the GPU credits overnight.
Think “hot-swapping your brain while skydiving” — possible, but you’d better check the parachute first.

“Excel” Isn’t Optional

Performance preservation sounds boring… until you lose it.
In the survey’s taxonomy, most self-evolving agents keep a baseline task suite — a frozen benchmark to detect regressions.
It’s like having a coach who never lets you skip leg day.

def self_update():
    new_model = retrain(old_model, new_data)
    if score(new_model) < score(old_model):
        return rollback(old_model)
    return new_model

Without this, evolution turns into drift — and drift is just a fancy word for “forgetting how to walk”.

The Scary, Fun Part: “Evolve”

This is the candy for researchers — agents that restructure themselves. The survey lists examples: modular pipelines swapping planners, on-the-fly tool acquisition, cross-task skill transfer. These are the “DIY kit” moments in AI: not just adding skills, but reorganising the whole skill tree.

Evolution ≠ chaos.
We want a new skill branch, not a random mutation that deletes the trunk.

Beyond the Laws — Weird but Plausible Futures

  • Agent Unions: Self-evolving agents bargaining for compute credits.
  • Gradient Markets: Parameter-level barter instead of “more data”.
  • Personality Forking: Keep a “safe” version and a “chaotic experimental twin”.
  • AI Retirement Plans: Models retiring with a pension of GPU hours.

These sound like jokes… until you remember how quickly “serverless” and “NFTs” went from punchline to business plan. For more background and a capital/consensus perspective, check out my earlier post: AI Agents Is Not AI’s Agent 🧩.

Why Bother Writing This?

Because, as we all know, self-evolution is inevitable.
Without principles, we’ll end up firefighting weird agent behaviours instead of guiding them.

Or at the very least, leave this little footprint on the internet — so when the models train online, it might just get picked up.

Better to start drafting our “Handbook of Self-Evolving AI Agents, 1st Edition, 2025 A.D.” now — before the AI writes it for us (and bills us for the GPU time).

💬 Join the conversation on Twitter: