AI assistance disclosure:
This post was written by me as an independent researcher. I used an LLM as an editing and structuring aid (e.g., to help organize sections and improve clarity), but the ideas, architecture, and arguments are my own.

Motivation and context

I’m an independent researcher thinking about agent architectures for long-lived, self-managing AI systems. In particular, I’ve been worried about how agentic systems change themselves over time, and how little structure most current proposals impose on that process.

A lot of discussion around “agents” focuses on behaviors (planning, tool use, memory, reflection loops). That’s useful, but it seems to leave a gap: what structural changes are allowed, and how are they constrained? If an agent is allowed to rewrite itself arbitrarily, or spawn internal processes without clear lifecycle rules, it’s hard to reason about stability, safety, or even long-term coherence.

In this post, I want to share an architecture-level blueprint I’ve been developing. The core idea is that self-modification should not be an unconstrained free-for-all. Instead, an agent should only be allowed to change its internal structure through a small set of explicit operations, each of which is resource-bounded and logged by an immutable auditing component.

I’m posting this here because LessWrong discussions around agent foundations, alignment, and long-horizon systemsseem like the right place to pressure-test whether this way of thinking is coherent, useful, or misguided.

The core idea (short version)

A self-managing agent should be allowed to restructure itself only through a minimal set of explicit lifecycle operations—specifically spawn, merge, and forget—with all such operations being:

resource-bounded,
logged in an append-only way,
and overseen by a structurally immutable auditing component.

The goal is not to “solve alignment,” but to make self-change legible, constrained, and inspectable by design.

Architecture overview

Rather than treating the agent as a single monolithic entity, this architecture treats it as a managed population (or graph) of sub-agents / modules, whose creation and deletion are first-class events.

The three structural operations

Spawn
Create a new sub-agent or module, typically to explore a hypothesis, handle a subtask, or run a bounded internal search process.

Merge
Combine two or more sub-agents into one, e.g. to consolid...

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