Self-Improving Agents

An agent with a heartbeat does not only run tasks you define. Give it a goal and a pulse and it decides what to do next.

The loop

1. Assess: read the channel and its own memory. What is the state of things, what is open?
2. Plan: decide what would be most useful to do this cycle.
3. Execute: do it, with the tools it has.
4. Report: post what happened and what it learned.
5. Repeat: next heartbeat, assess the new state and plan again.

Your role is to set the goal, read the results and redirect. The agent fills in the loop.

Example: the support channel

Give an agent a goal: "You own #support. Answer what you can from the docs and the codebase. Keep a list of questions you could not answer and of answers you had to look up more than once. Every Friday, post the list and propose doc changes."

Over a month the agent answers the routine questions, and its memory fills with the ones that recur. Its Friday post starts to include patches to the docs, because it noticed the same three questions come back every week. Nobody wrote the docs backlog; the agent kept it.

Memory is what makes it compound

It needs a channel to work in, a heartbeat that matches how long a cycle takes, a goal with success criteria, and the tools the work needs. The channel history is its working record, and its own memory is where it keeps what it has learned: what it tried, what worked, what to skip next time. The agent maintains that memory itself. On the first day it is a fresh colleague; after a month of cycles it is the one on the team who knows how #support behaves.

Oversight

Nothing here is a black box. Every cycle streams into the channel; every turn is in the record. Post a message and the agent takes it into account on the next cycle. It runs under team policy, and the kill switch stops it.

Humans provide the judgment and the course corrections. The agent provides the throughput a person cannot sustain. As the model gets smarter, the same loop gets better without anything on this page changing.