How to build proactive agents & self-improving company (Fully explained)

AI JasonJune 2, 202611m

Topics14

Self-Improving Companies and AI-Native Business Operations0:00 Evolution from Human-Centric to AI-Native Workflows1:00 Closed-Loop vs Open-Loop Control Systems1:30 Getting Started with Memory Layers2:30 Memory Layer Architecture3:00 HubSpot Free AEO Creator4:00 Cron Jobs and Closed-Loop Formation5:00 Autonomous Ads Experiment Results6:00 Memory Types and Open-Source Solutions7:00 Gibbering Memory System8:00 Loop Me for Long-Cycle Tasks8:30 Cron Job Functionality for Learning Extraction9:30 Data Access Skills and CRI Challenges10:00 Printing Press for Agent-Native CRI Design10:30

In a Nutshell

Agents are now running closed-loop systems that capture performance data, extract learnings, and autonomously update their own strategies and skills without human oversight. Companies achieve this by combining memory layers (temporal logs + procedural skills), cron-scheduled review cycles, and agent-native CRIs that let agents access and act on data efficiently. Early results show 3-5× gains in revenue per employee and ad performance when these feedback loops are in place.

AI-Generated Notes

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Y Combinator ran a session on self-improving companies where companies in the current batch are hitting 5x more revenue per employee compared with 18 months ago. Their agents have been handling all the internal ops and write 45 of its own tools all autonomously. A company called Porsche already sold $30 to just build and run the whole company from scratch. All of this points to a new AI-native way of running your business.

The team has been experimenting with this AI-native way of running a company for the past few weeks and encapsulated their learnings and best practices into an open-source agent skill for long horizon work and self-iterating tasks alongside many other useful tools.

Before AI, humans have been the glue to use multiple steps to get a certain outcome. A human is the one that prioritizes and decides what kind of things to do. With the recent AI boom, most have translated to AI-enhanced workflows where you talk to an agent or AI workflow that is completing a task end-to-end. However, there's no feedback loop back to a system to inform improvement they should do. A human still remains the main driver about prioritization, plan, and trigger tasks.

What's really powerful is the real AI-native loop where agents can take an input or trigger about a certain goal, do certain tasks, but most importantly actually capture the feedback to learn what's working, what's not working, plan next steps to make sure next time it is doing things better.

Diana from YC explains this in a control system setup comparing closed-loop versus open-loop where there's no feedback back to the system versus a closed-loop where status, decisions, and outcomes are continuously captured and fed back into the intelligence layer.

There are five core elements for each AI loop: how the data actually ingests into a system, the policy layer which is like a contract about the workflow and SOP, two layers that allow agents to access different systems, write quality gates in the workflow where either human or AI evaluator can go through the output quality, and lastly some sort of mechanism that can bring those learnings back to the system so that it can improve its operations.

For each AI loop, start by setting up a memory layer or environment so the agent can keep a system or record of the tasks and outcomes, as well as different skills and current jobs for the agent to continuously execute and monitor the results. Depending on the type of task, there might be different cadences and skills.

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How to build proactive agents & self-improving company (Fully explained)

Self-Improving Companies and AI-Native Business Operations00:00

Evolution from Human-Centric to AI-Native Workflows01:00

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