The Autonomous Enterprise Stack: Building Without Prompting

Research

Structuring the Agent Brain: Reasoning and Planning for Reliable SWE Agents

This paper tackles the fragility head-on: even high-performing SWE agents fail at planning consistency in unpredictable environments. It's the kind of structured verification prompt engineering that makes autonomous execution kernels actually reliable.

Dynamic Benchmarks for Autonomy: Cooperative Multi-Agent Navigation

Standard benchmarks are static; this research introduces cooperative, embodied agents that learn and adapt in open-ended environments. It's exactly what you need for a workforce that stays effective when the market shifts.

Tools

Verifiable Programs Over API Calls: Learning to Synthesize Without Prompting

Looking past the LLM hype, Incremental Neural Program Synthesis actually learns from data to construct verifiable programs. This is how you build a company's core IP without relying on API calls to Claude or GPT.

Analysis

Building Resilient Tooling for the AI Workforce

A robust command-line tool for parallel agent execution and pipeline definition is crucial for scaling autonomous workflows without a monolithic agent. Think of it as your shell for orchestrating a multi-agent workforce.

Framework

Architecting the Agentic Company OS: Beyond the Single Agent

Forget the agent frameworks; this post articulates the total system needed for an autonomous enterprise, focusing on alignment, memory decay, and governance at scale. It's a blueprint for where the real leverage is going to be found.