Agentic workflows

How to Build a Self-Improving Company with AI

How to Build a Self-Improving Company with AI

YC General Partner Tom Blomfield explains how to move beyond the 'copilot' mindset and restructure companies as series of recursive, self-improving AI loops. He details how to make company knowledge legible to AI, creating systems that improve overnight with minimal human intervention, ultimately rendering traditional middle management obsolete.

CI/CD Is Dead, Agents Need Continuous Compute and Computers — Hugo Santos and Madison Faulkner

CI/CD Is Dead, Agents Need Continuous Compute and Computers — Hugo Santos and Madison Faulkner

Madison Faulkner and Hugo Santos explain why traditional CI/CD, built for human developers, is failing under the load of AI agents. They propose a new paradigm of 'Continuous Compute' centered on intent-driven agent loops, fast inline validation, and a pre-merge layer where humans review outcomes, not diffs, paving the way for a 'multiverse' of parallel development.

Building your own software factory — Eric Zakariasson, Cursor

Building your own software factory — Eric Zakariasson, Cursor

Eric Zakariasson from Cursor explains the shift from single-agent pair programming to managing a multi-agent "software factory". He outlines the practical steps required, from establishing a well-structured codebase with guardrails to adopting a managerial mindset that focuses on automation, asynchronous work, and scaling agent fleets to increase software development throughput and consistency.

How To Build A Company With AI From The Ground Up

How To Build A Company With AI From The Ground Up

Y Combinator Partner Diana Hu explains how to build an AI-native company where AI is the core operating system, not just a tool. She covers how to make a company queryable, the impact on team structures, and why startups have a massive edge in this new paradigm.

Full Workshop: Build Your Own Deep Research Agents - Louis-François Bouchard, Paul Iusztin, Samridhi

Full Workshop: Build Your Own Deep Research Agents - Louis-François Bouchard, Paul Iusztin, Samridhi

This hands-on workshop details the construction of a sophisticated, dual-part AI system for producing high-quality technical content. It begins with an MCP-powered deep research agent that autonomously plans, searches the web, and analyzes sources like YouTube to synthesize a grounded research artifact. The second part is a constrained, deterministic writing workflow that transforms this research into polished, non-sloppy content using an innovative "Evaluator-Optimizer" pattern for iterative refinement. The session emphasizes crucial AI engineering principles, such as choosing between agentic and workflow-based architectures, and concludes with a deep dive into implementing practical observability and evaluation pipelines to ensure the system is both measurable and improvable.

Paperclip: Open Source Human Control Plane for AI Labor — Dotta Bippa

Paperclip: Open Source Human Control Plane for AI Labor — Dotta Bippa

Dotta, the creator of Paperclip, introduces it as an open-source orchestrator for building "zero-human companies." This talk demonstrates how to set up an organization of AI agents, leverage skills and custom instructions for reliable work, and automate business processes. Through a live demo, Dotta showcases creating a company from scratch, managing agent workflows with QA and routines, and outlines the exciting future roadmap for the platform.