proomt

Search

Search posts, papers, and topics

All posts

Hacker News front pagemanagement (221)3 min readintermediate

Trying the Software Factory Pattern

Read on lethain.comHN 87·45 comments

Summary

The post describes an experiment implementing the software‑factory pattern: an AI‑driven loop that audits a Linear project, syncs goals from Notion, metrics from Datadog/Snowflake, creates and updates issues, and executes non‑blocked tasks. It shows how tying together a unified task tracker, observability data, and an orchestrated agent harness can keep projects aligned without manual state hoard…

  • The factory loop consists of goal audit, metric review, issue creation/update, task execution, and repeat when goals change.
  • A single source of truth (Linear) combined with observable metrics (Datadog, Snowflake) enables agents to evaluate progress autonomously.
  • Orchestrated harnesses let agents act independently of a developer’s laptop, scaling the pattern across the org.
  • Missing infrastructure (e.g., metric dashboards or unified task tracking) breaks the loop, highlighting migration dependencies.

Teams building AI‑augmented development pipelines need a disciplined loop to keep goals, metrics, and work items in sync.

6/10
devopsobservabilityaiagenticproject management

Related reading

  1. Towards Self-Driving Codebases

    The post argues that AI agents could eventually handle low‑level engineering tasks—bug fixing, debugging, UI consistency, growth experiments—if the dev toolchain is made “agent‑legible”. It outlines missing primitives (global memory, code‑base rot prevention, better dev environments) and proposes a bootstrapping process to measure and improve a repo’s “agent readiness”. The piece is largely specu…

    Hacker News front pagedetail.dev9 minHN12099

  2. Use Curiosity, Craft, and Care to Decide What AI Should Write

    The post proposes a three‑principle framework—Curiosity, Craft, and Care—to decide how much AI should author each artifact in a software development workflow. It argues that AI can be used aggressively for exploratory, disposable outputs (Curiosity) but should be limited for artifacts that commit the team to decisions (Craft) and for communications that require personal ownership (Care). The auth…

    Atomic Objectatomicobject.com4 min

  3. Cloudflare Introduces the Agent Development Lifecycle to Replace Traditional SDLC

    Cloudflare’s Agent Development Lifecycle (ADLC) replaces the classic SDLC with an autonomous, event‑driven workflow system that lets AI agents handle code generation, testing, deployment, and maintenance. The platform builds on Cloudflare Workflows and the new @cloudflare/ci library to spin up containers, run headless browsers, and chain steps with caching and credential support. Observability is…

    InfoQinfoq.com2 min

  5. Modernizing the Trade Lifecycle With Governed Data and AI

    Databricks argues that modernizing the trade lifecycle now hinges on building a governed, real‑time data foundation that spans research, trading, risk, ops and compliance, rather than isolated AI pilots. Starting with a few high‑value questions—execution cost, shock risk, exception rates—and using Unity Catalog and Agent Bricks lets firms achieve measurable speed and auditability gains before sca…

    Databricksdatabricks.com5 min

  6. How Data 360 Builds Trusted Context: The Enduring Layer for Enterprise AI

    Salesforce’s Data 360 provides a shared runtime that assembles the minimal, authorized slice of enterprise data (“Trusted Context”) for each AI‑agent turn. A six‑stage Agent Context Engine (Resolve, Plan, Reconcile, Govern, Compile, Learn) pulls data from structured, unstructured, and streaming sources across Salesforce, Snowflake, Databricks, etc., applies fine‑grained policy, and returns a toke…

    Salesforce Engineeringsalesforce.com11 min