A software factory built from agent skills
AI coding agents can already write functions, modify repositories, run tests, and open pull requests. The harder problem is getting an agent to follow a repeatable engineering process before, during, and after the code is written.
No reject state. Here's the workaround.
An agent that proposed “move persistence into a shared library” six months ago will happily propose it again unless something durable says it was already rejected – and OpenSpec has no built-in state for that today.
Specs as deltas, not a 40-page PRD.
OpenSpec is a free, open-source CLI from Fission AI that gets you and your coding agent to agree on a change in plain Markdown before any code gets written, without the phase-gated ceremony of heavier spec-driven frameworks.
Skill, MCP server, or both?
Agent Skills and MCP servers are often presented as competing ways to extend an AI agent. That framing is wrong: a skill teaches the agent how to work, while an MCP server gives it governed access to live capabilities.
OpenCode from the command line, in practice
OpenCode’s command-line interface is built for scripting, CI pipelines, and unattended agent runs. This article is a practical guide to using it in daily work.
Enforced SDD skills, installed in one command.
Superpowers packages a full spec-driven methodology into installable Claude Skills, enforcing brainstorming, planning, subagent-driven implementation, and strict TDD instead of leaving that structure up to you.
A small coding agent that expects you to shape it.
Pi Coding Agent is a minimal, open-source terminal coding harness that ships with four default tools and leaves most of the behaviour to extensions, skills and your own workflow.
Delegate the noisy work, keep your context clean.
Most Claude Code sessions get slow and cluttered for the same reason: every exploratory grep, every log dump, and every “let me check one more file” stays in the main conversation forever.
Stop AI agents drifting from specs, tests, and code.
AI coding agents ship features fast, but specs, tests, and code quietly drift apart. This guide covers a traceability model, spec-to-test and spec-to-code mapping, and the CI checks that catch drift before a merge.
Process depth vs portability, not best tool.
Developers comparing Spec-Driven Development setups in 2026 are usually not asking which model is smartest. They are asking which workflow will keep an AI agent aligned without burying them in ceremony.
Five phases from intent to verified code.
Spec-Driven Development works when the specification is a workflow, not a document you file away after kickoff. The point is not to produce a large product requirements document.
Long-running A2A tasks outlive chat sessions.
Most AI agent demos still behave like chat completions with extra steps: you send a prompt, wait a few seconds, and get an answer back in one response.
Faster LLM inference without quality loss - a practical guide
A 70B model generates one token per forward pass, and each pass reloads weights from VRAM, computes attention across the context, and synchronizes memory. Between tokens, the GPU sits idle while it waits for sequential dependencies to resolve.
40% of multi-agent pilots fail. Here's how to pick the right orchestration pattern - and avoid the ones that break.
Single-agent AI systems peaked in 2025 — you gave one LLM a prompt, some tools, and a goal, and it did reasonably well on bounded tasks.
The spec as source of truth, not a side document.
Spec-Driven Development is one of those ideas that software engineers have reached for before and then set aside when the effort stopped paying.
Specs as source of truth, or slow ceremony?
Spec-Driven Development entered 2026 as the serious developer’s answer to vibe coding drift.
