Automation Articles
Browse 723 articles about Automation.
OpenAI's Symphony Spec: How Using Linear as an Agent Control Plane Drove a 500% PR Increase
OpenAI's open-source Symphony spec uses a Linear board to orchestrate autonomous coding agents — and internal teams saw 500% more landed pull requests.
What Is Semantic Memory Search for AI Agents? Tools, Levels, and When to Use Each
Semantic memory search lets agents recall relevant context by meaning, not keyword. Learn the 6 levels of AI memory and which combination fits your use case.
How to Use Skill Systems in Claude Code: Chaining Skills Into Autonomous Pipelines
Skill systems chain modular Claude Code skills into scheduled, multi-step pipelines. Learn how to build content creation, repurposing, and research workflows.
Is Your Tech Stack Agent-Ready? The 5-Question Diagnostic for Evaluating Any Tool as Agent Infrastructure
Not every tool can serve as an agent control plane. Here's the 5-question diagnostic — state machines, ownership, audit history
What Is an Agentic OS? The System That Makes AI Tools Produce Consistent Results
An agentic OS tells your AI who you are, how you work, and how to execute complex tasks. Here's what it is and why you need one for reliable outputs.
What Is the Self-QA Loop? How AI Agents Critique Their Own Output Before You See It
A self-QA loop has an AI agent render, screenshot, and critique its own output before handing it to you. Here's how to implement it in your vertical agent.
What Is Structured Memory in AI Agents? How to Build Persistent Context
Structured memory lets AI agents reuse context across sessions without bloating the window. Learn how to build portable memory artifacts for your agents.
How to Build an Agent-Native Product: Lessons from OpenClaw, Hermes, and Codex
Agent-native products use outcome-based prompts instead of step-by-step instructions. Learn the design patterns behind the best agentic tools available today.
How to Build an Agentic Coding Workflow with Claude Code and Jira: A Full Walkthrough
Learn the complete agentic coding workflow: ideation, PRD creation, Jira ticket generation, PIV loop implementation, and system evolution using Claude Code.
What Is Agentic Context Grounding? The Pattern Behind Claude Design and Vertical AI Apps
Agentic context grounding reads a source of truth before generating anything. Learn the six patterns behind Claude Design that apply to any vertical AI agent.
What Is an AI Memory System? How to Build Persistent Context for Your Agents
AI models are stateless but your work isn't. Learn how to build a durable memory layer using SQLite, Postgres, embeddings, and MCP servers for your AI agents.
How to Build an AI Orchestrator That Delegates to Cheaper Sub-Agent Models
Use a frontier model as orchestrator and cheaper open-weight models for heavy lifting. This hybrid approach cuts costs while maintaining output quality.
How to Run Claude Code with Cheaper Models: OpenRouter, NVIDIA NIM, and Ollama
Use Claude Code's interface with DeepSeek, Gemma, and other affordable models via proxy. Get 80–90% of Opus quality at 2–5% of the cost.
DeepSeek V4 vs Claude Opus 4.7: Which Model Is Right for Your AI Workflows?
Compare DeepSeek V4 and Claude Opus 4.7 on benchmarks, pricing, context length, and agentic use cases to find the best model for your stack.
How to Use the Google Workspace MCP Server with Claude Code and Codex
Connect Gmail, Drive, Calendar, and Chat to your AI coding agents using the Google Workspace MCP server. Here's how to set it up and what you can automate.
Open-Weight AI Models Are Catching Up: What It Means for Enterprise Automation
Open-weight models like DeepSeek V4, Gemma 4, and Qwen are closing the gap with frontier models. Here's what that shift means for enterprise AI workflows.
What Is the PIV Loop? The Core Methodology for AI-Assisted Software Development
The PIV loop—Plan, Implement, Validate—is the repeatable process for handling individual coding tickets with AI agents. Here's how to apply it to any project.
What Is the Verifiability Principle? Why AI Excels at Code and Math but Struggles Elsewhere
AI automates what can be verified, not just what can be specified. Learn why verifiability drives AI capability and what it means for your automation strategy.
The 9 Components Every Production Agent Harness Needs (and What Breaks Without Each One)
From while-loops to lifecycle hooks: the exact nine components that separate a toy agent from a production harness, with failure modes for each.
Agent Harness vs Framework: What's the Difference and Which Do You Need?
Frameworks like LangChain require human assembly. Harnesses ship as working agents. Here's how to choose between them for your AI workflow.