automation
5 notes tagged automation in the AI agents topic. How agents are built, tuned and run: loops, harnesses, MCP, skills and coding agents, with what broke and what we measured.
- Owner agents, plain functions: where the model sits in our ops estate
Give each domain one owner agent that reports back, keep the steps as plain functions, and add autonomy to a running platform one gated decision at a time.
- AI agent explained: LLMs, workflows and agents
Most people confuse LLMs, workflows, and agents, but understanding these three levels unlocks the real potential of AI automation. The key difference lies in who makes…
- Design tokens in the AI era
Design tokens create consistency across platforms by centralizing visual attributes. AI tools now automate token creation and management, making design systems…
- AI knowledge automation
Exploring the motivations behind the MCP Playbook, its role in establishing a dynamic runbook, and new patterns for capturing AI-generated knowledge like chat logs and…
- Your AI approach
Most people struggle with AI tool paralysis and prompt fatigue, but building an AI-native workflow comes down to three focused strategies. The key is systems over tools.