airoweb blog
Posts about the messy middle of AI adoption: workflows, tools, MCPs, review habits, data boundaries, and the small decisions that make or break automation inside a team.
Latest posts
New essays, checklists, and technical explainers on AI workflows, agents, MCPs, and the review habits that keep automation usable.
All posts8 min read
AI infrastructure
A useful SOUL.md is not a place to store every instruction. It should define durable identity, voice, values, and hard boundaries, while project rules and repeatable workflows live in more specific files.
7 min read
AI infrastructure
An MCP server that reaches company systems needs the same operational discipline as an internal API: ownership, versioning, access control, telemetry, and retirement criteria.
7 min read
AI infrastructure
Multica skills are most useful when they encode reviewed operating knowledge for repeatable agent work, not when they become a dumping ground for every prompt fragment.
6 min read
AI infrastructure
Before connecting an agent to tools, classify each action by data access, reversibility, business impact, and the approval step required before it runs.
3 min read
Company adoption
A simple pilot review helps teams decide which AI experiments deserve support, which need limits, and which should stop.
6 min read
AI infrastructure
Fable 5's launch, suspension, and capped return show why teams should route frontier models through fallback, review, access, and budget controls.
Regular themes
The aim is not to cover every new model release. It is to explain what changes in the work when AI becomes part of the process.
Adoption notes
Operating models, review gates, ownership, and the practical details that decide whether tools stick.
Workflow checklists
Short reviews for recurring AI-assisted work before it becomes normal business process.
Technical explainers
MCPs, agents, data access, logs, permissions, and integration choices in plain language.
01
Useful before broad
02
Specific before generic
03
Human review before scale
The best AI workflow is not the one with the most automation. It is the one where ownership, data access, review, and rollback are clear before people depend on it.
Operators
Teams turning experiments into repeatable processes.
Builders
People wiring tools, prompts, context, and approvals together.
Leaders
Owners who need a plain-English view of what changes in the work.
