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airoweb blog

Notes on making AI useful at work.

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

Recent notes.

New essays, checklists, and technical explainers on AI workflows, agents, MCPs, and the review habits that keep automation usable.

8 min read

Company adoption

Ask workers where the AI moved the work

An AI rollout can remove drafting while adding review, exception handling, surveillance, and accountability. A work-delta review makes that transfer visible before launch.

adoptionoperating modelworker consultationworkflow designgovernance

9 min read

AI infrastructure

The agent updated the record. The notification never left.

Two ordinary writes create a crash gap between an agent's decision and its external effect. Commit the decision and an outbox event together, then let a conventional relay handle delivery.

AI infrastructureagentsreliabilityevent-driven architecture

7 min read

Company adoption

AI governance needs decision rights, not another committee

A steering committee cannot own the daily decisions inside every AI-assisted process. Define who owns the governance system, the workflow, the platform, specialist review, and residual risk.

adoptiongovernanceoperating modelaccountability

9 min read

AI infrastructure

A running flag is not a lock for your agent queue

A task marked running can stay stuck after a worker dies, then collide with a replacement if the old worker returns. Expiring leases make ownership recoverable; guarded writes keep stale workers from acting.

AI infrastructureagentsqueuesreliability

9 min read

Company adoption

Stop measuring AI literacy by course completion

A completion record says someone received training. A useful AI literacy programme shows that they can recognize the limits, data boundaries, review duties, and escalation points of the AI-assisted work they actually perform.

adoptionAI literacygovernancetraining

7 min read

AI infrastructure

Valid JSON is not permission to act

A schema can make an agent's output parseable without making the requested action correct or permitted. Put a conventional policy-enforcement layer between model proposals and production writes.

AI infrastructureagentsstructured outputssecurityworkflow

Regular themes

Posts should help you make a call.

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.

For teams putting AI into daily work.

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.