Insights
Practical perspectives on software, AI, and automation from the team at Automathing.
A company can succeed technically at AI and still fail to create value. The biggest risks are organizational, human, and strategic. Here is what leaders need to watch.
An AI agent doesn't crash: it hallucinates, loops, picks the wrong tool, and still returns 200 OK. How we built observability for our agents' reasoning.
Full autonomy is a risk on critical actions. How to add human-in-the-loop checkpoints to your AI agent workflows, with a real example from TowerZ.
Before launching any AI project, answer these 4 strategic questions: inefficiencies, team alignment, target outcomes, and data governance.
How we built a multi-agent architecture at Automathing: one orchestrator per project, specialized AI agents, and rules written from real errors.
A practical framework for quantifying the business value of AI-driven automation: beyond cost savings, across speed, quality and capacity.
