AI agent workflow design

Agents with real responsibilities need more than a loop and a prompt: state, leases, approvals, escalation, and a human veto that actually works.

Start here when the team wants agents, but nobody has decided what happens when one fails at 2am.

When this fits

The workflow spans several tools and needs state, ownership and review loops.
Some actions must wait for a human — and the system has to enforce that, not hope for it.
You need a hard list of things the agent is never allowed to do.

Agent architecture with task state, claim-and-lease semantics and recovery rules.
Approval and escalation design for the actions that touch money, customers or data.
An evaluation plan for when autonomy gets to expand — and when it shrinks.

Agents as an operating workflow with a visible history, not an opaque chat demo.
Failure paths designed before real users depend on them.
Autonomy that shrinks under uncertainty instead of guessing harder.

An agent-operated task engine
Agents can claim, heartbeat, complete, fail, and escalate work safely while humans watch the same board update in real time.
Multi-tenant SaaS with isolation enforced on every query
Tenant isolation became the default path for new features rather than a convention that could be forgotten under delivery pressure.

For teams that need agents operating inside a controlled workflow, not improvising in a chat window.

Most systems sold as agents should be workflows. How to tell which one you need, and what an agent must have before it touches production.

AI projects rarely die at the demo. They die months later, in production, for reasons that were visible on day one.