AI agents vs AI workflows
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.
A workflow is the right default: known steps, known checks, known failure handling. An agent is justified only when the system genuinely has to choose between tools mid-task — and only after state, approvals and rollback are designed.
Workflows keep accountability legible
When steps are known, every failure has an address: which step, which input, whose problem. Give that up for autonomy only when autonomy has a measurable reason to exist.
An agent is a set of operating rules, not a prompt
An agent that can act needs leases on the work it claims, approvals for actions that matter, escalation when it repeats a failure, and a trace of everything it did. Without those it isn't autonomous — it's unsupervised.
Autonomy must be reversible
The system should be able to dial autonomy down — route to a human, require review, pause a queue — without a redesign. If reducing autonomy requires an architecture change, the architecture is wrong.
Case studies behind this
- 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.
Related services
AI agent workflow design
Start here when the team wants agents, but nobody has decided what happens when one fails at 2am.
Open serviceInternal tools automation
Start here when operators need a real tool — with permissions, audit trails and half-done states — not another spreadsheet and a pile of scripts.
Open service