The Governance Problem in Multi-Agent Systems
Multi-agent systems distribute responsibility across a network. Governed execution keeps authority, evidence, approvals, and accountability intact as work moves.
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Approvals
5 articles on this topic.
How an AI agent for invoice processing keeps a human in control
Invoice processing is high volume, rule-bound, and unforgiving of errors. A governed AI agent can read, match, and code invoices while routing every payment decision to a person.
How an AI agent for expense reports keeps approvals in place
Expense reports are repetitive, rule-bound, and easy to get wrong. A governed AI agent can read receipts, apply policy, and route the judgment calls to a human, while recording every step.
Approvals are a feature, not a friction
Approval is often treated as the thing slowing automation down. In business AI execution, it is the mechanism that lets more work move without pretending every action has the same risk.
Designing human checkpoints for AI agents
Human-in-the-loop design can be precise rather than performative. The checkpoint should appear at the moment risk changes, with enough context for a fast decision.