GenAI Agents for the Workflows That Actually Resist Automation

Description and use

Reduced a six‑month accounting onboarding to three weeks by deploying narrow GenAI agents focused on judgment points and designing deliberate human checkpoints.

Problem

Enterprise accounting onboarding for a new fee‑charging business can take up to six months because most steps require judgment rather than simple data movement.

Outcome

The initiative reduced a six‑month onboarding process to three weeks by targeting automation to judgment points and using narrow agents with deliberate human checkpoints.

Workflow

• Map the process and split steps into mechanical and judgment‑heavy buckets.
• Design narrow, bounded agents for each judgment decision.
• Place human checkpoints where reversibility is low and provide agent context to reviewers.
• Treat plausible‑but‑wrong outputs as signals where judgment must remain human.

Evidence

Enterprise accounting onboarding for a new fee‑charging business can take up to six months. Not because any single step is technically hard, but because most of the steps require judgment: assessing fee structures, tax treatment, and compliance context, rather than just moving data from one place to another.

Source

The core idea is simple to state and hard to execute: treat automation as a question of where judgment actually lives in a workflow, not as a question of model capability.

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Narrow agents, each scoped to one bounded decision, consistently outperformed the monolith.

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We designed the checkpoints deliberately, placing them where a wrong decision would be expensive and hard to reverse, and giving the reviewer the specific context the agent used to reach its conclusion.

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The most useful failures were the ones where an agent's output looked correct but missed context a human would have caught.

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