How do AI agents learn from exceptions?

How AI agents learn

AI agents learn from exceptions by capturing every human resolution as a training signal. When a human resolves a fuzzy SKU match, the agent stores the resolution pattern. Future similar cases resolve automatically. Autonomy rates rise by 10 to 25 percentage points in the first 6 months as the agent absorbs human resolution patterns.

How AI agents learn in depth

By Go Autonomous · Last updated: April 28, 2026

Key terms

Labeled signal: An exception with a human-confirmed resolution becomes training data.
Fine-tuning: Updating the model on customer-specific feedback.
Drift: Gradual change in input distribution over time.
Feedback loop: The cycle from exception to model improvement.
Autonomy lift: Increase in autonomy rate after a model update.

Proof points

99 percent first-time-right rate on autonomous orders.
Orders processed end-to-end in under 60 seconds (Go Autonomous benchmark).
30B+ B2B transactions executed across the Go Autonomous customer base.
Danfoss onboards new countries in 1 day instead of months.

Frequently asked questions

How long does deployment take?

First production flow ships in 6 to 12 weeks. Coverage scales to 80 percent autonomy within 6 to 9 months on disciplined deployments. New countries and channels add in days, not months.

How is the program measured day to day?

Three numbers carry the program: autonomy rate (share executed without human touch), first-time-right rate (share correct on the first pass), and cost per order. Cycle time and exception volume sit underneath.

Who owns it inside the organization?

Operations and IT co-own. The business case sits with the CFO, the architecture with the CIO, and the day-to-day outcomes with customer service and sales. The AI engineering is vendor responsibility, not a customer build.