A feedback loop is when a system uses its own results to improve future performance. Instead of running the same way every time, the system learns from what happened and adjusts how it works next time.

Think of it like this: if you send an email and notice people ignore it, you might change your subject line. That's a feedback loop. You took an action, saw the outcome, and changed your approach based on what you learned.

In business operations, feedback loops make automation smarter over time. When an AI agent processes an invoice and a human corrects it, that correction becomes a learning signal.

The agent notices the pattern, like "invoices from this vendor always need tax adjustments," and starts handling similar cases correctly without needing corrections.

This creates compounding value. Early on, your team might correct the agent frequently. But as the feedback loop runs, corrections drop because the agent learns from each one. Within weeks or months, straight-through processing rates climb from 60% to 90%+, and your team focuses on exceptions instead of routine fixes.

Feedback loops are what separate static automation (like RPA scripts that break when something changes) from adaptive automation that gets better with use. They turn AI agents from tools you program into systems that learn your business.

Frequently Asked Questions

How is a feedback loop different from regular automation?

Regular automation, like an RPA bot or a workflow rule, does the same thing every time. If you program it to extract invoice numbers from a specific field, it will always look in that field, even if vendors start putting numbers somewhere else. A feedback loop changes this.

When someone corrects the bot's mistake, the system learns and adjusts. Next time it sees a similar invoice, it checks the new location. Regular automation is like following a recipe exactly. Feedback loops are like a chef who tastes the dish and adjusts the seasoning.

What are the stages of a feedback loop?

Every feedback loop has three stages: action, outcome, and response.

• First, the system takes an action, like processing a purchase order.
• Second, it captures the outcome, such as whether the PO was approved, rejected, or flagged for review.
• Third, that outcome feeds back into the system to improve how it handles similar cases in the future.

For example, if your AP agent keeps flagging invoices from a specific vendor for manual review, and you approve them every time, the agent learns that the vendor is low-risk and starts auto-approving their invoices.

What's the difference between positive and negative feedback loops?

Positive feedback amplifies change, while negative feedback stabilizes it. If your claims processing agent learns that faster resolutions boost customer satisfaction scores, it might prioritize speed even more (positive feedback). But if rushing causes more claim denials, the system rebalances to maintain accuracy and compliance (negative feedback).

Both types matter.

Positive feedback drives improvement and growth. Negative feedback prevents the system from going too far in one direction and maintains control.

How do feedback loops work in practice for something like invoice processing?

Let's say your AP agent processes 100 invoices. It auto-approves 70, but your team corrects 30 because of issues like missing PO numbers, wrong GL codes, or duplicate invoices. Each correction becomes a data point.

The agent analyzes what triggered the correction, such as "this vendor always uses 'PO#' instead of 'Purchase Order,'" or "invoices over $10K from this department need VP approval."

Next month, the agent applies those patterns and auto-approves 85 invoices. After three months, it's at 95. Your team's workload drops from 30 corrections per 100 invoices to 5, and processing time falls from days to hours.

How long does it take for a feedback loop to show results?

You'll see initial improvements within weeks, but the real compounding value comes over months.

In the first 2-4 weeks, the agent learns obvious patterns, like which vendors have consistent formats or which GL codes apply to recurring expenses. Straight-through processing might jump from 60% to 75%.

Over the next 2-3 months, it learns edge cases and exceptions. By month six, many teams see 90%+ straight-through rates. The key is that learning accelerates. Every correction teaches the agent something, so each month requires fewer corrections than the last.

Can feedback loops work if my processes aren't standardized?

Yes, and this is actually where feedback loops shine. If your AP team handles invoices differently depending on the vendor, department, or invoice type, a feedback loop adapts to those variations.

It doesn't need perfect standardization upfront. Instead, it learns your actual patterns by watching how your team handles different scenarios.

For example, if your team always escalates invoices from international vendors, the agent picks up on that and starts doing it automatically.

Feedback loops adapt to how you actually work, not how a consultant thinks you should work.

What happens if the feedback loop learns the wrong thing?

This is a real risk, especially if bad data or incorrect corrections enter the system. If someone accidentally approves a fraudulent invoice, and the agent learns from it, you could end up reinforcing the wrong behavior.

This is why human oversight matters. You need visibility into what the agent is learning and the ability to correct its course.

Zamp addresses this through activity logs and approval checkpoints.

Every action the agent takes is recorded, so you can see exactly what it learned and when. If you notice it making incorrect assumptions, you can flag specific cases and add guardrails in the Knowledge Base, like "never auto-approve invoices over $50K" or "always route international payments to the finance director."

You can also configure approval checkpoints at any step, so even if the agent is learning, certain decisions still require human sign-off until you're confident in its judgment.