𝗬𝗼𝘂𝗿 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗜𝘀𝗻'𝘁 𝗕𝗿𝗼𝗸𝗲𝗻. 𝗬𝗼𝘂𝗿 𝗖𝗼𝗺𝗽𝗮𝗻𝘆'𝘀 𝗧𝗿𝘂𝘁𝗵 𝗜𝘀.
An AI agent had one job. It needed to pay an approved vendor invoice. The finance team wanted to stop doing this manually.
On a Tuesday, the agent picked up an invoice for 48,000 Ksh. The ERP system marked it as approved and unpaid. The agent checked the rules. Everything passed. It paid the invoice.
The problem? The invoice was already paid last Thursday by a human.
No single system was wrong, but they all disagreed:
• The ERP said it was unpaid because the bank sync failed. • The bank feed said it was paid. • A Slack thread said to hold all payments to this vendor. • An email from the vendor confirmed they received the money.
Every system held a piece of the puzzle. But no system could answer the only question that matters: Has this specific invoice been paid?
A human clerk would have caught this. Not because they are smarter, but because they feel friction. A human remembers the task or notices the Slack message. Humans act as the glue between messy systems. We provide judgment.
AI agents have no instinct. They read data at machine speed. They see "Approved" and "Unpaid" and they act. They do not feel that something is wrong.
The agent did not fail. It worked exactly as designed. The failure is what I call epistemic collapse.
Most companies try to fix agents with better prompts or better models. This is the wrong move. You cannot prompt your way to information that does not exist in your data.
The real problem is the lack of epistemic infrastructure.
Data is what your systems store. Truth is what is actually happening. Most companies have plenty of data but zero truth.
Current systems crush three different things into one field:
- Observation: What a system says happened.
- Truth: What is actually true.
- History: What used to be true.
When a database says "Status: Unpaid," it deletes the history and the doubt. It presents a single observation as absolute truth.
AI agents are a stress test for this old problem. They remove the humans who were silently fixing these contradictions every day. Without humans to bridge the gaps, the cracks in your data become expensive mistakes.
Stop trying to build better agents. Start building the layer underneath them. You need a system that tracks observations, recognizes disagreements, and identifies stale data.
जब तक आप सत्य की एक परत तैयार नहीं कर लेते, आपके एजेंट गलत काम को पूरी सटीकता के साथ करते रहेंगे।
स्रोत: https://dev.to/code_with_mwai/your-ai-agent-isnt-broken-your-companys-truth-is-2cl8
वैकल्पिक शिक्षण समुदाय: https://t.me/GyaanSetuAi