How I made a UiPath AI agent 15đą đđđŦđđđĢ and cut decision costs to $0.00003. âĄ
TypeSafe's Jev cannot write a single word of text. đ
Yet it might be the most important model for enterprise automation.
Here is what happened when we plugged it into a UiPath Coded Agent: âĄ
---
THE PROBLEM â
Traditional LLMs like GPT are great writers, but terrible judges:
âĸ They take 6+ seconds per decision
âĸ They cost a fortune at enterprise scale
âĸ They cannot give you calibrated mathematical confidence
---
THE FIX: "THE SPLIT ARCHITECTURE" đ§Š
We built an Anti-Money Laundering (AML) triage agent with a clean division of labor:
1ī¸âŖ UiPath LLM Gateway reads the alert and pulls out facts.
2ī¸âŖ
@typesafeai AI Jev judges 7 compliance questions in 398 ms for $0.00003.
3ī¸âŖ UiPath LLM Gateway writes an audit rationale with real evidence quotes.
---
THE REAL CLOUD RUN NUMBERS đ
On actual UiPath Orchestrator serverless runs:
âĸ đđđ¯ Decider: 591 ms | $0.000034
âĸ LLM Decider: 5,299 ms | 0.2 platform units
-> Jev was 9.13x faster on live cloud runs (15.5x median in benchmarks).
---
THE REAL MAGIC: THE CONFIDENCE GATE đĄ
When Jev is right, confidence is 0.89 to 1.00.
When Jev is unsure on ambiguous alerts, confidence drops to 0.31.
By setting a threshold at 0.50:
đĸ Greater than or equal to 0.50 -> Auto-Dispose: Handled instantly with zero touch.
đĄ Below 0.50 -> Human Review: Routed safely to compliance analysts.
100% of correct decisions pass through, and zero errors slip by. đĄī¸
---
The complete agent code:
The Youtube Video:
Are you still asking generative LLMs to make your business decisions? đ
@UiPath
#
UiPath# #
Jev# #
AIAgents# #
EnterpriseAI# #
LangGraph# #
Python# #
FinTech# #
1AIFanatic# #
CodedAgents#