Rohan Paul (@rohanpaul_ai) · 10/19/2026, 15:59:29
AVRI Cuts AI Security Agent Costs by Optimizing Code Understanding Tokens
Research reveals that AI security agents spend 60.4% of their tokens on code understanding and bug-triggering reasoning rather than execution. The new tool AVRI addresses this bottleneck by optimizing context retrieval and input-operation linkage reuse, reducing operational costs for Codex and OpenCode by 18.0% and 23.7% respectively without compromising success rates.
SOURCE COVERAGEFull Original Report
Rohan Paul (@rohanpaul_ai) · 2026-10-19 07
AI agents that hunt security bugs spend 60.4% of their tokens understanding code and reasoning about how to trigger the bug, so that is where cost cuts should aim.
AI agents hunting security bugs can burn millions of tokens without producing an input that crashes the program. Across 200 runs from 4 agents, 60.4% of tokens went to understanding code and reasoning about how to trigger the bug.
Existing cost-saving methods rarely helped. Only 24.4% of head-to-head comparisons with the plain agent kept success at lower cost.
So they built AVRI, a tool that returns related code together and saves the links between program input and the unsafe operation. On 20 new tasks, it cut total cost by 18.0% for Codex and 23.7% for OpenCode without lowering success or recall.
Before adding generic cost tricks to a security agent, measure where its tokens go and let it reuse what it learned about the code.
