Every sprint, we are required to run Fortify security scans on our applications. Fortify gives us two scans. The static scan reads the source code without running it, and the dynamic scan tests the running application from the outside, the way an attacker would. Each finding comes with a severity level, and the higher the severity, the tighter the deadline. Some findings are real issues. Others turn out to be false positives.
Fixing them has always been slow. Developers need to invest many hours to research each finding and understand the issue. Usually the developers fixing the issues are not the ones who wrote the code, so the team needs time to understand the code before applying the fix. Depending on the complexity, the cycle can take weeks, and the deadlines don’t wait.
We are building an agent team with the Claude Agent SDK to do this work with us. While designing it, I kept coming back to one question: what kind of teammate should the AI actually be?
Research on human-AI teams separates two results. Augmentation is when AI makes the person better than they are alone. Synergy is when the person and AI together beat both the person alone and the AI alone. A large meta-analysis by Vaccaro, Almaatouq and Malone (2024) looked at 106 experiments. Augmentation was common, but synergy was rare, especially in decision tasks. In many studies the AI alone would have done better than the team.
An X-ray example helped me understand why. Say an experienced doctor is right 90% of the time and the AI is right 75%. Together they can beat both, because the doctor knows when the AI is wrong. Now take a new doctor who is right 70% of the time, with an AI at 90%. The team is better than the doctor but worse than the AI alone, because the new doctor overrides the AI at the wrong moments. A team only beats both when the person is good enough to judge the AI.
The same paper recommends dividing the work up front, based on who is better at each part. That is how we designed our agent team. The agents review all static and dynamic findings and categorize them into true or false positives. A senior developer agent fixes the true positives, and another agent reviews the code. A senior QA engineer agent runs unit tests and dynamic tests, and a coordinator agent tracks the work and reports back to me. If an agent thinks an issue is a false positive, it tags it and I make the decision. After the fix we run both scans again.
I keep the false-positive decision for a reason. It’s the most costly mistake. AI can hallucinate, and its explanation can look very real and seem accurate. A real issue that gets wrongly dismissed stays in production as a security hole nobody is looking for.
Personality turned out to matter too. In the MindMeld study, Ju and Aral gave AI agents different Big Five personalities and found that the pairing of human and AI personality changes the quality of the work. I am open and conscientious. For security fixes, I want agents that are highly conscientious and lower on openness than me. There is a set of rules in place, and if a request goes beyond these rules, the agent should push back and let me know the rule forbids it. An agent with high openness like me can go out of context, and a small security fix turns into a bigger change nobody planned. For designing a new feature, a more open agent can work.
Another paper from the same group surprised me. DosSantos DiSorbo, Ju and Aral found that AI agents follow rules strictly even when a person would make an exception. If the limit for flour is $10, the agent won’t buy it at $10.01, while most people would. In a grocery run that is a weakness. In security work it’s exactly what we want.
For communication, we use AI to increase productivity, so I want agents that only focus on business. Ju and Aral found that human-AI teams focused on process and content, and more feedback messages were linked to higher quality. Business focus doesn’t mean the agents stay quiet, though. They need to ask questions and never assume. I also found that an interview-style review works much better for me, like “give me the first 10 and let’s go through them one by one.” After approving 50 good recommendations in a row, it’s easy to stop reading closely.
Claiming AI alone will be 100% correct is unrealistic. But these are security risks, so we need to aim for 100%, and we get there with a human in the loop. Augmentation is the part I’m sure of. Synergy is the goal, and splitting the work by who does each part better is how we’re trying to reach it.
Sources
- Vaccaro, Almaatouq & Malone (2024), “When combinations of humans and AI are useful,” Nature Human Behaviour
- Ju & Aral, “Collaborating with AI Agents”
- DosSantos DiSorbo, Ju & Aral, “Teaching AI to Handle Exceptions”