Most people believe they understand how software breaks. Decades of debugging and patching have trained us to think that every problem can eventually be fixed if we just find the right line of code.

AI Doesn’t Break the Way Software Does

When I worked as a Senior Product Manager across SharePoint, OneDrive, and Lambda at AWS, I lived inside the familiar rhythm of software development.

We shipped products, found bugs, logged Jira tickets, fixed them, and shipped again.
Every problem could be traced to a clear line of code or configuration and when you fixed it, it stayed fixed.That cycle built an entire philosophy around control. If something broke, there was always a reason. And if you were patient enough, smart enough, and had the right people in the room, you could make it right.

Then in 2023, I started working on copilots. That’s when I realised how deeply this belief system fails with AI.The copilots had no bugs in the traditional sense.'

They hallucinated and got things wrong in ways that didn’t make sense. Sometimes they were too confident. Sometimes they said things they shouldn’t.
We logged Jira tickets. We ran test cases. We tuned prompts and retrained models and every time we thought the issue was gone, it would resurface just wearing a new face. That was the moment it became clear.

You can’t debug behaviour. You can only understand it, test it, and monitor it. Traditional software fails for logical reasons. AI fails for behavioural ones. Code follows the rules we write.

AI follows patterns it has absorbed from billions of data points, many of which no human has ever seen in full. When an AI system misbehaves, it isn’t because someone missed a semicolon or forgot a bracket. It’s because somewhere inside the learned behavior, a hidden pattern was reinforced. You can’t trace or fix it the way you fix code. You can only observe it, simulate it, and keep validating what it does.

That realisation became the seed for Disseqt AI

We didn’t set out to create another testing tool. We wanted to rethink what testing even means in the age of Agentic AI. Disseqt is a testing and simulation workbench for DevOps teams. It evaluates AI systems for Responsible AI, security, and compliance principles before they ever reach production.

It treats safety as a living process, not a checkbox at the end of deployment. AI safety is not debugging. It is the discipline of watching what you build, continuously, as it learns and evolves in ways you can’t fully predict.

At AWS, every bug I fixed gave me the illusion of finality. Building copilots took that illusion away. It taught me that as AI grows in capability, it also grows in unpredictability.
And that is the paradox of this new era.

The more powerful our systems become, the less certain we can be about what they will do next. So maybe AI doesn’t need fixing at all, instead it needs vigilance.

That is where this story began and that is why Disseqt AI exists to make sure the intelligence we create is accountable to the world that uses it.

Disseqt AI serves as an enterprise workbench for testing and simulating AI agents before they reach production

It helps DevOps teams validate agents on three fronts:

• Responsible AI principles such as fairness, bias, and transparency.

• Security vulnerabilities including jailbreaks, prompt injections, and data leakage.

• Governance and compliance standards defined by frameworks like ISO, NIST, and the EU AI Act

Using automated red teaming and jailbreaking, Disseqt AI identifies risks early and
makes t he entire testing process faster and cheaper. Our data shows that teams using Disseqt increase production readiness by nearly 70 percent while reducing validation costs by about 80 percent

When I look back, I realise that what we once called debugging has now evolved into something much bigger. It is about staying awake to what’s possible, and what could go wrong if we stop watching.

The next frontier of AI won’t be built by those who move fastest. It will be built by those who build safely, transparently, and with the courage to test what others assume will work.