In our last Deploy Bravely post, Ian Swanson talked about the “Pilot Trap” — the place where projects get stuck in testing. For engineering leaders, that trap often starts with a dangerous illusion.
You build a prototype. It works beautifully in the demo. It answers every prompt correctly. But when you push that same system into the real world, it crumbles. It encounters adversarial inputs, confused context, and edge cases that never appeared in your clean training data.
Suddenly, your “game-changing prototype” becomes a liability.
The Shift: From Unit Tests to AI Evals
In traditional software engineering, the path to quality is deterministic. We rely on Unit Testing. The logic is binary: If user inputs X, expect Y. If the code passes, it ships.
Generative AI demands a fundamental shift in how we define “quality.” Because these models are probabilistic—meaning they don’t always give the same answer twice—you cannot simply write a static test for every possible conversation.
Instead of Unit Tests, we need AI Evals.
An “Eval” is essentially a dataset of tasks, questions, and prompts designed to systematically grade your AI’s behavior. Think of Evals as the new baseline for quality. They answer the questions: Is the model helpful? Is it accurate? Does it stay on topic?
However, building an Eval dataset has a limitation: It only tests what you know to look for. You can build excellent Evals to measure accuracy, but how do you test for the unknown?
- What if a user tries to trick the bot into revealing competitor data?
- What if they use complex “jailbreak” techniques to bypass safety filters?
- What if the model hallucinates a plausible but dangerous answer in a scenario you never anticipated?
Ideally, your Evals should cover these scenarios, but the “tail” of edge cases and negative examples is effectively infinite. Building a static dataset that anticipates every possible security risk or safety failure is incredibly difficult and time-consuming. To find the risks that cause reputation damage without stalling development, you need a more aggressive approach.
The Solution: Automate the Attack
To build resilient AI, you can’t just grade it; you have to try to break it.
Most teams rely on manual “Red Teaming”—hiring humans to try and trick the model. While essential, manual testing doesn’t scale. You can’t hire enough people to test every version of every model against the thousands of evolving attack vectors emerging every week.
This is where Automated Red Teaming becomes the force multiplier for your Evals.
As I discuss in this video, this requires shifting validation left. We use AI to test AI. By deploying automated adversarial agents that attack your model with thousands of evolving prompts, you can uncover weak points in minutes rather than months.
These agents act as the ultimate stress test for your Evals, finding the failure modes that your internal datasets missed and feeding that data back into your system to make it stronger.
Confidence is the Accelerator
Good evaluations and automated red teaming help teams ship AI agents more confidently. Without them, it’s easy to get stuck in reactive loops—catching issues only in production, where fixing one failure creates others.
- Engineers stop guessing and start knowing exactly where their guardrails are weak.
- Security Teams get visibility without acting as a roadblock.
- The Business gets resilient, trustworthy AI that can be deployed with confidence.
Enterprises don’t need perfect AI; they need AI that behaves predictably within well-defined boundaries. Prisma AIRS makes this continuous validation practical, turning risk management into the ultimate accelerator.
This is Part 3 of our Deploy Bravely series.
Up Next: Rich Campagna on why runtime security is the only way to trust AI in the wild.
