Black Box Validation: What Ops Can Teach Us About AI Development
Why operations professionals might have an edge in AI-augmented development
I haven’t posted for a coule of weeks; school holidays meant a driving holiday with the family, and I’m now back finishing up a range of engagements. But I’ve been mulling over an interesting pattern I noticed before I left.
Operations and security engineers seem to be adapting quickly to AI-augmented development. The reason why suggests something interesting about where this field might be heading.
The Ops Advantage
Ops and security professionals are comfortable working with code as a black box. While they dig into implementation details when needed, they routinely operate without understanding every line. Their confidence comes from external validation: monitoring systems, security scanners, test results, performance metrics. These feedback loops signal when something’s wrong.
This comfort with validation over comprehension translates directly to AI-augmented development. Instead of inspecting every line of AI-generated code, we can rely on the same validation approach. Linters, static code analysis, dynamic analysis, and other inspection practices verify code automatically. Humans still review code, but tools provide adequate confidence that a generated function performs as expected.
The Psychological Barrier
For many software engineers, this shift is uncomfortable. They’re trained to understand and own every line of code they ship. Letting go of that deep comprehension feels irresponsible.
Ops and security professionals don’t carry this burden. They’ve already made peace with validating systems they don’t fully understand internally. This removes a psychological obstacle that can slow traditional developers from adopting AI-augmented workflows.
Recognizing this barrier is why we’re helping to create time, space, and a safe environment for software engineers and leaders to learn AI-assisted software development through our course: [From Assisted to Agentic AI Development](https://hyprinnovation.io/training/from-assisted-to-agentic-ai-development).
The Tight Inner Loop
Here’s how the validation approach works in practice:
The AI agent operates in a tight inner loop, taking small, targeted, self-contained actions guided by specs and context documents. After each action, it validates against multiple quality attributes: Can it handle load scenarios? Does it comply with standards and regulations? Are there security issues? Does it meet product and business goals? Are coding standards met?
The feedback loop serves dual purposes. It enables the AI to iterate and run longer, addressing issues as they arise. And it gives us confidence that the software meets our quality requirements: security, performance, reliability.
The agent keeps rolling forward, with validation tools providing continuous feedback on each incremental change.
The Outer Loops
Human oversight operates at a broader scale. When validation feedback reveals deficiencies in the spec itself, indicating the current approach won’t produce the necessary architecture, you discard what’s been generated, improve the specs, and rerun.
Production environments create even broader feedback loops. User feedback, observability data, and business metrics flow back into the development cycle. These signals inform not just the code, but the specifications and requirements themselves: multiple layers of validation feedback, just as ops teams have always used to refine their systems.
Why This Matters
The pattern suggests something: you don’t need humans to inspect every line to build reliable systems. You need robust validation mechanisms and the discipline to trust your feedback loops.
The ops and security mindset of validating complex systems through external signals rather than internal inspection might point to where AI-augmented development is heading. As AI tooling becomes more capable, the professionals who’ve already internalized this approach may have a natural advantage.
It raises a question worth considering: are your validation loops strong enough to work this way?
I can’t tell you how much this warms my heart to read.
I’ve long thought (in AI years) that AI-enabled development is as much about setting up the guardrails around its delivery as it is about having deep understanding or confidence in the code it creates.
And you’re spot on; having operated someone else’s software for most of my career, I’m very comfortable not knowing line by line what I’m fighting, working, caressing, loving, hating, or supporting.
And finally, perhaps more controversially, given the Ops and Sec teams’ comfort and experience with the “black box,” I think leaders with that background are better placed to lead into the future. Those with purely development backgrounds who can’t get over that hump of comprehension are going to find it challenging to lead engineering teams effectively going forward.