What It Takes to Trust the Devices in Your Pocket: A Conversation with Anil Reddy Putlacheru

The device in your pocket is one node in a very large crowd. By the end of 2025, roughly 21.1 billion connected devices were in use worldwide, and most of them depend on talking to something else: a phone, a charger, a firmware server, a pair of earbuds a few feet away. A product can pass every check on a lab bench and still misbehave the moment it joins that crowd. A headset pairs cleanly in a quiet office and drops on a packed train. A smartwatch reads healthy for a week, then drains overnight after an update. The real question is never whether a device works once. It is whether it keeps working in conditions no one fully controls.

Anil Reddy Putlacheru has spent nearly 20 years on that question. A senior software developer in test, he builds the automated systems that put consumer hardware through the messy conditions of real use before it ships: wireless links that fail, batteries that age, firmware updates that go wrong. His work was recently the subject of a TechBullion feature. Lately it has moved into AI-assisted engineering tools, but the discipline underneath has not changed. The job is to prove a product will hold up when the person using it is not an engineer and has no patience for excuses.

We spoke with Anil about why connected devices fail after they leave the factory, what it takes to catch those failures first, and how software testing grew from a checklist into a serious engineering discipline of its own.

A device passes all its tests and still fails in the field. Where does that gap come from?

Most tests ask a narrow question: does this feature work once, under clean conditions? Real use asks something harder. Does it keep working across thousands of cycles, in a noisy radio environment, on a battery at half its original health, surrounded by 40 other devices fighting for the same spectrum? Those are different questions. A device can pass the first and fail the second, and the second is the one the customer actually lives in.

The other factor is time. Many failures only appear after a device has been running for days or weeks. A slow memory leak. A battery that drifts. A firmware state that quietly corrupts itself after enough updates. You will never catch those by hand in an afternoon. You catch them by running the device continuously, the way it will really be used, and watching for the moment it starts to drift away from healthy.

So how do you close that gap?

You automate the boring, endless parts of real use, and then you let them run for a long time. Much of my career has been building systems that do exactly that for connected consumer hardware. Instead of a person pairing a headset a few dozen times by hand, you build a rig that pairs and unpairs it thousands of times, cycles the battery through charge and drain again and again, pushes firmware updates in a loop, and records everything, so you can find the exact cycle where behavior changed.

Automation like this does not remove human judgment. It concentrates on it. When a rig has run a device through thousands of pairing attempts and just one of them failed, that single failure earns an engineer's full attention, because it is real and it can be reproduced. Manual testing rarely gives you that. It gives you "it seemed fine," which is not something anyone can debug.

Bluetooth comes up constantly in your work. Why is it so hard to get right?

Because you do not control the other side of the connection. Around 5.3 billion Bluetooth devices are expected to ship in 2025 alone, from every manufacturer, running every version of the stack, each with its own quirks. Your earbuds have to work with a brand-new phone, a 5-year-old phone, a laptop, a car, a watch. The specification says they should all interoperate. Reality is messier. Two devices that each pass certification on their own can still fail to talk to each other in one specific state, and you only find it if you test the combinations real people actually own.

So a lot of the work is refusing to trust the easy path. You test pairing when the airwaves are already crowded. You test reconnection after the signal cuts out in the middle of a transfer. You test what happens when the phone updates its own software and your device did not change at all. Low-energy connections add another layer, because now power behavior and connection behavior are tangled together. This is slow, careful work, and it is most of the job.

What is the failure mode that worries you most?

A bad firmware update. When you send an over-the-air update, you are reaching into a device already sitting in someone's hand and rewriting part of how it works. If that goes wrong, there is no lab bench to catch you. The device can land in a state it cannot get out of, and now it is a paperweight in a customer's drawer. In 2025, recalls in the United States covered nearly 858 million defective units across industries, and a growing share of product problems now trace back to software and firmware rather than a broken part.

So the update path gets tested harder than almost anything else. You confirm the upgrade works, but you spend even more time on what happens when it fails halfway through: the power drops, the connection dies, the new image is corrupt. Can the device fall back to the version it was running before? Can it recover on its own, without a trip to a service center? I would take a device that updates a little slower and always recovers over one that updates fast and occasionally bricks. For me that tradeoff is not close.

You have watched testing change a lot over your career. How different is the discipline now?

It stopped being a checklist and turned into engineering. Early on, testing was often treated as the thing you did after the real work was finished, and testers were not always expected to be strong software engineers. That view is gone, or it deserves to be. Building a system that drives real hardware around the clock, ties failures together across firmware and connectivity and power, and produces evidence an engineer can act on — that is a genuine software problem. You are writing the tools yourself, not operating someone else's.

Teams that still treat testing as a lower tier of work tend to pay for it later, in the field, in front of customers, which is the most expensive classroom there is. The teams that treat validation as first-class engineering catch problems while they are still cheap and quiet. And the skill that matters most is not fluency in any single tool. It is being able to picture how a system will fail, then to build something that forces that failure to happen on purpose.

Lately your work has moved toward AI-assisted engineering. How does that change validation?

It changes where an engineer spends attention. Much of validation is investigative: something failed, and now you are reconstructing what led up to it from logs, traces, and history. That reconstruction is slow, and it is a good fit for AI tooling — not to make the call for you, but to gather the relevant evidence so you begin from a real starting point instead of a blank screen. I have been building in that direction, and the aim is always to get an engineer to a correct answer sooner, without asking anyone to trust a black box.

I stay cautious about the hype, though. In validation especially, an AI that is confidently wrong is worse than no AI at all, because testing is the one function whose entire purpose is to be trustworthy. So I set a simple bar. Does the tool get an engineer to a correct conclusion faster, or does it just generate more confident-sounding output? If it is the second, it does not belong anywhere near a validation pipeline. That is a stricter standard than many teams are applying right now.

Where do you want to take this work next?

I want validation to feel less like a gate bolted on at the end and more like something running quietly the entire time, so that by the time a device is ready to ship, its reliability is already proven in data rather than argued over in a meeting. For connected products, that means testing continuously against the messy version of the world instead of the clean one, and treating battery life, connectivity, and firmware recovery as part of the core product rather than details to check off at the end.

The specific thing I am focused on is making that evidence easier to trust and quicker to reach, whether it comes from a robotic rig or an AI agent. The job itself has not changed in 20 years. When someone lifts a device out of the box, it should work, and it should keep working long after everyone has stopped paying attention to it. That is still the part I find worth doing.