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Building Trust with AI Agents - Henri Terho

Software Testing Unleashed · 2026-04-30 · 21 min

0:00--:--

Key moments - from our scoring

Substance score

55 / 100

Five dimensions, 20 points each

Insight Density12 / 20
Originality11 / 20
Guest Caliber13 / 20
Specificity & Evidence9 / 20
Conversational Craft10 / 20

Henri Terho, a senior consultant with regulated industry experience, argues that building trustworthy AI systems isn't fundamentally different from managing human teams or subcontracted software - it requires solving two core problems: the Oracle problem (how do you know what you want?) and the communication problem (how do you convey requirements clearly enough for the system to deliver it?). The challenge intensifies because AI systems multiply both mistakes and successes at scale. Rather than viewing AI testing as novel, Terho positions it as an evolution of existing practices: acceptance testing, end-to-end validation, and quality assurance frameworks already handle similar trust-building challenges. However, AI introduces critical differences - non-deterministic outputs mean testers must shift from checking single test results to monitoring trends, mean time between failures, and statistical patterns over time. Terho emphasizes that specs and test definitions become the single source of truth when code becomes a black box. He also addresses fears about AI replacing testers, arguing instead that the testing mindset and skillset - defining criteria, writing specifications, designing validation - are positioned better than most IT roles for the AI era. The conversation touches on broader implications: AI will surface organizational problems like version control chaos (multiple SharePoint documents), force companies to document business rules explicitly, and likely drive change from outside IT departments where business value creation is clearer.

Key takeaways

  • →Building trust in AI systems requires solving the Oracle problem (knowing what you want) and the communication problem (conveying requirements clearly enough for AI to deliver) - challenges identical to those with human teams.
  • →AI testing fundamentally differs from traditional testing because models are non-deterministic; testers must monitor trends and statistical patterns (mean time between failures) rather than expecting single test results to be reliable.
  • →Specs and test definitions become the single source of truth when code is a black box, making BDD and TDD practices more critical than ever for establishing what AI systems should actually do.
  • →Testers are well-positioned for the AI era because the core testing skillset - defining acceptance criteria, writing specifications, designing validation - directly translates to how you interface with and verify AI systems.
  • →AI will likely drive organizational change from outside IT departments (marketing, business units) where value creation is clearer, forcing companies to document business rules and create platforms for single sources of truth.

In this episode

  1. 1Introduction and AI Trust in Quality Assurance
  2. 2The Oracle Problem and Communication Challenges with AI
  3. 3Real-World Examples of AI System Failures
  4. 4Testing and Validation Approaches for AI Systems
  5. 5Non-Deterministic Testing and Trend Analysis
  6. 6Managing Changing AI Models and System Stability
  7. 7BDD, TDD, and Specification-Driven Development for AI
  8. 8Democratization of Software and Quality Responsibilities

Mentioned

Henri TerhoChatGPTSalesforceAzureSharePointHustaf ConferenceRitchieZillaRobb

Guests

Henri Terho

Topics in this episode

AI agentsAI guardrailsAcceptance testingOracle problemnon-deterministic testingBDD (Behavior-Driven Development)TDD (Test-Driven Development)trend-based monitoringstatistical validationblack box software verification

Questions this episode answers

How do you know if an AI system is delivering what you actually wanted?

Apply the same acceptance testing and validation approaches used for subcontracted software: clearly communicate requirements upfront, validate that the output matches your specs, and recognize that failures often stem from poor requirement communication rather than system failure.

Why is traditional test automation insufficient for AI systems?

AI models are non-deterministic, meaning you cannot run a single test, see it pass, and assume the system is reliable; instead, you must monitor trends, statistical patterns, and mean time between failures over time, similar to how aerospace and manufacturing approach quality.

What testing and quality skills transfer best to the AI era?

The core testing mindset - defining acceptance criteria, writing clear specifications, designing validation frameworks, and establishing single sources of truth - directly translates to verifying AI systems and is more applicable than traditional coding expertise.

Will AI replace testing and QA jobs?

No; AI will augment how testers work rather than automate them away, and the demand for trust-building and validation will likely make QA roles more critical as AI systems become business-critical.

What happens when AI model behavior changes because the underlying vendor updates the model?

This is a systems-level challenge requiring stability mechanisms and continuous validation frameworks; it highlights that managing AI systems means managing components that constantly change, unlike traditional software with fixed code.

What our scoring noted

Our reviewer’s read on each dimension, with quotes from the episode.

Insight Density

12 / 20

The episode covers several substantive topics - the Oracle problem, non-deterministic testing, spec-driven development for AI systems, and the role of testers in an AI-augmented world - but drowns them in conversational padding, throat-clearing, and meandering explanations. Key ideas (e.g., 'testing AI requires looking at trends, not single pass/fail outcomes') are valuable but buried in lengthy setups and repetitive analogies that don't add clarity. The signal-to-noise ratio is moderate; a B2B operator would extract useful framings but wade through substantial filler to get there.

building systems where we can actually trust in AI In the way i can trust my colleagues and everybody else Is very important
with AI models of course being non-deterministic. You Have to kind Of like shift your thinking in testing also not running Like. Is this green? No! You're looking at over time...you are Looking At trends

Originality

11 / 20

The core framing - that AI testing is fundamentally about communication/specification (Oracle problem) and non-deterministic validation (trends over single tests) - is sensible but not novel in 2024. The analogy to aviation/machine-shop quality practices is borrowed wisdom, not original thinking. The suggestion that testers should expand into DevOps/business context is increasingly standard advice. Guest touches on biases in code-driven decisions, which is interesting, but doesn't develop it with rigor or contrarian depth. No major counterintuitive claims or first-principles challenges.

There are a lot of problems, but I think that the main ones have kind like this same problem we had with humans and everything else on there
It's the same thing that machine shops airplane like these guys do That they're Not looking just one point test

Guest Caliber

13 / 20

Henri Terho is a senior consultant in regulated industries with stated passion for AI and QA, which is relevant to the topic. However, the transcript provides minimal evidence of deep operational experience shipping AI systems at scale. References to 'research' and being approached inbound by marketing teams suggest advisory/consulting work rather than hands-on building. No specific companies, scale metrics, or war stories from major AI deployments. He is reasonably calibrated for a B2B QA audience but not a high-bar operator or practitioner with battle-tested frameworks.

He's senior consultant with broad experience in regulated industries And also he very passionate about AI & Quality Assurance
I'm actually doing a little bit research on my PEG topic about how we've done major business decisions just by doing software

Specificity & Evidence

9 / 20

The episode is notably light on concrete examples, metrics, and named case studies. The ChatGPT business-strategy example is generic and illustrative only, not a real deployment story. Discussion of 'locking to Azure' and SharePoint document versioning are vague references to real problems but lack specificity: no company name, no actual numbers, no timeline. No metrics on AI test pass rates, failure modes observed in production, or financial impact. The theoretical framing is clean but the substantiation is weak; a B2B operator would struggle to translate abstract points into playbook items.

if you ask AI agent take a generic like chat gpt or something, you say that hey I'm small business owner. i am doing this in software can make me a business strategy and of course from there on get the very generic business strategy
coder has made major business decision like locking us to Azure or one day

Conversational Craft

10 / 20

Host Ritchie asks opening questions that are reasonable but rarely pushes back or dig deeper on claims. When Henri says 'AI will augment not replace testers,' there's no follow-up on specific examples or tension points. Guest makes broad assertions ('testing skill set is already very suitable for the new world') that go unquestioned. Host allows repetitive explanations to stand unchallenged and doesn't probe contradictions (e.g., if specs become 'black box,' how does one write tight specs without seeing the system?). Conversation feels collegial and safe rather than intellectually rigorous; few moments of productive disagreement or sharp redirect.

Yeah.. This more worked on first option. Of course I want the first one!
And I think it's a communication problem we have with humans. We encounter daily in our job

Conversation analysis

Computed from the transcript - who did the talking, and the words that came up most.

Most-used words

testing21problem18software16system11course11change11systems10quality9trust9problems9test8write8build7didn6code6figure6

Episode notes

How to build trust into AI systems when they constantly change underneath you Free e-book: The 7 success factors of software testing. 25 years of project experience in one 33-page workbook, now also in English Get it for free "AI doesn't think, it doesn't analyze, it predicts." - Henri Terho In this episode, I talk with Henri Terho, senior consultant and AI enthusiast, about why building trust in AI systems requires the same rigor we've always applied to software - just now at a whole new level. Henri explains how AI agents multiply both our successes and our mistakes, why prompting is harder than it looks, and why testers are uniquely positioned to thrive in this shift. We dig into the oracle problem, the communication trap, and why your test suite might soon matter more than your codebase. Henri Terho is a Senior AI Consultant at Eficode with broad experience spanning regulated industries - automotive, banking, aerospace, and beyond - alongside a deep commitment to open-source collaboration.

Full transcript

21 min

Transcribed and scored by The B2B Podcast Index.

Welcome to Software Testing Unleashed, the podcast for testers developers and all software people who want to create best quality software. My name is Ritchie. I'm a software-quality coach author & keynote speaker And happy to bring you an episode live from The Hustaf Conference in Budapest. Gustav is one of the greatest conferences I've ever been to.

Top quality talks, amazing people and that unique mix of friendly and intense atmosphere you only find at truly great events! If you have every get a chance go there seriously You will love it. My guest today is Henry Tero. He's senior consultant with broad experience in regulated industries And also he very passionate about AI & Quality Assurance.

And that's what we talked about. How can we build trust into the AI world? how to deal with all the AI agents and how Can We Deal With Testing & Quality in The AI Era, and now enjoy the episode! Hello Henry nice To Have You On The Show Here.

Hey, thank you for inviting me. It's really good to be here and all the cool props on everything that you have it's great. Yeah Thank You very much. And Zillac said To Me you Have to Invite Henry.

he was in Episode one. I think of podcast Okay? Now when He Says i have to invite somewhere. sure i will follow That.

i would like to thank Zilla But also your talk. so i know that its worth that you are Here. Very fun that me and Zilla did the talk together, because we kind of had this like a old test thing. Maybe if young AI is I'm not that young anymore but still it's they'll kind of like.

We have nice report going on And you're gonna do this again A couple more times so It's gonna get even better. So i really enjoyed. Yeah as well was great topic Because its'so important now. You talked about AI engines Especially About The Trust Thing.

Let's go into it. What is the problem? There are a lot of problems, but I think that the main ones have kind like this same problem we had with humans and everything else on there. But now because its an automated system We really need to solve these problems Because when you do it faster You multiply your mistakes And you multiply your successes.

So building systems where we can actually trust in AI In the way i can trust my colleagues and everybody else Is very important. Now with the current AI systems, we want to talk about systems and not just models. That you want a system around an AI model. be that source of trust?

You have a process, checks and balances, guardrails... And there's lots of stuff built around it! You go back to basics like the Oracle problem or communication problems. how do you communicate what I want?

The same problem with humans as well. Now you have to do the AI and also their Oracle. side of the problem is that how Do I make sure? That this stuff that comes out it's actually true or he's actually what i want?

Yeah, so those two things How would they build that? if you can answer those then you have the trust that we're looking for. That's a problem with humans too, as you mentioned. so yeah but you say...

we have to deal with it. what do I have? Do you have concrete example of this problem how its shows up in reality? Yeah if think about like you ask AI agent take a generic like chat gpt or something, you say that hey I'm small business owner.

i am doing this in software can make me a business strategy and of course from there on get the very generic business strategy. what do you expect to be? You have all points in it nicely done but then offcourse thats what they model does. its only predicts if doesn't think it doesn't analyse but works magically in that way.

Of course we got generate one. it doesn't have probably what you exactly have. It doesn't know what you want or, might be totally wrong. You are in a different country but didn't realize to say the hate.

actually I'm working Germany so take Germany into account But i am working Finland and selling this other countries. So take that into account Because typically when people interact with AI They kind of used to fix my code now, and then they are angry that why didn't this work? But you get the same experience with developers or anybody else. The production is broken, fix it!

Okay, it would be nice to know what's broken. Can you get me the logs? can help we figure that out? where do I find out The actual complaint?

What was broken? You know how to write a proper bug report in a concert. So this is the same communication problem now with AI than all bug reports and all of That. How do you convey actually what Do you want?

yeah because because as we must We've filled A lot Of those gaps by our experience pretty much. but then off course the systems don't have Experience And your not working With kind of pros But an AI model to computer. So you have to figure out that your prompt is good, your requirements are good and all of this that you're giving the system is actually good and specific enough so that the system can work. And I think it's a communication problem we have with humans.

We encounter daily in our job The other side... Well when talking about Oracle problems how do they know if what we did was right? and the AI model actually outputs something. And this might get really philosophical if you really dig into what's right in a company context, is it just that?

hey my boss asked for this? That may be totally valid reason to do it yourself. of course I'll do because my boss asks me maybe not the best idea but hey i was asked to do then how im measured so thats where i got pay from Or then the company will make money if this feature works or something like that. So you see these problems in everywhere.

and how do you build, or codify... these best practices now into your AI system? is what we have to do.. to build that trust of those systems as well.

And Is it possible? Can can be done with the AI systems from nowadays? I think so. The technology's there.

I can give the AI system my credit card, access to accounts and chat. All personal chats no problem but somehow i have trust that this system does good things with it. How do you do? Q&A testing validation all typical tools we already had.

Those are pretty much the same problem. You subcontract a piece of software from somewhere, how do you know that they've delivered what you want? Yeah! Do end-to-end testing or do acceptance testing...

or some kind validation? where did we get what wanted? and then it might fail because.. you actually didn't deliver proper specs.

so again communicating wrong things Or when in fact they didn't delivery Where can prove this isn't exactly what we wanted? So the same stuff happens. But in reality, I think it's hard for people to do their Q&A stuff with AI because they're such a fluffy unknown... Yeah.

And that is also part of the problem seen now with AI. That might be talking really technical about AI and how the matrices go How probabilities calculate. Somebody just started asking if there was an AI recipe for cake. When you talk about AI currently really hard to know at which level are we talking about and how does it impact me or what's going to happen.

And I think that, We also talked a little bit about the whats holding us back. is there lot of fears on. why do i fear making mistakes? Do I fear for my career that AI will take my testing career out from me?

those fears now can dominate. I cannot speak. Dominance mode kind of like now. that first, because it's a big change.

It is huge and thats why i think its interesting. but Its totally natural to be afraid at the level of change Of course. Yeah yeah Thats true Because there are huge impact in all over world Especially testing QA. But i think it will raise up qa To become much more important than just programming.

What do we have to do? Additionally, or special with AI what we are not doing with our software nowadays. Yeah good question I think it comes down a lot to repetition and doing like. when you think about software testing is typically very What's the word?

Like You input something and you always get the same answer. So what's the world for that? It's deterministic. yeah so testing is easy because well Software should be deterministic and if it isn't then you know, You have a problem.

but with AI models of course being non-deterministic. You Have to kind Of like shift your thinking in testing also not running Like. Is this green? No!

You're looking at over time...you are Looking At trends..you Are looking at like mean Time between failures. It's the same thing that machine shops airplane like these guys do That they're Not looking just one point test in a sense, but they are looking at trends.

So I think that's also going to change a lot on how we look at testing that you just cannot run a single test say it is okay and then go for because there will be Black Swan events. type of stuff happens all these models Because You Cannot Predict It. And Also Of Course The One Thing That We Really Have To Think About Is Not Just about the Testing and Validation But Building These Systems is that the models change underneath because companies update it. And how do you manage a system where components constantly keep changing, so to figure out some kind of stability and something too?

actually deliver something. I think that's a real change also in how we see software testing because typically, besides all the safety stuff... We have this case where you just see our expected result at screen okay? And it doesn't work because there is another quality.

now Yeah, I think that's maybe even like the separation between validation and testing kind of what's going on. And i think terminology will evolve as well. What are we talking about to facilitate it? We can more clearly indicate whether you're even testing or whatever You've been doing here.

That's gonna be also a big an interesting change in their field at all yeah Or Like As he said It's Gonna Be. I Think it's gonna Be much More About BDD is more up Much More About TDD stuff because that's one. How do I write down what i actually want in a proper way, make that into some kind of acceptance criteria. And then basically because code is going to become more and more black box Because you just let the AI generate an application for you.

Yeah yeah. Then their only source of truth Is your test and spec. If it passes this thing specified It's fine. The software think its fine without talking into the code.

Yeah, I like that idea. you mentioned BDD and TDD because often it's just used as tools to get something done with testing or test automation but in reality this is something we have got a same understanding of. what they are doing there As your mention. We need to do it with AI too So we need more information that the eyes know what it should do.

And I think one of the big things also is going to change, maybe too much spec in the beginning because everybody's gonna overreact and write huge specs then end up in very... In the same problems with code. now have millions of lines of spec which should probably be conflicting and maybe the systems, that's why behave in strange ways. And you're going to start de-packing I think your spec more than your code actually at this point.

so That is also gonna be an interesting change. The abstraction level rises quite much. So can i even... Because how do we write a spec for let us say salesforce?

It´s huge system. Please write me open. what does it do? I have no idea how to do that.

Yeah, yeah! That's true. but i think people get annoyed about it because if we want with AI... We wanted all very fast and quick.

just give me. And now i have put a lot of information had write down on the stuff and put into AI ...I think the people don't like it. But people don't like work.

Of course, if I can just say to my development team fix the problem and then go get coffee of course that's better than hey... I figured it out. It is this function in here. i found out This actually a problem.

Can you fix this one? And maybe change it to be this way. Yeah.. This more worked on first option.

Of course I want the first one! That was good shift in the view. How can we get that into our teams, this idea of doing that? That's a really good question.

But I think it also somehow naturally happened because in my career doing programming and doing stuff at the university are now testing on quality. I haven't experienced things like people from for example marketing come to me saying you're the guy with AI who could help us with these problems. Yeah, I like that hasn't happened with software. Hey you are the software architect.

could You help us solve this problem? yeah That hasn't happen. but now a day and you get a lot of these like inbound. hey This is how really cool.

we want to be part of it. And can you help us with this? yeah, and this way i also think that much Of the change is gonna come not from they're Like IT. It's coming up on the outside.

where there kind of like value is because who don't have this History of writing code or being deeply knowledge about my personal identity Being i'm sql expert which sadly I think is now going to be a less and less relevant skill set. But like, hey! My marketing...I need this tool actually track our clients and figure out what's actually going on of it.

just much closer the value with dangerous word but i think we can use it here. that close to the value creation at point up. yeah business thing they're doing in the softwares gonna enabling that is a much more, let's say approachable enabler because you don't have to know magic runes of JavaScript or Python. You can just prompt it and it democratizes the computers.

I really like that. Yeah, I think this is one big point that now everyone can build software more or less. But quality question. We don't know it and we as testers...

One of our main skills why are testing? Is when you're building trust into software what do you think? how does changes in the AI world get its most important for us to be the same? What's your opinion on that?

It also shows a lot of the biases that we have. I'm actually doing a little bit research on my PEG topic about how we've done major business decisions just by doing software, because if you didn't realize some coder has made major business decision like locking us to Azure or one day. Nobody even thought back in today. and hey great!

We got application done but it affects everything. so big thing is going happen now. We have to understand a little bit of the business side as well and figure that out. And how do you make sure in many steps, we are building right things?

Again it comes down to making sure there is some kind of platform for doing this so they can have a single source of truth about what your company does. That's also going be interesting. You all have to start writing down. what do you think we actually are going to do?

Because AI is gonna be the number one interface. So that's an interesting change, figuring out how to communicate it with everybody and everybody like hey! We've got this project AI which knows about all of our documents on projects. Ask him...

ask the AI for questions when he was onboarding For example. And then in other side as well. How can I make sure they're right? We need to build systems again on a platform or some kind of system to check like multiple AIs.

discussing this, that is actually a good thing. Some of these problems are kind-of. you hear them and think they're easy. but for example You might have SharePoint where we had twenty different versions of the document which was right one.

It's super difficult problem when thinking about it? When did lot context? Did we send this to client? Which ones did we sent Have improved afterwards?

And how do we figure out? It's now surfacing a lot of these problems that humans have solved. The problem we had already and didn't solve in the last game! We can solve them with AI, but they're bubbling up to the surface saying actually you need something about this.

so I think it is also quite cool. What do you think? As testers as quality people what are our skills today? how should we evolve that work.

I think we are in a very low position, but i think the testing skill set and mindset is in a good position for AI because as said it's much more about hey what are criteria they're actually doing? how can you write this into spec? How do we write these things to test case ? How could be verify what were doing so that baseline already really good compared many other places in IT Because thats basically main interface to interface with these systems.

and how do you design them? Yeah. And on top of that, kind of like start figuring out How the build this kind of Statistical testing system such as it's not your know make test go green type Of a thing they're very care about one single test. but These trends and how they go and how Do You Not just work As a tester in Your own small box But Like Expand your skillset in many other places In The whole Thing?

because I think Testing and validation is gonna eat parts of DevOps is going to eat part of programming as well. So expanding in those directions, I think it's gonna be something that's very valuable for you as well. and what we've heard talks here from Robb on many others talking about teach people multiple roles so they'll make a better tester because then have more context. yeah the same thing with AI if there are more contexts.

but Well, of course I'm biased because i'm a tester as well. But the skill set is already very suitable for the new world and that's why I wouldn't be scared. The number one fear that I've tried to allay today too many people That AI isn't going take your job. They're not gonna automate you.

It's going to augment you and the way that you work. And there is a lot of work done around this area, so... I think it would be nice if we have also this mission or this vision for our community! Henry, thank you very much that you were here and gave us your insights from the talk.

It was really great! I appreciate it. And yeah i wish a good rest of the conference. It's now sliding in the finish here...

and hope we see as soon at another conference or maybe next year? four weeks, we're going to be an automation start doing the same thing again. so version two point zero. So it means that I'm gonna be there and continuing the journey of telling this too as many people as you can!

Great to hear us yeah thank you very much. have a good time.

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