Building Trust with AI Agents - Henri Terho

How to build trust into AI systems when they constantly change underneath you

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"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. He has played a key role in fostering community-driven innovation, having served as chairman of Tampere Entreprenourship society and co-founding Tampere Tribe to support local startup culture. Henri’s passion for AI, quality assurance, and rapid software development is evident in both his industry work and ongoing PhD research on agile product innovation. He frequently shares his expertise on stage and in publications, championing lean practices and the latest AI advances to empower organizations worldwide.

Highlights:

• AI models only predict, don't think—trust requires building validation systems and guardrails around them.
• Testing must shift from deterministic green/red checks to monitoring trends and statistical validation over time.
• Communication problems with AI mirror human ones: vague prompts fail like vague requirements always did.
• Testers' skill set—writing specs, defining criteria, verification—is perfectly positioned for AI-driven development.
• AI democratizes software creation but surfaces old problems: conflicting documents, unclear specs, poor documentation.