What AI Agents mean for Engineers: Alexey Tulia on Ownership, Safeguards and Technical Judgment

As AI takes over more routine coding, engineers are expected to spend less time writing code and more time owning what that code does in the real world, according to Alexey Tulia, Executive Leader at Coinspaid Dev.

As reported by Coinpedia, Tulia shared this view during the AI Impact in Engineering panel at Tech Race Summit 2026 in Warsaw. The discussion dealt with a shift that many technology companies are only beginning to face. AI today mostly helps with drafting and analysis, but the next generation of agents will be connected to live systems, including sensitive data and deployment pipelines, and will be able to take actions there directly.

For students and working engineers, the practical consequence is a change in what counts as good work. Tulia expects that by 2029 AI will generate most production code and that smaller teams will manage larger areas of responsibility. When machines produce code quickly, the scarce skills become understanding the business problem and verifying that the output behaves correctly once it reaches production. He suggested that leaders should give teams business context and a clear expected outcome, and then judge productivity by correctness, maintainability, security and operational performance. The number of lines written tells very little about the value delivered.

The same logic applies to the agents themselves. “The more authority we give machines, the more important accountability becomes,” Tulia said. He described a situation that is close to reality for many teams: an agent that can prepare a change and deploy it to production. Two questions follow immediately. Should the agent be allowed to release it without a human approving it, and who is responsible if the deployment fails? In his view, the organisation should answer both before granting access. That means setting permission controls that limit what the agent can do, keeping audit logs of its actions, having a reliable way to stop it and being able to recover quickly from a broken release. The wider industry is having a similar conversation. Anthropic CEO Dario Amodei has called for slowing capability development so that safety work can catch up, while Tulia focused on the limits a single company should set for its own production systems.

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Preparing for this future starts with foundations that make change safe. Tulia advised CTOs to connect AI spending to a concrete need inside the organisation. His priorities include strong APIs and reliable data, supported by automated testing, observability, security and flexible architecture. Work on architecture and on reducing vendor lock-in may bring little revenue in the short term, but it makes it far easier to switch providers or redesign a system when assumptions prove wrong. He also warned against roadmaps that use up all available capacity, since teams then have no time to experiment with emerging tools or react when priorities move. “I don’t need to predict the future perfectly. I need to make being wrong cheap,” he said.

For the CTO role, the conclusion is that technical depth still matters, combined with a solid grasp of the business. As building technology gets easier, companies will bring in more vendors and more AI-generated systems, and someone has to evaluate them. “I think technical judgment becomes even more important,” Tulia said. Coinspaid Dev, where he works, is an independently owned software engineering company specialising in blockchain.