Before AI Agents Touch Production: Alexey Tulia’s Case for Cheap Mistakes and Clear Ownership

Giving an AI agent the right to change live systems is less a technology decision than a question of who stays responsible. That was the core of Alexey Tulia’s remarks at Tech Race Summit 2026 in Warsaw.

According to Hackread, Tulia, Executive Leader at Coinspaid Dev, joined the AI Impact in Engineering panel to discuss how engineering roles and the job of the CTO are shifting as AI systems gain the ability to act through a company’s infrastructure. His view of the near future starts with money and architecture, then works its way toward accountability.

Why the next budget should buy flexibility

Tulia’s advice to CTOs was to tie every AI investment to a specific organisational need. The areas he prioritised are foundational: well-designed APIs, reliable data, automated testing, observability, security and an architecture that can be reshaped without a rewrite. These are what allow a company to introduce new technology without putting existing operations at risk.

Some of this work, such as reducing dependence on a single vendor, brings in almost no revenue at first. It pays off when a provider has to be changed or a system redesigned because conditions shifted. Tulia summed up the logic in one line: “I don’t need to predict the future perfectly. I need to make being wrong cheap.”

The same thinking applies to people’s time. If a roadmap fills every available hour, the team cannot experiment with an emerging tool or adapt when the business changes direction. Leaving capacity for tests is part of the investment.

How the engineer’s job is changing

AI already speeds up writing code and building prototypes. Tulia sees that speed as an opening for engineers to go deeper into the problem the business is trying to solve and to follow their work all the way into production, where it either delivers or fails.

For this to happen, managers have to supply the missing context: why a task matters and what outcome is expected. Measurement changes too. Output volume says little when an assistant can generate thousands of lines in minutes, so Tulia pointed to correctness, maintainability, security and operational performance as the qualities worth tracking.

When an agent can deploy

Most organisations today use AI to draft and analyse. The next step connects agents to production environments, including sensitive data and deployment pipelines, so they can carry out tasks on their own. The debate around agentic AI has grown sharper this year, with voices inside the industry arguing that capability is moving faster than safety research. Tulia brought that concern down to the level of daily operations.

“The more authority we give machines, the more important accountability becomes,” he said.

He illustrated the problem with a familiar scenario. An agent prepares a change and is technically able to release it. Does it need a person to approve the release? If the deployment breaks a service, whose failure is it? Before such access is granted, Tulia argued, a company should be able to answer yes to four questions:

1. Can we limit exactly what the agent is allowed to do?
2. Is every action it takes recorded in an audit log?
3. Can we stop it immediately if something looks wrong?
4. Can we roll back and recover from a failed deployment?

Without these controls, autonomy simply moves risk around. With them, authority is defined and a human remains answerable for the outcome.

Read: Why Modern Brands Are Moving Away from Basic Warehousing to Value-Added Logistics

A smaller team with a wider remit

Looking toward 2029, Tulia expects engineering teams to shrink while the scope each one covers expands. He also anticipates that AI will produce most of the code running in production. In that world, the scarce skill is checking whether generated work is right.

“I think technical judgment becomes even more important,” Tulia said.

He does not expect the CTO role to become less technical. It will still demand deep engineering expertise, paired with a solid grasp of the business, and it will involve managing a growing number of vendors and AI-built systems as creating technology becomes easier. The practical takeaway for leaders is immediate: decide who owns what, and put safeguards in place, before agents are connected to anything critical.

About Coinspaid Dev

Coinspaid Dev is an independently owned and operated software engineering company specialising in blockchain infrastructure. It employs more than 120 engineers and has over 11 years of industry experience, combining software engineering, infrastructure, security and R&D teams that build distributed systems for more than 20 blockchain networks.