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Following2026年华沙Tech Race峰会：AI工程师与CTO的角色演变
Coinspaid Dev执行负责人Alexey Tulia在华沙举行的2026年Tech Race Summit“工程领域的AI影响”专题讨论会上，深入探讨了工程师及首席技术官（CTO）角色正在发生的转变。他的发言核心聚焦于向能够直接通过公司基础设施执行操作的AI系统过渡。
目前，企业已广泛利用AI辅助文档起草与分析工作。下一阶段的关键在于将AI智能体（Agents）接入实时系统——从敏感数据到部署管道，使其具备实际执行能力。这种访问权限的开放引发了关于权限控制及决策责任归属的重要问题。
当AI能够在生产环境中执行操作时
Tulia指出：“我们赋予机器的权力越大，问责制的重要性就越高。”
试想一个能够准备并部署变更至生产环境的AI智能体。在没有人类审批的情况下，是否应允许其自主执行？若部署失败，谁应承担最终责任？
在授予此类访问权限之前，组织必须建立严格的权限控制和审计日志机制。同时，必须具备中止智能体运行以及在部署失败后恢复系统的能力。这些要求共同印证了Tulia的核心观点：在生产环境中获得更高的自主权，必须以明确定义的权限和人类责任为前提。
投资于“可变更性”的能力
Tulia recommends that CTOs closely align AI spending with specific organizational needs. The infrastructure priorities he highlighted include strong API interfaces and a reliable data foundation. Automated testing, observability, security, and flexible architecture provide the necessary space for enterprise security to introduce new technologies.
The engineering team also needs to reserve capacity to conduct experiments. If the project roadmap drains all available resources, the team will have little room to test emerging tools or respond when priorities change.
While architectural optimizations and reduced vendor lock-in effects may not immediately lead to revenue growth, they can ensure that companies can more easily replace service providers or revise systems when assumptions change. "I don't need to predict the future perfectly, I just need to make mistakes cheap," Tulia said.
Engineers are accountable for results
AI is accelerating the process of coding and prototyping. Tulia believes this should leave more room for engineers to deeply understand business issues and track results until they are implemented in the production environment.
Leaders can support this transformation by providing the team with a business context and clear expected outcomes. Subsequently, productivity assessments should shift from code volume to correctness, maintainability, safety and operational performance.
Looking forward to 2029
Tulia expects that by 2029, smaller engineering teams will manage a larger range of areas of responsibility. He also predicts that AI will generate most of the production code, increasing the importance of verification capabilities and technical judgment.
CTO角色将继续要求深厚的技术专长与商业理解的结合。随着技术创建门槛的降低，更多的供应商和AI生成的系统将进入企业内部。“我认为技术判断力变得尤为重要，”Tulia表示。
对于工程领导者而言，当前的紧迫任务是在AI智能体获得关键生产系统访问权限之前，明确界定保障措施和责任归属。
