Inside an AI-native engineering organization

Taito.ai, an eight-person people operations startup with four engineers, uses an agent-centered software development workflow in which engineers rapidly create disposable end-to-end prototypes, promote only promising ideas to a team-reviewed implementation plan, build features largely through agents, and then divide work into pull requests mainly to focus human review on sensitive changes. Its agents handle feature coding, bug investigation, testing, CI failures, and automated reviews, while deterministic checks and review rules flag areas such as authentication, authorization, personal data, secrets, and complex migrations for human attention. The company reports that its Fixer agent reduced its backlog of unresolved reported bugs to zero and resolves about 70% of reported issues within 30 minutes, largely by addressing small problems that previously lacked priority. Taito treats repeated review feedback and coding conventions as maintained “skills” for agents, supported by extensive automated tests, sandboxed permissions, local Kubernetes environments, and CI pipelines. Humans remain responsible for architectural understanding, high-stakes trade-offs, security and customer-impact decisions, and close review of foundational systems, while the company acknowledges that its approach is easier for a small, young organization without substantial legacy software and that the appropriate balance between agent autonomy and human review remains unsettled.

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