What changes when AI agents use your software

Daytona cofounder Ivan Burazin argues that AI engineers will increasingly need to build reliable, secure tools for agents, whose practical failures often stem from access and integration problems rather than model capability alone. He emphasizes that agents should operate with distinct, delegated identities rather than a user’s full credentials, using scoped permissions, expiring grants, auditable actions, and trusted integration layers that keep secrets outside model context. The discussion also highlights the need for clear tool contracts that distinguish authorization errors, service failures, accepted jobs, completed tasks, and uncertain outcomes, particularly when retries could create duplicate or costly operations. Agent reliability should be evaluated through repeated trials in resettable environments, with defined success criteria that account for correctness, timing, cost, security, refusals, and recovery from dependency failures. Although Burazin’s prediction that agents may become dominant users of software remains speculative, teams can already test whether agents can complete real customer tasks safely and independently, then improve interfaces based on measurable workflow outcomes.

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