LinearB’s Post

Adoption was last year's question. Operationalization is this year's. 96% of orgs are already using AI agents. 97% are exploring system-wide agentic strategies. The pilots ran. What stalls is the next step: getting agents to behave reliably inside enterprise environments where APIs change without warning, data is messy, business rules conflict across systems, and the agents themselves are non-deterministic by design. In this Dev Interrupted guest article from Luis Blando (CPTO, OutSystems) makes the case. Production environments are structurally hostile to unchecked autonomy. What scales is bounded autonomy: orchestration, evals, tracing, and control. The leaders who pull ahead in the agentic SDLC don't chase capability. They build measurement loops around it: task success rate, human override rate, rollback frequency, groundedness. Then they iterate inside controlled operating boundaries. Same instinct we've been making the case for: activity signals aren't outcome signals, and adoption isn't the same as impact.