At Kilo Code, engineers now spend only 1% of their time coding, with agents handling the rest, prompting new questions for dev teams regarding system safety, model cleanup, multi-model architectures, and justifying soaring token bills. Tech leads view this as a natural evolution as agentic AI integrates into enterprise workflows. While agents excel at greenfield development, human involvement is crucial for brownfield tasks and making strong product decisions. Replit uses agents to review pull requests, assigning risk scores and self-merging low-risk ones, emphasizing a “human on the loop” approach. They utilize a fleet of secure agents in cloud VMs for end-to-end task execution, which once resolved a complex bug that stumped human engineers. Multi-model support is becoming essential, with Kilo Code offering over 500 models, allowing companies to switch models based on cost and project phase. Replit also makes model choices on behalf of users to optimize cost and capability. Managing runaway AI costs is a concern, with companies implementing strategies like using expensive models for planning and cheaper ones for execution. Symbotic sets per-month cost caps for employees, using tools to track usage and adjust tiers. Replit found significant AI spending outside engineering, highlighting the need for visibility, model routing, and sensible defaults, as most tasks don't require frontier models. Ultimately, the focus is on ROI, measuring value by metrics like cost per pull request rather than just spend.