AI Coding Agents Now Write 99 Percent of Code at Some Shops. The Bills Are Piling Up.
At Kilo Code, engineers read or write code themselves about 1 percent of the time. The rest is handled by AI agents. The statistic, shared by co-founder Emilie Schario at VB Transform 2026, captures a shift that is no longer theoretical: agentic coding has moved from experiment to default at a growing number of engineering organizations, and with that shift comes a new set of problems that few teams have fully solved.
The panel brought together engineering leaders from Replit, Kilo Code, and warehouse automation firm Symbotic to compare notes on what happens when agents take over the commit log. The consensus: agents are remarkably good at greenfield work but stumble on existing codebases, the token bills are substantial, and the biggest unspoken challenge is figuring out who is accountable when an agent gets something wrong.
Jared Go, distinguished engineer for AI and cloud at Symbotic, described his team's approach as funneling agent output through a checklist of security, elegance, and correctness criteria. "Greenfield is so easy for agents," Go said. "Brownfield we all know is where the actual challenge lies." His team found that agents make weak product decisions farther down the development chain, which is where human judgment still carries the load.
Replit has taken a more structured approach. Amol Jain, head of product engineering, described an internal system where an agent reviews every pull request and assigns a risk score. Low-risk PRs self-merge. Higher-risk changes go to human reviewers. "The idea was human on the loop, not human in the loop," Jain said. He characterized Replit's internal tooling as "self-driving for software engineers" -- developers hand a task to a fleet of agents that run in cloud VMs behind token proxies, handling end-to-end planning, implementation, and testing.
Jain offered a specific example: an engineer could not reproduce a deep, gnarly bug. The task was handed to an AI manager agent, which told the original agent to go to sleep, then spun up a group of sub-agents that traced the issue. It then launched more agents that found the fix. Six hours later, a working pull request was ready for a bug that had stumped the human team.
All three panelists agreed that model lock-in is fading. Kilo Code supports more than 500 models through its gateway. Schario argued that "your software that you're using to do agentic engineering should be decoupled from the model that you're using to do it" -- a position that reflects the broader industry move toward multi-model routing based on cost, capability, and task fit.
The panel did not paper over the cost question. Token consumption from always-on coding agents is rising fast, and engineering leaders are now asking whether every agent invocation translates to real productivity or just burned budget. The answer, for now, depends on how well teams meter access and how clearly they define what they are willing to hand off.
Sources
VentureBeat: AI coding agents are blowing through budgets -- Replit, Kilo Code, and Symbotic explain how they're managing it
