Stop the Bleed: 8 Ways to Prevent Replit Agent from Burning Through Your Budget (www.weavy.com)

Replit Agent users are increasingly reporting runaway bills — from dozens to hundreds of dollars per incident (one developer lost $3,000) — when the agent gets stuck in infinite “fix” loops or runs many costly iterations. This piece lays out eight practical defenses to prevent those budget disasters, emphasizing that cost control is now a core operational concern for anyone using LLM-driven builders. The stakes matter to the AI/ML community because agentized workflows can pay per-token/runtime mistakes, so prompt quality, execution oversight, and integration choices directly translate to dollars and development velocity. The recommended tactics are hands-on and technical: review the Agent’s Plan stage before execution and stop runs that show no visible progress (set a 15–20 minute mental timeout); tighten prompts (example: give exact error, file, and what not to change); test after every iteration and use an external LLM (ChatGPT/Claude) to cheaply audit or triage issues; prefer prebuilt components (e.g., Weavy) for complex collaboration, avoiding repeated WebSocket/persistence failures; require a “proof of work” summary (files changed, why, verification steps); and adopt real Git-based checkpoints instead of relying on Replit rollbacks. The upshot: with prompt discipline, short feedback loops, external audits, and standard engineering safeguards, Replit Agent can be productive without becoming an expensive experiment.