Vibe Coding: Closing the Feedback Loop with Traceability (blog.sentry.io)

Developers embracing “vibe coding” with tools like Cursor and Claude Sonnet 4 are hitting a predictable blind spot: LLMs can write and refactor code but can’t see what actually happens when that code runs. The article argues that adding execution visibility—Sentry traces and telemetry—into an LLM agent’s context via an MCP (Model Context Protocol) server closes that feedback loop. By feeding trace/waterfall data (for example, span timings showing a slow db.select.library_entries) and specific trace/span IDs back to the agent, you let the model verify which functions ran, measure performance, and identify errors or missing steps instead of iterating blindly. Practically, the workflow is: create a persistent plan doc, have the agent generate code, deploy to staging with Sentry instrumentation, then ask the agent (via MCP) to compare trace data against the plan and propose or implement fixes. Sentry’s hosted MCP and its Seer agent can automate incident-to-PR flows, generate tests, and review patches, enabling an RL-like loop where agents observe outcomes and improve. For the AI/ML community this means agentic development becomes execution-aware—improving reliability, observability, and automation while keeping human-in-the-loop guardrails (CI, tests, linters) to prevent cascading failures.