New projects in @triggerdotdev now open with a "Copy AI agent prompt" button. Paste it into Claude Code or Cursor and your agent goes from an empty repo to a task that's registered and verified in the dashboard. How it works ↓

Setup a Smart column to point at a run's payload, metadata, or output with a JSON path: $.𝚘𝚛𝚍𝚎𝚛.𝚝𝚘𝚝𝚊𝚕 as a number $.𝚌𝚞𝚜𝚝𝚘𝚖𝚎𝚛𝙸𝚍 as text $.𝚜𝚝𝚊𝚝𝚞𝚜 as a badge Give it a label and it fills in down the whole list.

You can now show, hide, or reorder columns on the @triggerdotdev runs list. You can also add smart columns that pull a single value straight out of a run and display it for every row. ↓

Every environment in @triggerdotdev used to authenticate with a single secret key. Now each environment can hold as many keys as you want. Every service, CI job, and integration gets its own scoped credential. ↓

You can run bulk cancel and replay straight from the SDK. Point 𝚛𝚞𝚗𝚜.𝚋𝚞𝚕𝚔.𝚛𝚎𝚙𝚕𝚊𝚢() or 𝚛𝚞𝚗𝚜.𝚋𝚞𝚕𝚔.𝚌𝚊𝚗𝚌𝚎𝚕() at a filter and it hits every matching run, from a handful to millions. ↓

New in @triggerdotdev: the health report. One command tells you if your project is actually healthy: – is work starting? – are runs succeeding? – is your telemetry fresh? 𝚝𝚛𝚒𝚐𝚐𝚎𝚛 𝚛𝚎𝚙𝚘𝚛𝚝 𝚑𝚎𝚊𝚕𝚝𝚑 ↓

Define your prompts in code. Override them live when you need to. On @triggerdotdev a prompt is an id, a model, typed variables, and a template, and every deploy creates a new version. Override the text or model from the dashboard, no redeploy ↓

The MCP server for @triggerdotdev does a lot more than deploy and trigger tasks now. Your coding assistant can chat with your deployed agents, manage prompt versions, and pull a health report. All from Claude Code or Cursor. ↓

You could always see what a task did. Now you can see what it spent. Every LLM call your @triggerdotdev tasks make becomes a span in the trace. One flag, 𝚎𝚡𝚙𝚎𝚛𝚒𝚖𝚎𝚗𝚝𝚊𝚕_𝚝𝚎𝚕𝚎𝚖𝚎𝚝𝚛𝚢, on any Vercel AI SDK call. Nothing to install. Whole AI bill, or any single call, in the trace ↓

Every turn is a span in the dashboard: the prompts, the responses, the tool calls, how long each took. An AI metrics dashboard ships with every project too: spend, tokens, latency percentiles, cost by model. No instrumentation to write.