Recent changes and improvements to Dash0

AI SDLC Insights shows what your coding agents cost and how assisted pull requests move through review, but only in aggregate. When a number looked off, you could not see which pull requests were behind it, or what the agents did on any one of them.

The new Pull requests tab, next to Sessions, lists every pull request across your repositories. Each row shows the repository, author, type, state, reviewers, and how long it has been open, and the list follows your time range like every other tab.

What's new

AI sessions per pull request: Click a pull request to open a side panel with the AI sessions that worked on it, the models they used, their tokens, and what they cost. One click takes you to those sessions in the Sessions tab.

A heatmap you can select from: The chart above the list plots pull requests by AI cost or by time to merge. Drag a box over the cells that stand out, such as the most expensive pull requests on one day or the merges that took more than a week, and the list shows only those. Keep the selection as filters and it survives a reload or a shared link.

Status filter: List the pull requests that are open, awaiting review, draft, merged, or closed.

From a chart to what's behind it: Click a chart such as investment distribution, cycle time, or AI penetration and choose Explore. The Pull requests tab opens on the pull requests that chart counted, with your filters kept. The coding charts work the same way: click a model, skill, MCP server, or team and Explore opens the Sessions tab on the sessions it counted.

Totals at a glance: Switch the chart to Stats for total pull requests, AI-assisted pull requests, and total AI cost across the whole list, or hide it to give the list the full page.

Open any span and the full trace streams in instantly, even at tens of thousands of spans. Search by name, filter by type or errors, and see logs and span events directly on the timeline.

Dash0's AI SDLC Insights now supports GitHub Copilot App and OpenCode, giving teams more complete visibility into AI coding activity across their development tools.

With these new integrations, AI coding sessions from both environments are included in Dash0's cost tracking, developer adoption metrics, and AI-assisted pull request analysis. Whether your team uses Claude Code, Cursor, Codex, GitHub Copilot, or OpenCode, you can track AI usage and its impact in one place.

To get started, install the Dash0 plugin for GitHub Copilot App or add @dash0/agent-plugin-opencode-v2 to OpenCode. Run /dash0-configure to connect the plugin to your Dash0 account. OpenCode requires a reload before and after configuration.

Check out the Integration Hub for detailed setup instructions for GitHub Copilot App and OpenCode.

Your users feel every failure, including the ones deep inside your network. An internal API times out, a private payment gateway slows down, a service behind the firewall stops answering, and the first sign is a customer complaint. Public synthetic checks can't see any of it, so those parts of your system go unwatched.

Private locations bring synthetic monitoring inside your network. The checks you already run against public endpoints now run against internal services too, with the same results and the same notifications when something breaks. Your whole system is covered in one place, instead of public services in Dash0 and everything else left to chance.

Each private location shows at a glance whether it is connected. Run it in more than one place, and your checks keep going when one of them goes down.

Read the documentation to set up your first private location.

AI SDLC Insights shows what your coding agents cost, who uses them, and how assisted pull requests move through review. Turning those charts into a decision was still your job: which number matters this week, and what should you change because of it?

The Overview tab now has a Recommended actions card, right under the headline numbers. It reads the measurements on the page for your time range, filters, and percentile, and turns them into two to four findings. Each finding fits on one line: a Risk, Watch, or Opportunity tag, what it found, which tab the evidence is on, and the action to take.

Click a finding to open it in a side panel with the recommended action, why it matters, and the measurements it is based on. Every number the card quotes is one you can check. The tab tags and "See it in" links take you to the chart behind a finding with your range, filters, and percentile intact, and "See what data was used for these recommendations" lists every measurement the card was written from, with the ones behind your finding highlighted.

The recommendations stay the same while your numbers do, and refresh when something actually moves, so the card does not reword itself every time you open the page. If a finding helps, or misses, rate it with the thumbs on its row. That feedback goes straight to the team improving them.

The Overview now reads in order: where things stand, what to do about it, and the detail behind both.

Until now, asking Agent0 about AI SDLC Insights did not get you far. Agent0 could not read the page's data, so it either said it had nothing to go on or pieced together numbers that did not match the cards in front of you.

Agent0 now reads AI SDLC Insights directly, from any page. Ask what your coding agents cost per model, whether review got faster than in the previous period, which pull requests are still waiting for a first review, or what happened in a specific coding-agent session. The answer comes from the same data the page shows and says which time range it covers.

On the AI SDLC Insights page, the Agent0 panel also knows what you are looking at: the tab, the time range, your filters, and the percentile you picked for the duration cards, so a p90 view gets p90 answers. Open a coding-agent session and it knows that too. Each tab offers three suggested questions of its own, such as "What is driving our coding-agent spend?" on Cost or "Which stage slows our pull requests down the most?" on Productivity, and an open session adds three more, like "Where did this session's cost go?"

So when a number on the page looks off, you can ask Agent0 why and get an answer that starts from the slice you are already looking at, not from scratch.

Pin any page and it moves to the Pinned section at the top. Hover a collapsed section to open its pages without expanding it.

Agent0 threads, your conversations with Agent0, now keep running when you open another thread or leave the page. Until now, switching away meant losing sight of the thread Agent0 was still working on, so you waited or kept going back to check.

The bottom bar, along the bottom of the screen, lists your running and recently finished threads. A spinner marks a thread that is still working, and a dot marks one that finished while you were somewhere else. Select a thread, or press ⌘+Shift+9 (Mac) or Ctrl+Shift+9 (Windows/Linux) and use the arrow keys, to return to its answer. While the page stays open, the prompt you had not sent and any follow-up you queued are still there. Removing a thread from the bar does not stop it. See Run Threads in Parallel.

As Dash0 grew, borders, panels and navigation started to compete with your dashboards, traces and logs for attention. The new layout uses spacing and surface levels instead of lines to group elements, so each page has less noise. Nothing has moved, so you don't need to relearn anything.

Until now, anyone who could read a dataset could change or delete every automation in it.

Now only admins and the people you share an automation with can edit or delete it, and everyone who can read the dataset can still read it. Select Share on an automation's page to give edit access to a member, a team, or every member. In Settings, each team and member page has a new Automations tab that lists the automations shared with them directly.

If you edited automations that someone else created, you can now only read them until someone shares edit access with you. The creator of an automation and admins keep their edit access.

Read the docs: Control Automation Sharing and Access

When you create an organization, the data storage region picker now offers AWS us-east-1 (N. Virginia) next to Oregon, Ireland, and the Netherlands. US customers had asked for an east-coast region.

Each region is a card showing the cloud it runs on, the place, and the cloud's own region ID. Dash0 stores that organization's telemetry in the region you pick, and you cannot change the region once the organization exists. Organizations that already exist keep the region they were created in.

See Organizations for the full region list and what the choice affects.

If your team keeps runbooks and agent skills in Git, next to the code they describe, Agent0 couldn't use them directly. Until now, bringing them to Agent0 meant copying each one into Dash0 by hand, and the copies drifted as soon as someone changed the original.

Now an admin can import them. In Organization Settings > Agent0 > Skills, select Import from Git. Scan any public GitHub or GitLab repository, or a private GitHub repository through the GitHub integration. Dash0 finds every SKILL.md, including skills with supporting files, and imports the ones you select. When a skill changes in the repository, select Update and Dash0 reads it again from the branch or tag you imported from.

Read the docs to import your first skills: Import Skills from a Git Repository