Antigravity CLI is the most recent agentic coding CLI from Google, changing the now-deprecated Gemini CLI. It inherits the asynchronous subagent mannequin that makes Antigravity stand out from the sphere, syncs bidirectionally with Antigravity Desktop, and is optimized for velocity on Gemini 3.5 Flash.

DataRobot ships a full plugin for Antigravity CLI straight from the identical open supply repository that powers our Cursor, Claude Code, and Gemini CLI integrations. One set up offers you the whole DataRobot ability set inside Antigravity’s agent and slash-command interface.

Set up the DataRobot plugin with a single command:

agy plugin set up 

If you happen to’re nonetheless on Gemini CLI, the identical repository installs there too:

gemini extensions set up 

Already utilizing the DataRobot extension in Gemini CLI and switching to Antigravity? Migrate it straight:

agy plugin import gemini

As soon as put in, the total DataRobot ability set is out there together with datarobot-setup and datarobot-agent-assist and might be invoked with slash instructions like /datarobot-skills:datarobot-agent-assist

Debugging brokers is difficult. LLM calls return plausible-sounding output even when one thing has gone unsuitable, software calls fail silently, and latency issues are invisible within the remaining response. With out structured hint information, the one choice is log-hunting and guesswork.

To point out how the DataRobot tracing ability works in observe, right here’s a concrete instance: a LangGraph agent in a single major.py file that manages bike exercises. It has a number of instruments, produces inconsistent solutions, and the basis trigger isn’t apparent from the conversational output alone.

Including production-grade tracing to this agent takes a single ability invocation: /datarobot-skills:datarobot-external-agent-monitoring.

The ability provisions a brand new DataRobot Use Case, devices the agent to emit traces by way of OpenTelemetry, and writes a monitoring_setup.md artifact with the runtime configuration steps.

With instrumentation in place, run the agent and ship it a query — on this case, “What’s the schedule this week?”

The ability generates setup directions that embrace the Use Case entity ID and the surroundings variables wanted to route traces to DataRobot:

The DataRobot tracing interface surfaces the total request historical past. Every hint exhibits end-to-end latency, complete token consumption, and the whole span tree:

Drilling into the “schedule this week” request reveals the total image: 2,700 tokens consumed, tool-level latency for every name, LLM invocation depend, and any customized attributes emitted by way of customary OTel instrumentation. That is the information that makes debugging tractable, not inference from remaining output.

For native improvement, the DataRobot CLI surfaces hint updates in actual time: dr plugin set up xp adopted by dr xp --entity-id=. This creates a good iteration loop — run the agent, examine the hint, repair the difficulty, repeat.

On this case, the span output makes the basis trigger express: the agent lacks calendar entry, which is why it couldn’t reply the scheduling query. That failure wasn’t surfaced within the agent’s conversational response:

As an alternative, the agent responded with generic steering:

2. **Construct every week from scratch** - If you happen to inform me just a few issues, I can sketch out a balanced week for you:

   - Your purpose (common health, an occasion/race, constructing endurance, and many others.)
   - What number of days/hours you possibly can practice
   - Your present health stage and any FTP you recognize

A strong common week would possibly appear to be:

- **Mon** - Relaxation or straightforward restoration spin
- **Tue** - Intervals
- **Wed** - Endurance trip (zone 2)
- **Thu** - Restoration or relaxation
- **Fri** - Tempo/threshold work
- **Sat** - Lengthy endurance trip
- **Solar** - Straightforward trip or relaxation

The hint made the hole between anticipated and precise agent conduct instantly actionable. This identical sample applies at enterprise scale: whether or not the agent is operating on a laptor or in manufacturing on a cloud supplier, DataRobot traces the total execution tree and surfaces what the agent truly did — not simply what it stated.

The hole between an agent prototype and an agent in manufacturing is generally operational context. Your coding agent writes the code. DataRobot provides the observability layer and the ruled deployment goal. One plugin set up, one ability execution — and you’ve got production-grade hint information from the primary run.