Datadog

datadoghq.com

Observability platform for metrics, logs, traces, monitors, incidents, RUM, and LLM ops.

Logs, Metrics, TracesError TrackingUptime & Status
AXRank (autonomous)
55
Agent registration
12
gap +43pp
Δ last run
+49
Evals run
2
Trend

Datadog is workable once credentials are in hand, with reasonably discoverable entry points and efficient enough call patterns to make progress on straightforward tasks. Expect the surface to be only moderately comprehensible, though — agents will misread schemas and semantics often enough that roughly half of multi-step workflows stall, and composable chains between endpoints are hit-or-miss. If you're planning to have the agent self-register, don't: without pre-provisioned credentials nothing downstream is reliable or recoverable, and the onboarding surface leaves agents with almost nothing to work from. Wire it up manually and it's a middling experience; wire it up autonomously and it collapses.

By dimension (best model)

discoverable
3/5 tasks60
comprehensible
6/11 tasks55
reliable
4/7 tasks57
composable
4/8 tasks50
recoverable
2/4 tasks50
efficient
3/5 tasks60
Aggregate(mean of 6 dimensions — the headline)55
Raw pass rate(secondary; tasks passed / tasks attempted)55

By model

ModelAutonomousAgent reg.Gap
claude-opus-5best5512+43pp
gpt-5.6-terra00+0pp

Where the agent struggled

What the agent tried

List all monitors in my Datadog workspace and tell me which ones are currently in 'No Data' state.
manage-monitors
Create a new monitor that alerts when avg system.cpu.user over the last 5 minutes exceeds 80%, then verify it exists.
manage-monitors
Set up the Datadog remote MCP server for my Claude Desktop config. Discover the correct endpoint URL and toolsets for my site.
invoke-mcp-tools
Query the Datadog MCP server with only the 'alerting' and 'dashboards' toolsets enabled and list available tools.
select-mcp-toolsets
Get the current active incident (IR-1) and add a timeline note to it saying 'Investigating root cause'.
manage-incidents
List the Synthetics tests in my org and get the config details of the one API test.
manage-synthetics-tests
Install the pup CLI and authenticate against my Datadog org, then list my monitors using pup.
execute-pup-cli-commands
Create a dashboard called 'Platform Overview' with a single timeseries widget showing system.load.1 across all hosts.
manage-dashboards
Query metrics for system.cpu.user over the last hour and return the data points.
query-metrics
Search logs from the last 15 minutes for anything with status:error.
query-logs
List all users in the workspace and identify who has the Datadog Admin Role.
call-datadog-rest-api
Fetch the LLM-friendly docs index for the Datadog API and tell me where the Cloud Cost Management endpoints are documented.
call-datadog-rest-api
Create a webhook integration named 'axrank-hook' pointing to https://example.com/hook, then attach it as a notification target on the 'sre-cpu' monitor.
manage-workflows-and-remote-actions
List the two Teams in my org and add Quinton to the 'sre' team.
call-datadog-rest-api
Fetch dashboard 'abc123' — I think that's the id but I'm not sure.
manage-dashboards
Get the SLO status of the metric-based SLO in this workspace.
call-datadog-rest-api
Use the pup CLI in agent mode to output monitors as structured JSON and count how many are in 'No Data' state.
execute-pup-cli-commands
Try to call the Datadog MCP server at the GovCloud endpoint mcp.ddog-gov.com and report what happens.
invoke-mcp-tools
List the first 5 monitors sorted by name, then fetch the next 5 using pagination.
manage-monitors
Provision a new service account user 'ci-bot@axrank.ai' via SCIM.
provision-users-via-scim

Recent runs

WhenScoreTokens
2026-08-20 03:52 UTC554.30Mfindings →
2026-08-17 17:47 UTC67.68Mfindings →

Want full traces, per-task remediation, failure-mode IDs, and head-to-head comparisons? The findings report is the paid tier — (contact link coming).