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Datadog API for AI Agents
Infrastructure monitoring and observability
Datadog provides APIs for metrics, logs, traces, and infrastructure monitoring. AI agents can use Datadog to query metrics, search logs, manage monitors, and automate incident response.
What AI agents can do with Datadog
Structured actions an AI agent can execute through the Datadog API
Action
Description
Inputs
Outputs
queryMetrics
Query time-series metrics
query, from, to
series[], status
searchLogs
Search log entries
query, from, to, sort, limit
logs[], status
createMonitor
Create an alert monitor
name, type, query, message, thresholds
monitor_id, name, state
listAlerts
List triggered alerts
query, group_states
monitors[], counts
Use cases for Datadog + AI agents
- Infrastructure health monitoring
- Log analysis and search
- Alert management and incident response
- Performance metric tracking
- Anomaly detection and reporting
How to connect Datadog to an AI agent
- 1Get your Datadog API and Application keys
- 2Generate an AgentSpec for monitoring actions
- 3Define metric, log, and alert actions
- 4Publish for discovery
- 5Set appropriate access scopes
Best practices
✓Use specific metric queries to avoid scanning too much data
✓Include time range filters in all queries
✓Add reasoning docs about alert severity levels
✓Implement escalation logic for unacknowledged alerts
✓Cache metric queries that are called frequently
Frequently asked questions
Can AI agents monitor infrastructure with Datadog?+
Yes. Agents can query metrics, search logs, check monitor status, and respond to alerts through structured actions. This enables automated incident detection and response.
How do agents handle Datadog alerts?+
List triggered monitors, check their state and context, and take action (acknowledge, notify team, trigger remediation). Include escalation logic in reasoning docs.
What Datadog data can agents access?+
Metrics, logs, traces, monitors, dashboards, and events. Use API and Application keys with appropriate scopes to control access levels.
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