Correlate your Datadog metrics instantly with AI
Swiftask connects your AI agents to Datadog to analyze and correlate millions of metrics in real-time. Stop searching for the needle in the haystack.
Result:
Dramatically reduce your MTTR by automatically identifying correlations between metrics, logs, and events.
AI Agents
datadog
Connector datadog · Secure OAuth 2.0
Data explosion makes manual correlation impossible
Faced with complex microservice architectures, SRE teams are overwhelmed by the volume of Datadog data. Manually correlating metrics, traces, and logs takes precious time during critical incidents.
Main negative impacts:
Extended diagnostic time
Engineers spend hours switching between Datadog dashboards trying to link latency spikes to deployments.
Alert fatigue
Noise from thousands of uncorrelated alerts hides weak signals, leading to missed or poorly prioritized incidents.
Knowledge silos
Understanding correlations often depends on a single expert, creating a bottleneck during crises.
Swiftask acts as an analytical brain over Datadog. It continuously correlates your metrics, logs, and events to instantly pinpoint the root cause.
BEFORE / AFTER
What changes with Swiftask
The traditional approach
A latency alert triggers. The engineer opens 5 different dashboards, manually compares charts, hunts for anomalies in logs, and guesses the link to recent deployments.
The Swiftask + Datadog analysis
The Swiftask AI agent receives the Datadog alert, queries correlated metrics, analyzes recent logs, and presents the diagnosis: 'Latency caused by memory leak on service X after deployment Y'.
Activate AI correlation in 4 steps
1
STEP 1 : Secure Datadog integration
Connect Swiftask to your Datadog instance via API key. Access is read-only, ensuring your data security.
2
STEP 2 : Define analysis scope
Configure the critical services and metrics the agent should monitor as a priority.
3
STEP 3 : Trigger configuration
Trigger automatic analysis as soon as a threshold is crossed or a specific event is detected in Datadog.
4
STEP 4 : Diagnostic and notification
The agent analyzes, synthesizes, and sends the correlation report directly to your collaboration tools (Slack/Teams).
Advanced analysis capabilities
The agent cross-references time series, infrastructure tags, and application logs to detect anomalies invisible to the human eye.
- Target connector: The agent performs the right actions in datadog based on event context.
- Automated actions: Automatic metric/log correlation. Change behavior detection after deployment. Multi-service causality analysis. Automatic incident summarization.
- Native governance: Swiftask learns from your past incidents to refine the relevance of future correlations.
Each action is contextualized and executed automatically at the right time.
Each Swiftask agent uses a dedicated identity (e.g. agent-datadog@swiftask.ai ). You keep full visibility on every action and every sent message.
Key takeaway: The agent automates repetitive decisions and leaves high-value actions to your teams.
Operational benefits
MTTR cut in half
The time taken to find the root cause is reduced from tens of minutes to a few seconds.
Eliminate noise
Only correlated and relevant signals are escalated to on-call teams.
Infinite scalability
The AI analyzes thousands of metrics simultaneously, where a human could only handle a few dozen.
Skill transfer
AI insights help junior engineers diagnose complex incidents like experts.
Automatic documentation
Every analysis is archived, creating a valuable knowledge base for post-mortems.
Compliance and security
Swiftask applies enterprise-grade security standards for your datadog automations.
- Read-only access: Access to Datadog is strictly limited to reading data, with no write or modify permissions.
- Encrypted data: All communications between Swiftask and Datadog are encrypted in transit and at rest.
- Isolated access: Uses restricted API keys to ensure the principle of least privilege.
- Enterprise compliance: Architecture designed to meet GDPR and SOC2 standards.
To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.
RESULTS
Impact on your SRE performance
| Metric | Before | After |
|---|---|---|
| Mean Time to Diagnose (MTTD) | 30-60 minutes | < 2 minutes |
| Irrelevant alerts | High volume | 80% reduction |
| Diagnostic reliability | Variable (human) | High (data-driven) |
| SRE cognitive load | Maximum during crisis | Resolution-oriented |
Take action with datadog
Dramatically reduce your MTTR by automatically identifying correlations between metrics, logs, and events.