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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.

Resultat:

Dramatically reduce your MTTR by automatically identifying correlations between metrics, logs, and events.

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.

Les principaux impacts négatifs :

  • 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.

AVANT / APRÈS

Ce qui change avec 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

ÉTAPE 1 : Secure Datadog integration

Connect Swiftask to your Datadog instance via API key. Access is read-only, ensuring your data security.

ÉTAPE 2 : Define analysis scope

Configure the critical services and metrics the agent should monitor as a priority.

ÉTAPE 3 : Trigger configuration

Trigger automatic analysis as soon as a threshold is crossed or a specific event is detected in Datadog.

ÉTAPE 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.

  • Connecteur cible : L'agent exécute les bonnes actions dans datadog selon le contexte de l'événement.
  • Actions automatisées : Automatic metric/log correlation. Change behavior detection after deployment. Multi-service causality analysis. Automatic incident summarization.
  • Gouvernance native : Swiftask learns from your past incidents to refine the relevance of future correlations.

Chaque action est contextualisée et exécutée automatiquement au bon moment.

Chaque agent Swiftask utilise une identité dédiée (ex. agent-datadog@swiftask.ai ). Vous gardez une visibilité complète sur chaque action et chaque message envoyé.

À retenir : L'agent automatise les décisions répétitives et laisse à vos équipes les actions à forte valeur.

Operational benefits

1. MTTR cut in half

The time taken to find the root cause is reduced from tens of minutes to a few seconds.

2. Eliminate noise

Only correlated and relevant signals are escalated to on-call teams.

3. Infinite scalability

The AI analyzes thousands of metrics simultaneously, where a human could only handle a few dozen.

4. Skill transfer

AI insights help junior engineers diagnose complex incidents like experts.

5. Automatic documentation

Every analysis is archived, creating a valuable knowledge base for post-mortems.

Compliance and security

Swiftask applique des standards de sécurité enterprise pour vos automatisations datadog.

  • 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.

Pour aller plus loin sur la conformité, consultez la page gouvernance Swiftask et ses détails d'architecture de sécurité.

RÉSULTATS

Impact on your SRE performance

MétriqueAvantAprès
Mean Time to Diagnose (MTTD)30-60 minutes< 2 minutes
Irrelevant alertsHigh volume80% reduction
Diagnostic reliabilityVariable (human)High (data-driven)
SRE cognitive loadMaximum during crisisResolution-oriented

Passez à l'action avec datadog

Dramatically reduce your MTTR by automatically identifying correlations between metrics, logs, and events.

Générez vos rapports de post-mortem instantanément depuis Datadog

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