Tarification

Boost your MTTR with AI-driven Bugsnag error analysis

Swiftask turns your Bugsnag data into actionable insights. Automatically calculate and track your MTTR to speed up incident resolution.

Resultat:

Reduce downtime and improve deployment reliability without manual effort.

Manual MTTR tracking slows down your technical velocity

Manually tracking Mean Time To Repair (MTTR) from Bugsnag is error-prone and time-consuming. Teams spend hours cross-referencing data, extracting reports, and trying to understand error trends, which delays decision-making.

Les principaux impacts négatifs :

Fragmented data: Error information is isolated within Bugsnag. Without aggregation, you lack a full view of actual performance.
Outdated reporting: Manually generated reports are often obsolete by the time they are published, making quality management ineffective.
Limited reactivity: Time spent analyzing logs is time lost from actively resolving critical bugs.

Swiftask automates the extraction and analysis of your Bugsnag data. Your AI agent calculates your MTTR in real time and alerts you to critical trends.

AVANT / APRÈS

Ce qui change avec Swiftask

Without Swiftask

A developer manually extracts data from Bugsnag every week. They clean CSV files, calculate averages in a spreadsheet, and create charts. The process is slow, error-prone, and frustrating.

With Swiftask + Bugsnag

The Swiftask agent continuously monitors your Bugsnag errors. It updates your performance dashboards instantly and notifies the team as soon as an MTTR drift is detected.

Automate your MTTR tracking in 4 simple steps

ÉTAPE 1 : Initialize the analysis agent

Set up a Swiftask agent dedicated to software performance monitoring.

ÉTAPE 2 : Connect your Bugsnag projects

Integrate Swiftask with your Bugsnag projects via API for secure error reading.

ÉTAPE 3 : Define calculation parameters

Configure your MTTR calculation rules based on your criticality and priority criteria.

ÉTAPE 4 : Activate automatic reporting

Receive periodic summaries or instant alerts on your KPIs directly in your workflow.

AI analysis capabilities for your errors

The agent analyzes resolution velocity, volume of recurring errors, and the impact of deployments on overall stability.

Connecteur cible : L'agent exécute les bonnes actions dans bugsnag selon le contexte de l'événement.
Actions automatisées : Automatic MTTR calculation. Error trend identification. Performance report generation. Threshold-based alerting. Correlation between deployments and incidents.
Gouvernance native : All analyses are centralized in your Swiftask interface for total transparency.

Chaque action est contextualisée et exécutée automatiquement au bon moment.

Chaque agent Swiftask utilise une identité dédiée (ex. agent-bugsnag@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 gains for your team

1. Data-driven decisions

Drive your quality roadmap with precise and reliable measurements.

2. Massive time savings

Eliminate administrative technical reporting tasks.

3. Continuous improvement

Quickly identify bottlenecks in your correction processes.

4. Cross-functional visibility

Share clear KPIs with non-technical stakeholders.

5. Proactive alerting

Act before incidents pile up thanks to real-time monitoring.

Log confidentiality and security

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

Data encryption: Your Bugsnag logs are processed via encrypted and secure connections.
Granular access control: Determine exactly who accesses performance reports within Swiftask.
Compliance: Data management compliant with enterprise security standards.
Independence: You retain full control over your error data.

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

RÉSULTATS

Improved performance indicators

Métrique	Avant	Après
MTTR calculation time	Hours per week	Real-time (instant)
Data accuracy	Human error risk	Algorithmic reliability
Reporting frequency	Weekly/Monthly	Continuous / On-demand
Incident response time	Reactive	Proactive

Passez à l'action avec bugsnag

Reduce downtime and improve deployment reliability without manual effort.

Corrélez vos erreurs Bugsnag instantanément avec l'IA

Cas d'usage suivant.