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gestão de lançamentos

  • Automated DevOps with AI agents for enterprise teams.
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    What is SRE.ai?
    SRE.AI offers the most advanced natural language DevOps platform for enterprise teams. It allows teams to collaborate smarter and deliver faster by using AI agents tailored to any workflow. Key features include fast deployments and back-promotions, error resolution at any layer, release insights with simulations and impact reports, and comprehensive automation through deep integrations across your entire stack. It's designed to ensure faster, safer releases while preventing future issues and protecting against data loss.
    SRE.ai Core Features
    • Advanced natural language processing
    • Fast deployments and back-promotions
    • Error resolution and prevention
    • Release simulations and impact reports
    • Deep integrations with existing tools
    • Backup and disaster recovery
    SRE.ai Pro & Cons

    The Cons

    The Pros

    AI agents tailored to any workflow for enhanced automation.
    Fast deployments and back-promotions with simple controls.
    Early identification and resolution of errors using AI.
    Simulations and impact reports to predict release outcomes.
    Deep integration with existing development tools.
    Customizable backup schedules and disaster recovery plans.
    SRE.ai Pricing
    Has free planNo
    Free trial details
    Pricing model
    Is credit card requiredNo
    Has lifetime planNo
    Billing frequency
    For the latest prices, please visit: https://sre.ai
  • A methodology offering twelve best practices to design, configure, and deploy scalable, maintainable AI Agents.
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    What is 12-Factor Agents?
    The 12-Factor Agents framework adapts the proven 12-factor app principles to the unique demands of AI Agent development. It prescribes a single codebase with version control, explicit dependency declaration, environment-agnostic configuration, and seamless integration with external services. It defines clear build and release stages, supports stateless processes, port-based binding, process concurrency, graceful shutdowns, and parity between development and production. Centralized logging and scripted administrative tasks are also emphasized. By following these structured guidelines, development teams can create AI Agents that are modular, scalable, and resilient, simplifying deployment, enhancing observability, and reducing operational complexity.
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