Automic Automation's Key Capabilities and Features

Broadcom's Automic Automation delivers a comprehensive, end-to-end solution for automating workflows, enhancing operational efficiency, and ensuring scalability, security, and compliance through key features across these strategic capabilities:

• Workflow Automation and Job Orchestration

• Monitoring, Alerting, and Analytics

• Integration and Extensibility

• Scalability and High Availability

• Self-Service Automation and User Empowerment

Each capability works together to provide a unified automation platform that adapts to the needs of modern enterprises.

Workflow Automation and Job Orchestration

These features focus on workflow automation, intelligent job scheduling, and dependency management. Automic Automation enables designing and sequencing complex workflows with reusable job templates and sophisticated dependency controls, ensuring precise orchestration of IT and business processes across diverse systems.

Job Scheduling and Orchestration

Automic Automation provides comprehensive features for workflow automation, intelligent job scheduling, and dependency management. These capabilities enable you to design and sequence complex workflows using reusable job templates and supporting conditional logic, error handling, and branching to adapt dynamically to runtime conditions. Advanced dependency management ensures that jobs spanning multiple platforms execute in the correct sequence with resource-aware prioritization. This ensures precise orchestration of IT and business processes across on-premises, cloud, and hybrid environments.

With its scalable architecture and agent-based model, Automic Automation can handle millions of jobs concurrently, making it suitable for mission-critical enterprise automation at scale. The platform also supports multi-tenancy and role-based access control. Together, they allow organizations to scale automation across multiple teams and geographies, while maintaining control, compliance, and security at every level.

At the core of Automic Automation is its ability to schedule and execute jobs across heterogeneous environments — from mainframes to cloud-native platforms — through an extensible, event-driven framework. Unlike traditional schedulers that rely solely on calendars, Automic Automation supports:

• Time-based scheduling: Execute tasks on fixed intervals, cron-like expressions, or specific dates and times.

• Event-based scheduling: Trigger jobs when business or system events occur, such as:

  • File arrivals or updates

  • Database changes

  • Message queues or APIs

  • System alerts or external signals

• Dependency-based scheduling: Sequence jobs based on the completion or state of other jobs and workflows, ensuring precise orchestration of multi-step processes.

This flexibility allows IT teams to build automation that responds dynamically to real-world conditions, instead of following static plans.

Workflows

Workflows are the backbone of Automic Automation. They allow you to orchestrate multiple jobs into logical, reusable chains that represent end-to-end business or IT processes.

The key workflow features are:

• Reusable templates and objects: Build modular workflows with standard job templates that can be reused across projects.

• Conditional logic and branching: Adapt execution paths dynamically based on runtime conditions or job outcomes.

• Error handling and remediation: Define fallback paths, retries, and automated resolution for failed jobs.

• Cross-application orchestration: Combine jobs from different platforms (ERP, cloud services, databases, CI/CD pipelines) into a single workflow.

Workflow Designer

The Workflow Designer is Automic Automation’s intuitive interface for building workflows. It provides:

• Drag-and-drop design: Easily arrange jobs, tasks, and dependencies visually.

• Dependency mapping: Define relationships between tasks with clear visual connectors.

• Runtime simulation: Preview execution paths and dependencies before deployment.

• Versioning and audit trails: Track changes to workflows for compliance and rollback.

Events

Events provide the foundation for real-time, event-driven automation. Instead of waiting for scheduled times, Automic Automation can react instantly to:

• File Events: Trigger workflows when files are created, modified, or deleted.

• Database Events: Monitor tables, rows, or queries to initiate automation on data changes.

• Message and API Events: Integrate with external systems through REST, SOAP, or message queues.

• System Events: React to log updates, system alerts, or application triggers.

This event-driven model enables adaptive, responsive automation aligned with business needs.

Schedules

Schedules in Automic Automation allow you to define recurring execution patterns for jobs and workflows. They support:

• Calendar-based planning: Business calendars, holidays, and custom schedules.

• Granular control: Hourly, daily, weekly, monthly recurrence patterns.

• Overrides and exceptions: Flexible adjustments for one-off scenarios or maintenance windows.

• Dynamic adaptation: Combined with events, schedules can shift based on workload demands or SLA requirements.

Schedules ensure predictable, repeatable execution, while still allowing adaptability when integrated with events and dependencies.

Dependency Management

Complex enterprise workflows often involve jobs spanning multiple platforms and systems. Automic Automation provides advanced dependency management to guarantee execution in the correct order, with features such as:

• Cross-platform sequencing: Ensure tasks across cloud, on-premises, and hybrid systems align.

• Resource-aware prioritization: Allocate jobs based on available compute resources or workload balancing.

• SLA monitoring and remediation: Detect delays or failures and trigger automated responses, such as rerouting workflows or scaling infrastructure.

• Conditional execution: Trigger downstream jobs only when upstream conditions are met (for example, only if data is successfully loaded).

By managing dependencies intelligently, Automic Automation ensures consistency, accuracy, and reliability across your enterprise workload automation.

Multi-Tenancy through Clients

Automic Automation is designed to serve multiple business units, teams, or customers from a single platform instance through its multi-tenancy architecture. Together, they allow organizations to scale automation across multiple teams and geographies, while maintaining control, compliance, and security at every level.

A Client acts like a self-contained workspace where automation objects (Jobs, Workflows, Calendars, Variables, and so on) are created, stored, and executed.

• Logical separation: Each Client operates in its own isolated namespace with dedicated automation objects, workflows, schedules, and resources.

• Shared infrastructure, isolated governance: Tenants share the same underlying automation engine but maintain strict boundaries for security, compliance, and operational independence.

• Resource quotas and policies: Administrators can define limits on execution agents, job priorities, and workload capacities per Client.

• Custom calendars and schedules: Each Client can define its own time zones, business calendars, and exceptions without impacting others.

• Client-specific integrations: Connectors and action packs can be scoped to individual Clients, enabling tailored automation environments (for example, SAP for Finance, Kubernetes for DevOps).

This approach makes it possible to run enterprise-wide automation as a service, where multiple departments or even external customers share one robust platform while maintaining data segregation and governance.

Role-Based Access Control

Security and governance in Automic Automation are enforced through fine-grained, role-based access control which ensures that every user only has the level of access they require. This is achieved through granting different authorizations and privileges.

• Granular permissions: Access can be restricted to specific objects (jobs, workflows, calendars), functions (design, execute, monitor), or environments.

• Role hierarchy: Roles can be layered (for example, designer, operator, administrator), supporting both least-privilege principles and enterprise governance.

• Segregation of duties: Role-based access control prevents conflicts of interest by ensuring that the same user cannot both design and approve critical workflows.

• Scoped visibility: Users only see the clients, jobs, and dashboards relevant to their role, avoiding accidental cross-team interference.

• Audit and compliance: All role assignments, access changes, and actions are logged with full audit trails, supporting different regulatory requirements.

• Integration with identity providers: Role-based access control can integrate with enterprise identity systems (LDAP, Active Directory, SSO) for centralized authentication and role provisioning.

Using Scripts in Automic Automation

When talking about scripting in Automic Automation, there are two layers:

• Automic Automation Scripting Language (AE scripting) which is used inside objects to control workflows and handle automation logic.

  This language is designed specifically for workload automation, with syntax and constructs tailored to job orchestration rather than general-purpose programming. Scripts can be embedded in almost every object type, including Jobs, Workflows, Events, and Notification objects, giving designers granular control over execution behavior.

  The scripting language provides predefined system functions and variables that expose information about jobs, workflows, and runtime environments. For example, scripts can retrieve the status of a preceding job, evaluate system time, or access environment variables. This allows workflows to adapt dynamically, such as by skipping unnecessary tasks, rerouting based on failure conditions, or branching execution according to input values.

  While scripting is powerful, it should complement - not replace - Automic Automation’s object-oriented design. Best practice is to use scripting for dynamic behavior and exception handling, while keeping the overall process flow visible in the Workflow Designer. Scripts should be modular and reusable, relying on variable objects to minimize hardcoding. This approach balances flexibility with maintainability, ensuring that automation logic remains understandable and auditable.

• External scripts which are standard system or application scripts (Python, Shell, Perl, PowerShell, etc.) that are executed through jobs and workflows, allowing you to reuse existing automation assets within Automic Automation’s governance, scheduling, and monitoring framework.

  When you create a Job object in Automic Automation, you can specify the target system and the interpreter required for the script. For example, a Linux Job can call a Shell or Perl script, while a Windows Job can call PowerShell or Python. Automic Automation Agents deployed on the target systems ensure that the scripts execute reliably, capturing both the standard output and error streams. This information is returned to Automic Automation, where it can be logged, audited, and used for dependency management.

  Scripts written in Python, Shell, or Perl can be parameterized with Automic Automation variables, allowing them to adapt dynamically at runtime. For instance, a Shell script might take a filename as input, with that filename being passed in from a variable that was populated earlier in a workflow. This combination allows organizations to reuse existing script investments while taking advantage of Automic Automation’s object-oriented design and centralized governance.

  Error handling is another important integration point. External scripts typically return exit codes that indicate success or failure. Automic Automation captures these codes and evaluates them according to predefined rules. If a script fails, Automic Automation can automatically trigger retries, escalate alerts, or launch alternate workflows. This ensures that external scripts benefit from the same reliability, SLA monitoring, and automated remediation as native Automic Automation tasks.

Combining AE AE scripting with external languages unlocks powerful hybrid automation patterns. AE scripting can control workflow logic—deciding whether to run a job, which script to call, or how to respond to results—while Python, Shell, or Perl handle complex application logic, data manipulation, or system interaction. This layered approach ensures that workflows remain transparent and auditable within Automic Automation, while leveraging the full power of established scripting ecosystems.

Robust End-to-End Process Automation

Automic Automation's end-to-end automation capabilities provide a unified, enterprise-grade platform for orchestrating complex IT and business processes across heterogeneous environments, from mainframe to cloud-native applications - it delivers true end-to-end process automation. It enables organizations to automate entire value streams by connecting disparate technologies, applications, and infrastructures into cohesive workflows.

Why does it matter? End-to-end process automation means you do not just automate jobs — you automate business outcomes.

• Finance can close the books faster by linking ERP jobs with reporting workflows.

• Retail can automate order-to-delivery by connecting e-commerce, warehouse, and logistics systems.

• DevOps can deliver software faster by orchestrating CI/CD pipelines across hybrid infrastructure.

Automic Automation provides the platform, governance, and intelligence to do this at enterprise scale.

The key features that support it are the following:

• Object-Oriented Automation Design: This modular design ensures processes are consistent, maintainable, and scalable across different teams and clients.

  • Reusable automation objects: Jobs, workflows, events, schedules, variables, and scripts are modeled as objects that can be reused across processes.

  • Parameterization: Objects can be configured dynamically with input values, making them flexible and reducing duplication.

  • Modular workflow construction: Build processes from smaller components, so updates and maintenance are simplified.

  • Versioning and lifecycle management: Track changes to automation objects with rollback capabilities for controlled evolution.

• Cross-Platform Orchestration: You can automate processes that span legacy systems and modern platforms simultaneously, removing silos.

  • Heterogeneous integration: Natively orchestrates mainframes, ERP (for example, SAP, Oracle), cloud services (AWS, Azure, Google Cloud), big data platforms, and DevOps toolchains.

  • Agent-based execution: Lightweight, distributed agents execute jobs reliably across diverse infrastructures.

  • Action packs and connectors: Prebuilt integrations reduce custom scripting (for example, for databases, file transfers, Kubernetes, Jenkins).

  • Hybrid automation: Control on-premises and cloud-native systems in a single workflow.

• Dynamic Dependency and Event Management: Processes react to business events in real time, not just at fixed schedules.

  • Event-based triggers: Workflows start when conditions are met (file arrival, DB update, API call, system event).

  • Conditional logic: Execution paths adapt in real time (branching, retries, exception handling).

  • Resource-aware execution: Jobs are prioritized based on system load and resource availability.

  • Cross-application dependencies: Complex sequencing ensures correct execution order across systems.

• SLA-Driven Automation: Automation stays aligned with business outcomes, not just technical tasks.

  • SLA monitoring: Track business KPIs, not just job status.

  • Automated remediation: Trigger fallback workflows, retries, or resource scaling when SLAs are at risk.

  • Predictive analytics: Forecast bottlenecks or delays based on historical execution data.

  • Business alignment: Define automation goals in terms of service-level objectives (SLOs), not just task completion.

• End-to-End Visibility and Control: Everyone from IT Ops to business stakeholders can monitor and trust automated processes.

  • Centralized monitoring dashboards: Real-time visibility into workflows, dependencies, and performance.

  • Drill-down analysis: Trace failures to root causes quickly.

  • Reporting and auditing: Generate compliance-ready logs and performance reports.

  • Role-based dashboards: Custom views for operators, developers, and business users.

• Governance, Security, and Compliance: Automation is safe to scale across regulated, enterprise environments.

  • Multi-client architecture: Isolated environments for departments or customers within one system.

  • Role-based access control: Granular permissions to enforce least-privilege access.

  • Immutable audit trails: Every change and execution is logged.

  • Encryption and policy controls: Ensure compliance with different regulatory requirements

• DevOps and Cloud-Native Support: Brings traditional IT automation into the DevOps and cloud-native world.

  • CI/CD pipeline orchestration: Automate build, test, and deploy steps with Jenkins, Git, Docker, Kubernetes.

  • Infrastructure as Code integration: Orchestrate Ansible, Terraform, and other IaC tools.

  • Cloud-native services: Trigger AWS Lambda, Azure Functions, or GCP Cloud Functions.

  • Auto-scaling: Dynamically adjust resources based on workload thresholds.

By bridging traditional systems and modern digital platforms, Automic Automation enables true end-to-end orchestration that accelerates delivery, improves resilience, and optimizes operational efficiency.

Cloud and Hybrid Automation

Automic Automation’s cloud, hybrid, and cloud-native automation capabilities enable seamless orchestration of workloads across on-premises data centers, private clouds, and public cloud platforms. Its architecture supports hybrid deployment models, allowing enterprises to centralize control of both legacy and cloud-native environments within a single automation framework. Through native integrations and APIs, Automic Automation can automate provisioning, scaling, and lifecycle management of cloud resources, while also managing dependencies with traditional systems like mainframes, ERPs, and databases. The platform’s event-driven automation and policy-based orchestration allow dynamic adaptation to workload demands, such as auto-scaling cloud services based on thresholds or triggering multi-cloud failover scenarios.

Its container-native capabilities extend to Kubernetes, OpenShift, and serverless environments, enabling automated deployment pipelines, microservices orchestration, and CI/CD workflows across hybrid infrastructures. Automic Automation also provides centralized SLA monitoring, predictive analytics, and automated remediation to ensure workload reliability and cost optimization in multi-cloud operations. Governance features ensure compliance across distributed environments, while the scalable agent-based model supports millions of concurrent executions.

The key features that support it are the following:

• Unified Control Plane: No more fragmented scheduling tools per environment — you get a holistic automation strategy.

  • Single orchestration layer: Manage mainframe, ERP, SaaS, containers, and cloud-native services through one automation engine.

  • Central policy enforcement: Security, SLAs, and compliance rules are defined once and applied consistently across environments.

  • Hybrid workflows: A single workflow can call on-prem batch jobs, provision cloud infrastructure, and trigger SaaS processes seamlessly.

• Cloud Resource Lifecycle Management: Automic Automation becomes a cost-aware orchestrator, not just a job scheduler.

  • Provisioning & deprovisioning: Automatically spin up VMs, databases, or containers on AWS, Azure, or GCP, then tear them down after use.

  • Auto-scaling: Trigger scale-out or scale-in actions based on thresholds like CPU, memory, or queue depth.

  • Cost optimization: Schedule cloud resources to run only when needed, reducing consumption costs.

• Container & Microservices Automation: Bridges the gap between cloud-native and legacy environments, a common challenge in hybrid IT.

  • Native support for Kubernetes & OpenShift: Deploy applications, scale services, and manage rolling updates from within workflows.

  • Microservices orchestration: Coordinate dependencies between containerized services and traditional IT workloads.

  • CI/CD pipeline integration: Automate build, test, and deployment pipelines across hybrid infrastructure.

• Multi-Cloud & Failover Scenarios: Ensures resilience and flexibility in how workloads are executed globally.

  • Multi-cloud orchestration: Run workloads across AWS, Azure, and GCP simultaneously, or choose dynamically based on policy (e.g., lowest cost).

  • Failover workflows: Automatically reroute jobs to alternate cloud providers or regions if a primary service fails.

  • Cloud-bursting: Extend on-prem workloads into the cloud when local capacity is exceeded.

• Compliance & Governance in Distributed Environments: Brings enterprise governance discipline into otherwise fast-moving, decentralized cloud environments.

  • Role-based access across cloud resources: Ensure only authorized roles can provision or modify cloud workloads.

  • Auditing and traceability: Every action taken on a cloud resource is logged in Automic’s audit trail.

  • Policy-driven execution: Define who can use which cloud services, at what cost thresholds, and for which workloads.

By unifying automation across cloud and hybrid ecosystems, Automic Automation becomes the connective fabric of hybrid IT, unifying legacy and cloud-native systems under a single, policy-enforced orchestration platform. It ensures that enterprises can move workloads to the cloud at their own pace while maintaining consistency, governance, and resilience across the entire technology landscape.

Monitoring, Alerting and Analytics

Automic Automation provides real-time monitoring, alerting, and comprehensive reporting. It offers dashboards with trend analytics, SLA monitoring, and audit trails, providing operational visibility and ensuring compliance and performance optimization.

Real-Time Monitoring and Alerting

Effective automation requires more than scheduling jobs - it requires visibility, responsiveness, and control in real time. Automic Automation's monitoring framework, alerting engine, and SLA management features provide real-time monitoring and alerting capabilities across hybrid and multi-cloud enterprise environments that ensure workloads not only run successfully, but also deliver on business commitments defined by service-level agreements (SLAs).

Real-time monitoring and alerting in Automic Automation transform automation into a business-aligned service. By combining continuous observability, actionable alerts, SLA monitoring, and the SLO object, organizations gain both operational resilience and confidence that automation supports critical business outcomes. Automic ensures that processes not only run, but run successfully within the boundaries that matter most to the business.

Real-Time Monitoring

Automic Automation offers centralized dashboards and drill-down tools that continuously update as jobs and workflows execute. This real-time approach allows users to monitor individual job runs, dependencies between tasks, and the overall state of workflows without waiting for periodic updates. The Process Monitoring perspective also provides visualization of dependencies, making it clear which downstream tasks are waiting on upstream completions. Resource utilization metrics such as agent workload and system queues are tracked to help identify potential bottlenecks before they impact execution.

Monitoring views can be tailored based on role. Operators see execution status and error conditions, developers can focus on workflow design and testing insights, while business stakeholders can view progress toward process completion. This role-based perspective ensures that monitoring delivers the right level of detail to the right audience. The result is faster detection of issues, reduced mean time to detect, and improved operational assurance across both on-premises and hybrid environments.

Alerting Framework

Monitoring is integrated with a powerful alerting system that reacts to execution conditions in real time. Alerts can be raised when jobs fail, workflows are delayed, resources are constrained, or thresholds are exceeded. These alerts are configurable with different severity levels, allowing teams to distinguish between warnings and critical issues. Escalation rules ensure that unresolved alerts move to the appropriate team or manager automatically, reducing the chance of unattended incidents.

Automic Automation supports multiple notification channels, including email, SMS, SNMP traps, webhooks, and integration with IT service management platforms such as ServiceNow. Alerts are not just informational - they can trigger automated responses. For example, a failed job can automatically restart, or a resource bottleneck can initiate a scaling workflow. Every alert and action is captured in the audit trail, ensuring compliance and providing data for future analysis.

SLA Monitoring and the SLO Object

A defining strength of Automic Automation is its ability to monitor not just job completion but also adherence to business outcomes. This is made possible through SLA monitoring and the SLO object. An SLA defines the expected performance or completion window for a business-critical workflow, such as end-of-day financial reconciliation or payroll processing. Automic Automation continuously tracks execution against these expectations, using historical execution data to forecast whether a process is at risk of violating its SLA.

The SLO object provides the mechanism for implementing this capability. An SLO object defines specific service-level objectives such as maximum runtime, deadlines, or completion windows. Once attached to a workflow or job chain, it continuously evaluates the execution against the defined objective. If the system detects a breach—or even predicts one—it can generate an alert and initiate corrective action. For example, a delayed workflow may automatically be reprioritized, given more resources, or escalated to management before the SLA is missed. By doing so, Automic moves beyond reactive monitoring to proactive SLA assurance.

End-to-End Visibility and Compliance

All monitoring and alerting activities feed into Automic Automation’s reporting and auditing framework. Every job state, alert, and corrective action is captured in immutable logs. This provides full traceability for internal governance and external audits, supporting compliance with different regulatory frameworks. At the same time, these logs supply valuable performance insights that can be exported into business intelligence tools, helping organizations optimize workflows and resource usage.

By combining real-time observability, intelligent alerting, predictive forecasting, and automated recovery, Automic Automation ensures enterprise workloads remain reliable, performant, and aligned with business SLAs.

Predictive Analytics and AI-Powered Insights

Beyond reactive alerts, Automic Automation leverages predictive analytics to detect anomalies and forecast potential SLA violations or performance bottlenecks before they occur. By analyzing historical execution patterns, seasonal variations, and workload trends, it can anticipate delays, resource contention, or failures in advance. SLA-based monitoring ensures that critical business processes are tracked against defined objectives, with proactive warnings issued well before thresholds are breached. This enables enterprises to move from reactive workload management to proactive, data-driven automation.

At the core of Automic Automation’s predictive analytics is the ability to analyze historical execution patterns and real-time telemetry to forecast potential problems before they occur. By learning from workload behavior, Automic Automation can identify when an SLA is at risk of being breached, when a workflow is likely to overrun its expected window, or when resource contention could cause bottlenecks. Unlike static schedulers that treat each job in isolation, Automic applies data-driven intelligence across entire process chains. This allows the system to highlight risks proactively, giving operations teams time to intervene or trigger automated remediation before business impact occurs.

The platform’s machine learning models play a crucial role in detecting anomalies. For example, if a workflow typically completes in 20 minutes but suddenly trends toward an hour, Automic Automation flags this deviation as a likely indicator of failure or environmental stress. The system continuously adapts its models as environments evolve, ensuring that predictions remain accurate even as workloads change over time. By correlating deviations with other telemetry data—such as CPU usage, queue lengths, or job failures - Automic Automation provides a contextualized view of why a process may be at risk, not just that it is at risk.

Predictive analytics also powers capacity planning and workload optimization. Automic Automation helps teams identify peak processing times, recurring seasonal spikes, and underutilized resources. With this information, organizations can rebalance workloads, shift non-critical jobs into off-peak windows, or adjust resource allocations to improve efficiency. For cloud and hybrid environments, this intelligence extends into cost control: predictive insights can recommend when to scale out resources proactively, or when to consolidate workloads to avoid unnecessary consumption charges.

A major feature for business alignment is Automic Automation’s use of predictive insights in conjunction with SLA and SLO management. By evaluating workflows against their defined objectives, Automic Automation can forecast SLA breaches hours before they happen. Instead of waiting for a violation, the system can automatically initiate mitigation actions such as accelerating dependent tasks, prioritizing critical workflows, or allocating additional agents. This ensures that automation remains tied directly to business outcomes, with AI models serving as an early warning system that protects service delivery.

These insights are delivered through intuitive dashboards that visualize trends, predictions, and risk indicators. Business and IT stakeholders can view not just what is happening now, but what is likely to happen in the near future. Predictive insights can also be embedded directly into workflows, where they trigger automated responses without manual intervention. For example, if an AI model predicts that a payroll workflow will overrun its completion window, Automic Automation can immediately reassign resources or activate a fallback process.

By embedding predictive analytics and AI-powered insights across its Automation Engine, Automic Automic Automation shifts the operational mindset from reactive firefighting to proactive service assurance. The system not only improves SLA compliance and operational resilience, but also drives efficiency by optimizing workload distribution and resource usage. For enterprises with mission-critical automation, these features provide the intelligence layer needed to keep automation aligned with business performance at scale.

Reporting, Auditing and Performance Metrics

Automation is only as powerful as the visibility and accountability that surround it. In complex enterprise environments, where thousands or even millions of jobs execute daily, organizations need clear insights into how automation performs, whether compliance obligations are met, and where improvements can be made.

Automic Automation provides a robust framework for reporting, auditing, and performance metrics that ensures transparency, governance, and continuous optimization. Its reporting and auditing capabilities provide enterprises with granular visibility, compliance assurance, and governance across all automated processes. Its reporting engine generates detailed, customizable reports on job executions, workflow dependencies, SLA performance, resource utilization, and user activity, with support for real-time dashboards as well as scheduled. Audit trails capture every system action, configuration change, and user interaction, ensuring full traceability and accountability in line with regulatory requirements.

By combining reporting, auditing, and performance metrics, Automic Automation gives enterprises complete visibility into how automation operates, where risks exist, and how processes can be optimized. Reporting provides flexible, role-specific insights; auditing guarantees compliance and accountability; and performance metrics deliver the data needed for continuous improvement. Together, these capabilities ensure that automation is not only powerful but also transparent, governed, and aligned with both operational and business goals.

Reporting

Automic Automation includes a dedicated reporting engine that transforms raw execution data into meaningful insights. Reports can be generated on demand or scheduled to run automatically, providing visibility into key dimensions such as job execution history, workflow dependencies, SLA compliance, and resource utilization.

The reporting framework is designed to be both comprehensive and flexible. Users can generate real-time dashboards for immediate operational awareness, while also exporting data into formats such as PDF, CSV, or Excel for integration with business intelligence tools. Reports can be tailored by scope—covering a single workflow, a client, or the entire system—and filtered by criteria such as time window, status, or job type. This flexibility allows operations teams to track routine workload performance, while business stakeholders can review whether end-to-end processes are achieving their intended outcomes.

By making reporting both role-aware and customizable, Automic Automation ensures that each audience, from system administrators to auditors, has access to the information that matters most to them.

Auditing

Auditing is central to governance and compliance in automation. Automic Automation maintains a complete, immutable record of every system action, configuration change, and user interaction. This includes the creation and modification of workflows, updates to schedules, execution events, and security-related actions such as role or permission changes.

Each audit log entry includes detailed metadata, such as the user who performed the action, the exact timestamp, and the affected object. This granular visibility provides full traceability and accountability, enabling organizations to satisfy stringent regulatory requirements. For regulated industries, immutable audit trails offer proof that processes were executed as designed and that no unauthorized changes occurred.

In addition, audit data integrates with Automic Automation’s role-based access control and multi-tenancy model. This ensures segregation of duties, so that the same person cannot both design and approve a critical workflow without leaving a traceable record. Combined with versioning and change history tracking, auditing empowers organizations to investigate issues, roll back unwanted changes, and maintain compliance in highly regulated environments.

Performance Metrics

Performance metrics in Automic Automation go beyond tracking whether a job succeeded or failed. The platform continuously gathers operational and business-relevant metrics that help teams optimize both execution reliability and resource efficiency.

At the system level, Automic Automation tracks throughput, agent workload, and queue latency, giving administrators insight into infrastructure utilization and potential bottlenecks. At the workflow level, execution times are measured and analyzed against baselines, allowing teams to identify deviations that may indicate inefficiencies or risks. When paired with SLA and SLO monitoring, performance metrics provide a direct link between workload execution and business outcomes, showing whether processes complete within required timeframes.

Metrics can also be aggregated and visualized in trend analyses, enabling predictive insights into workload growth, seasonal spikes, and long-term capacity needs. For example, a recurring payroll workflow can be tracked over months to detect whether runtimes are steadily increasing, signaling the need for optimization before SLAs are threatened. These insights help organizations not only resolve current issues but also plan proactively for future demands.

Integration and Extensibility

Automic Automation can integrate with other enterprise tools, extending automation across IT ecosystems.

Automation and Integration with IT Systems

Automic Automation’s ability to automate and integrate with IT systems is built on a highly extensible, API-driven architecture that enables seamless orchestration across heterogeneous enterprise environments. It provides out-of-the-box connectors and action packs for widely used platforms—including SAP, Oracle, Microsoft, AWS, Azure, Google Cloud, Hadoop, Informatica, and ServiceNow—allowing organizations to automate end-to-end business and IT processes without custom scripting.

Through REST and SOAP APIs, command-line interfaces, and message-based triggers, Automic Automation integrates with virtually any system, including legacy mainframes, distributed applications, and modern cloud-native services. Its agent-based model ensures secure, reliable job execution across diverse infrastructures, while reusable objects and templates standardize automation logic for consistency and scalability. Event-driven automation allows processes to be triggered by real-time IT events such as file transfers, database changes, log updates, or service requests, ensuring tight alignment with operational workflows. Automic Automation also integrates with DevOps toolchains—including Jenkins, Git, Docker, Ansible, and Kubernetes—enabling automated CI/CD pipelines and infrastructure-as-code deployments.

Combined with its centralized governance, SLA management, and auditing features, this integration framework ensures that IT teams can unify disparate systems under a single orchestration layer, eliminate silos, and achieve true end-to-end automation across hybrid and multi-cloud environments.

Collaboration and Workflow Sharing

Automic Automation is designed to streamline team-based development, operations, and governance of automation across large, distributed enterprises. Its object-oriented design allows workflows, jobs, and automation logic to be encapsulated as reusable components that can be parameterized, versioned, and shared across teams and business units, reducing redundancy and ensuring standardization. Integrated version control and lifecycle management provide full traceability of changes, enabling collaborative development with rollback and auditability for compliance.

Role-based access control and permissions frameworks ensure that workflows can be securely shared, with granular privileges for designing, executing, or modifying automation objects, supporting segregation of duties in multi-team environments. Automic Automation’s library of reusable automation templates and action packs promotes knowledge sharing and accelerates development by giving teams access to proven blueprints for common integrations and processes. In addition, collaborative features such as approval workflows, annotation, and documentation fields embedded within automation objects foster cross-team transparency and alignment.

By combining reusable automation assets, secure sharing mechanisms, and built-in governance, Automic Automation enables organizations to establish a culture of collaborative automation development, where IT, DevOps, and business teams can co-create, reuse, and evolve workflows within a unified, controlled framework that scales across hybrid and multi-cloud environments.

Scalability and Availability

Automic Automation is engineered to deliver enterprise-grade scalability and reliability through its distributed execution model, enabling organizations to handle massive volumes of automated tasks across heterogeneous and geographically dispersed environments. Its distributed, agent-based architecture supports millions of concurrent job executions and can scale up to 100,000 agents and 100 million jobs per instance, with workload balancing and dynamic resource allocation ensuring optimal performance even under peak demand. A centralized Automation Engine coordinates execution through distributed agents deployed across on-premises, hybrid, and cloud infrastructures, while horizontal scaling of automation engines and native software clustering provide dynamic scalability without the need for specialized fault-tolerant hardware.

High availability is ensured through clustered engine deployments, redundant communication servers, and built-in failover mechanisms that automatically reroute jobs in the event of node or network failures. Database clustering, replication, and session checkpointing further enhance resilience by allowing workflows to resume without manual intervention after disruptions. For enterprise continuity, Automic Automation supports disaster recovery with multi-site replication and failover configurations, while real-time monitoring and predictive analytics identify potential bottlenecks and capacity constraints before they impact operations.

By combining elastic scalability, distributed execution, and enterprise-grade redundancy, Automic Automation ensures automation processes remain performant, resilient, and highly available across global IT landscapes.

Self-Service Automation and User Empowerment

These capabilities focus on enabling non-technical users to interact with and control automation tasks. Intuitive self-service portals allow business users to initiate jobs and workflows without needing deep technical expertise. This empowers users from various departments to automate routine tasks, reducing the burden on IT teams and increasing productivity. Along with self-service portals, Automic Automation incorporates role-based access control, ensuring that different levels of access can be assigned to users. This balances flexibility with security by allowing appropriate access to various automation functions based on user roles.

Automic Automation empowers business users, developers, and operations teams to independently trigger, monitor, and manage automated processes within a secure, governed framework—reducing reliance on centralized IT while maintaining enterprise-grade control. Through intuitive web-based portals, service catalogs, and role-based dashboards, users can access predefined automation workflows, templates, and runbooks tailored to their specific needs without requiring deep technical expertise.

Behind the scenes, Automic Automation enforces role-based access control, approval workflows, and policy-driven governance to ensure that self-service activities remain compliant with organizational standards and regulatory requirements. Automation objects can be parameterized and exposed as reusable services, enabling business functions such as finance, HR, and customer support to initiate end-to-end processes—like report generation, user provisioning, or batch data updates—on demand. Integration with ITSM platforms like ServiceNow allows self-service automation to be embedded into existing service catalogs, streamlining request fulfillment and reducing manual ticket handling. Real-time monitoring and SLA tracking provide users with transparency into execution status and outcomes, while automated remediation ensures consistency and reliability.

By combining accessibility with governance, Automic Automation’s self-service automation accelerates business agility, reduces operational bottlenecks, and enables broader adoption of enterprise automation without compromising security or compliance.