Workflow automation
Workflow automation is the layer of GLBNXT Platform that connects AI capabilities to the processes, systems, and data sources your organisation already operates. It enables your team to build end-to-end AI-powered processes that move information between components, trigger actions based on events or conditions, and coordinate model interactions, data retrieval, and external system calls into coherent automated workflows without writing custom integration code for every connection.
On GLBNXT Platform, workflow automation is available as a managed service within your environment. Your team focuses on designing and building workflows. GLBNXT handles the infrastructure that runs them.
This section explains what workflow automation covers on GLBNXT Platform, the patterns it enables, and how to approach building workflows that are reliable, maintainable, and ready for production.
What Workflow Automation Is For
Workflow automation is the right architectural choice when your use case involves multiple steps, multiple systems, or processes that need to run automatically in response to events, schedules, or conditions rather than direct user interaction.
Common use cases in this category include:
Automated document processing pipelines that ingest, classify, summarise, and route incoming documents without manual handling
Scheduled reporting workflows that retrieve data, run AI-powered analysis, and distribute outputs to stakeholders on a defined cadence
Event-driven notification and escalation systems that monitor data sources and trigger AI-generated communications when defined conditions are met
Data enrichment pipelines that pass records through AI processing steps and write enriched outputs back to source systems
Multi-step approval and case management workflows that combine AI reasoning with human review checkpoints and downstream system updates
Integration workflows that synchronise AI-generated outputs with CRM, ERP, or other enterprise systems your organisation uses
If your use case requires AI to participate in a process rather than simply respond to a query, workflow automation is the layer that makes it possible.
Core Concepts
Triggers
Every workflow begins with a trigger. A trigger is the event or condition that causes a workflow to start executing. Triggers can be time-based, such as a scheduled run at a defined interval, event-based, such as a new file arriving in a storage location or a webhook received from an external system, or manual, initiated by a user action in the platform console or an API call from another system.
Choosing the right trigger type for a workflow is the first design decision. Time-based triggers are appropriate for processes that run on a predictable cadence. Event-based triggers are appropriate for processes that need to respond to real-world events as they occur. Manual triggers are appropriate for processes that should run on demand rather than automatically.
Steps and Nodes
Workflows are composed of steps, each of which performs a discrete action. A step might call a model endpoint to generate a summary, query a database to retrieve a record, call an external API to fetch live data, transform the structure of a data payload, apply conditional logic to determine which path the workflow should follow, or write an output to a storage location or downstream system.
Steps are connected in sequence to form the workflow. The output of one step becomes the input to the next, with data flowing through the workflow as it executes. Conditional branching allows workflows to follow different paths based on the content or result of a previous step.
Credentials and Integrations
Workflow automation frequently involves calling external services that require authentication. On GLBNXT Platform, all credentials used by workflows are managed through the platform secrets vault. Workflows reference credentials by name rather than by value, and the vault injects the actual credential at runtime. Your workflow definitions never contain sensitive values, and credential rotation does not require changes to workflow configuration.
GLBNXT Platform workflows support integration with a broad range of external services through managed connectors. These connectors handle the authentication, request formatting, and response parsing required to interact with external APIs, reducing the work required to connect a workflow to a new system to a matter of configuration rather than custom code.
Error Handling and Retry Logic
Production workflows need to handle failures gracefully. External services are sometimes unavailable, API calls occasionally time out, and data inputs do not always conform to expected formats. Workflows built on GLBNXT Platform should include explicit error handling at steps where failures are likely, with retry logic configured for transient failures and fallback paths or error notifications defined for failures that cannot be recovered automatically.
Building error handling into workflows from the start is significantly less effort than adding it after a production incident. Every workflow that calls an external system or processes user-supplied data should have a considered approach to what happens when that step fails.
Building Workflows on GLBNXT Platform
Visual Workflow Builders
The primary low-code approach to workflow automation on GLBNXT Platform is through visual workflow builders available in your environment. These tools provide a node-based interface where each step in a workflow is represented as a node that can be configured, connected, and tested interactively.
Visual builders are well suited for building and iterating on workflow logic quickly, for processes that follow well-established patterns, and for teams who want to deliver working automation without writing significant amounts of custom code. They provide a clear visual representation of workflow structure that makes complex multi-step processes easier to understand, review, and maintain.
Pre-built node types cover a wide range of common actions including HTTP requests, database queries, file operations, conditional logic, data transformation, and integration with popular external services. Model endpoint calls are available as native node types, making it straightforward to incorporate AI reasoning, summarisation, classification, or generation steps into any workflow.
Full-Code Workflow Development
For workflows that require custom logic not covered by pre-built node types, complex data transformations, or precise control over execution behaviour, the platform supports full-code workflow development. Custom code steps can be inserted at any point in a workflow, receiving the current data payload as input and returning a transformed payload for the next step.
Full-code development is appropriate when the logic of a workflow step cannot be expressed through configuration alone, when performance requirements demand optimised custom processing, or when a workflow needs to interact with a proprietary system that does not have a pre-built connector available.
As with other areas of the platform, low-code and full-code approaches can be combined freely within the same workflow. A workflow might use pre-built connectors for standard integrations, a custom code step for a specific data transformation, and a pre-built model node for AI processing, all within a single automated process.
AI Integration Patterns in Workflows
Incorporating AI model calls into automated workflows creates a range of powerful patterns that are not possible with traditional automation alone.
Classification and Routing
A language model can classify incoming data, documents, or messages and route them to different workflow paths based on the classification output. An incoming support request might be classified by topic, urgency, and required expertise before being routed to the appropriate handling process. A document ingestion pipeline might classify files by type and apply different processing logic to each category.
Classification and routing patterns remove the need for manually defined rules for every possible input type, replacing brittle rule-based logic with model-driven categorisation that generalises across inputs that rule sets would not anticipate.
Summarisation and Extraction
Workflows can pass documents, records, or data payloads to a model for summarisation, structured data extraction, or key information identification as part of an automated process. The extracted or summarised output can then be written to a database, included in a notification, or passed to the next step in the workflow.
This pattern is particularly valuable for document-heavy processes where the volume of incoming content makes manual review impractical, and where downstream steps require structured information that is embedded within unstructured documents.
Generation and Drafting
Workflows can use model generation capabilities to produce first-draft outputs such as email responses, report sections, analysis summaries, or structured documents as part of an automated process. Generated drafts can be written to a staging location for human review before sending, or delivered directly to recipients in workflows where review is not required.
Generation patterns work well in combination with retrieval, where the workflow first retrieves relevant context from a knowledge base or data source and then passes that context to the model to ground the generated output in accurate information.
Evaluation and Quality Checking
Workflows can include model-powered evaluation steps that assess the quality, completeness, or compliance of outputs generated earlier in the process. An evaluation step might check whether a generated response meets defined quality criteria before it is delivered, flag content that falls outside defined boundaries for human review, or score outputs against a rubric and route them accordingly.
Evaluation steps add a quality gate to automated processes that would otherwise rely entirely on the output of a single model call.
Connecting Workflows to the Broader Platform
Workflows on GLBNXT Platform do not operate in isolation. They connect to and coordinate with the other components available in your environment.
Workflows can trigger RAG retrieval to ground AI steps in organisational knowledge. They can read from and write to Postgres for structured data and state management. They can store and retrieve files from MinIO at any step in the process. They can call model endpoints in the Model Hub for any AI processing step. And they can expose webhook endpoints that other systems can call to trigger a workflow from outside the platform.
This connectivity means that workflows are not a separate automation layer sitting alongside your AI applications. They are the orchestration fabric that binds platform components together into complete end-to-end solutions.
Operational Considerations
Testing and Validation
Workflows should be tested with representative inputs before being deployed to production. Visual workflow builders provide execution history and step-by-step output visibility that makes it straightforward to trace exactly what happened during a test run and identify where logic needs adjustment.
Testing should cover not only the happy path but also edge cases and error conditions. How does the workflow behave when an external API is unavailable? What happens when a model returns an unexpected output format? What is the behaviour when a database query returns no results? Answering these questions during testing prevents production incidents caused by inputs or conditions that were not anticipated during design.
Monitoring and Alerting
All workflow executions on GLBNXT Platform are logged and visible through the Monitoring and Observability area of the platform console. Execution history, step-level outputs, error events, and performance metrics are available for every workflow run. This visibility makes it straightforward to identify patterns of failure, monitor throughput for high-volume workflows, and investigate individual executions when an unexpected outcome occurs.
For critical production workflows, configure alerting on failure events so that your team is notified immediately when a workflow encounters an error that requires attention. See the Observability and Monitoring section for guidance on configuring workflow-level alerting in your environment.
Version Control and Change Management
Workflows that run in production are software artefacts and should be treated accordingly. Changes to workflow logic, prompt configurations, model selections, and integration settings should be reviewed and approved before being applied to production. Maintaining version history for workflow configurations allows your team to roll back to a previous state if a change introduces unexpected behaviour.
For workflows that process sensitive data or operate in regulated contexts, change management for workflow configurations should align with the governance processes your organisation applies to other software changes.
Getting Started
The recommended starting point for workflow automation on GLBNXT Platform is to identify a well-defined, repeatable process in your organisation that currently involves manual steps and would benefit from automation with AI capability added at one or more points.
A practical first workflow follows this sequence:
Map the current manual process step by step, identifying inputs, outputs, decisions, and the systems involved at each stage
Identify which steps involve AI capability, such as classification, summarisation, extraction, or generation, and which are data movement or integration steps
Build the workflow in the visual builder, starting with the data flow and integration connections before adding AI steps
Test the workflow with representative inputs and validate outputs at each step before proceeding to the next
Add error handling and retry logic for steps that call external systems or process variable inputs
Deploy to production and monitor execution history through the platform console during the initial production period
For use cases that combine workflow automation with conversational AI, see the AI Assistants and Chat Interfaces section. For workflows that incorporate retrieval from organisational knowledge, see the RAG and Knowledge Systems section.
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