How Boomi Scribe streamlines documentation using AWS

Boomi Scribe alleviates documentation, one of the most persistent sources of technical debt for enterprise development teams. Enterprise developers often struggle to create and maintain documentation, especially when workflows (automated business processes) involve integrations with multiple enterprise applications and data sources. Boomi recognized this challenge and built Boomi Scribe, an AI-powered agent running on AWS that automates and streamlines the entire documentation process. Boomi Scribe automatically generates detailed documentation for the entire integration process, as developers work.

Boomi Scribe generates accurate, detailed, and user-friendly documentation using a multi-step approach, powered by AWS artificial intelligence and machine learning (AI/ML) services including Amazon Bedrock and Claude models. Boomi Scribe goes beyond automatic documentation. It provides concrete insights and version comparisons developers can use in planning and broader communication. Complete documentation also helps prevent gaps in auditing for business and compliance needs.

In this post, you will learn how Boomi Scribe automates integration process documentation, compares component versions to surface meaningful changes, and enhances developer productivity. It’s all backed by a scalable architecture built for the cloud on AWS.

Documenting workflows

Boomi integration processes can be represented as Directed Acyclic Graphs (DAGs), with each node corresponding to a specific step in a workflow. These workflows govern how data is retrieved, manipulated, routed, and delivered between source and destination systems. Without accurate documentation, these workflows can be unreadable to anyone who didn’t build them. This creates a significant problem for debugging, handoffs, and compliance.

Manual documentation pitfalls

Documenting these workflows was a manual, time-consuming task that led to inconsistencies and inefficiencies. But the key logical and technical details of each step must be captured for maintainability and stakeholder confidence. Version management and comparing changes between process versions was also tedious and error prone. Missed updates, incomplete feature descriptions, or inaccuracies caused misunderstandings that could result in rework and lost time. To scale to efficiently handle large volumes of processes and maintain accuracy, automation was needed.

Boomi Scribe’s solution

Boomi Scribe automates the entire documentation process. Boomi stores integration processes as XML files that contain metadata about the process, including how each step is connected and other related information. The Boomi Scribe agent starts by parsing these complex XML representations of integration processes to extract relevant features. These features are then transformed into a Directed Acyclic Graph (DAG) dot notation format, which serves as a structured and consumable input for the model. The agent then generates documentation by passing this input to AI models, powered by Amazon Bedrock and Anthropic’s Claude Haiku 4.5. Finally, the agent compares versions of DAGs to highlight component differences and changes.

By parsing and extracting data for each integration step and its features and organizing it into a DAG on each revision, accurate and complete data is passed to the model to create or update existing documentation. Developers no longer need to manually create or analyze documentation for each step of the process to accurately reflect their additions and changes.

Solution overview and architecture

The architecture for Boomi Scribe must be reliable, and it has to scale for use by developers across more than 33,000 Boomi customers. Boomi uses machine learning (ML) services in Amazon SageMaker AI to build and maintain models for classifying user intents. Boomi uses foundational large language models (LLMs), including Claude Haiku 4.5, in Amazon Bedrock to generate natural language documentation. Haiku is a few-shot learning model that Boomi uses to process DAGs and generate detailed descriptions. Boomi stores DAG files, generated documentation, and metadata in Amazon Simple Storage Service (Amazon S3). Amazon DynamoDB serves as an internal backend datastore to power system features and maintain service operations. Lastly, Boomi uses AWS Lambda to orchestrate the entire pipeline, from DAG parsing to documentation generation and comparison.

Context file format

<task_objective>
</task_objective>

<domain_context>
</domain_context>

<input_contract>
DAG
</input_contract>

<generation_rules>
</generation_rules>

<output_structure>
The generated document must include, in order:
- Objective
- Visual representation of the Process
- Process Metadata
- Business Context
- Process Steps and Functions
</output_structure>

<validation_conditions>
</validation_conditions>

<examples>
</examples>

Generated documentation and metadata are available for contextual reference in the Boomi Integration Canvas and in Boomi GPT.

Figure 1: Boomi Scribe architecture on AWS

Boomi Scribe’s workflow

The agent parses the DAGs to capture the details of the integration process. Then it generates comprehensive documentation including highlighting changes and insights from comparison with the previous version. And finally, it stores the results in Amazon S3.

DAG parsing

The integration process DAG is uploaded to Amazon S3 and an AWS Lambda function triggers the parsing of nodes and edges to represent integration steps. The following example DAG illustrates the relevant features that get extracted from the more complex XML representations of integration processes and the dot notation format. These features and format align to the input features that AI models need to understand the workflow and the integration details to generate the documentation.

digraph G {
    # Process-level attributes
    version="2";
    created_date="2023-09-14T02:15:26Z";
    modified_date="2024-10-28T23:30:06Z";
    process_name="Send Exception Mail";
    folder_full_path="/path/to/folder";
    number_of_connected_shapes="8";
    number_of_subprocess_components="0";
    sub_process_component_names="";
    number_of_map_components="0";
    map_component_names_info="";
    # Node definitions
    start[shapenumber="shape1", shapetype="start", shapename="Start", label="Main_NoData"];
    documentproperties[shapenumber="documentproperties", shapetype="documentproperties", shapename="Document Properties", label="Document Properties", name="NA"];
    message[shapenumber="message", shapetype="message", shapename="Message", label="Message"];
    dataprocess[shapenumber="dataprocess", shapetype="dataprocess", shapename="Data Process", label="Data Process"];
    catcherrors[shapenumber="catcherrors", shapetype="catcherrors", shapename="Try/Catch", label="Try/Catch", error="exception", default="connectoraction"];
    exception[shapenumber="exception", shapetype="exception", shapename="Exception", label="Exception"];
    connectoraction[shapenumber="connectoraction", shapetype="connectoraction", shapename="Connector", label="mail", actionType="GET", connectorname="Mail", connectorType="mail", objectName="NA", operationName="NA", operationType="NA", requestProfileName="NA", responseProfileName="NA"];
    stop[shapenumber="stop", shapetype="stop", shapename="Stop", label="Stop"];
    # DAG
    start -> documentproperties documentproperties -> message message -> dataprocess dataprocess -> catcherrors catcherrors -> exception catcherrors -> connectoraction connectoraction -> stop;
}

Documentation generation

The agent passes the parsed DAG to Claude Haiku 4.5 through Amazon Bedrock, where the model generates a summary consisting of high-level overviews of the workflow for stakeholders along with a process diagram. This is followed by a section for process metadata, including its name, version, dates, paths, and number of steps and components. The next section provides the business context for the process. Lastly, a process steps and functions section is generated with step-by-step descriptions for each step in the workflow, providing concise details about the process and context-sensitive descriptions tailored to Boomi’s integration processes.

Figure 2: Boomi Scribe generated documentation showing the process overview, metadata, and step functions

Component comparison

The current and previous versions of DAGs are compared using a proprietary algorithm in the Lambda layer that details changes, including a summary that also provides specific observations developers can act on. This is followed by sections for additions, modifications, and deletions broken out and labeled for each step.

Figure 3: Boomi Scribe version comparison showing additions, modifications, and deletions between versions 1 and 3

Storage and retrieval

The generated documentation can be viewed contextually within the tools, and the user can copy or download it in multiple formats, such as PDF and HTML.

Figure 4: Boomi Scribe documentation panel in the Boomi Process Canvas with download and copy options

Benefits

Deployed processes average 42 recorded versions, with a median of 16. This is a strong signal that teams can generate multiple documentation versions. Versioning provides the flexibility to refer to prior versions when needed. Boomi Scribe makes sure documentation is consistent, accurate, and stays current across the full process lifecycle without manual effort (or manual errors), scaling to handle hundreds of processes per customer each day without performance bottlenecks. Multiple case studies have shown that Boomi Scribe has delivered significant time savings, reducing time spent on documentation by up to 85 percent.

Conclusion

Organizations want to reduce costs and improve productivity by alleviating unnecessary toil. Maintaining software documentation, especially for long, complex workflows, has been a long-standing, problematic burden and source of technical debt. Boomi Scribe uses AI/ML tools from AWS to automate documentation and version management for complex workflows in a proven, scalable, and reliable solution.

Next steps

To learn more about how Boomi can help streamline your integration processes, visit Boomi Integration & Automation and Boomi AI Agents. To learn more about building and managing your own AI agents for your own use cases, visit Amazon Bedrock Agents and Boomi Agentstudio.

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