This disclosure is published as part of Intellectual Frontiers Open Innovation Initiative. It is not a patent filing, but a technical blueprint shared to promote transparency, collaboration, and to prevent overlapping patent claims in this domain.
ABSTRACT
The disclosure provides a computer-implemented system and method for document-centric orchestration and execution of multi-language workflows. A structured document, such as a Markdown-based artifact, comprises both executable and non-executable content segments. A runtime execution system parses the document to identify executable segments using predefined syntactic delimiters, constructs an execution sequence based on document structure, dependencies, or embedded metadata, and selectively executes the identified segments across one or more heterogeneous execution environments.
The system further includes validation mechanisms configured to enforce type constraints, schema definitions, and structural contracts on inputs and outputs, thereby ensuring deterministic and reproducible execution. Execution outputs are captured as structured artifacts and persistently stored in association with corresponding document segments. Additionally, provenance and lineage metadata, including execution parameters, timestamps, and dependency relationships, are generated and maintained to support traceability and auditability.
BACKGROUND OF THE INVENTION
Field of the Invention
The present disclosure relates generally to the fields of software engineering, and workflow orchestration, and more particularly to systems and methods for executing and managing multi-language computational workflows using document-based representations.
Description of the Related Art
Modern data engineering, DevOps, analytics, and artificial intelligence (AI) systems rely on increasingly complex workflows that integrate multiple programming languages, execution environments, and data processing steps. Such workflows commonly involve combinations of structured query language (SQL), scripting languages, configuration files, application programming interfaces (APIs), and machine learning models.
Conventional approaches to implementing such workflows are characterized by fragmentation across multiple artifacts. Execution logic is typically distributed among scripts, configuration files (e.g., YAML or JSON), notebook environments, pipeline definitions, and external documentation. As a result, the relationship between documentation, execution logic, and resulting outputs is often indirect and difficult to maintain. This fragmentation introduces challenges in reproducibility, auditability, and consistency, particularly in environments requiring deterministic execution and traceable results.
Existing workflow orchestration systems, such as directed acyclic graph (DAG)-based schedulers and continuous integration/continuous deployment (CI/CD) pipelines, focus primarily on task scheduling and execution management. However, such systems generally rely on external configuration definitions and do not integrate human-readable documentation with executable logic in a unified representation. Furthermore, these systems often lack mechanisms for embedding execution outputs, provenance data, and lineage information directly within the workflow specification.
Notebook-based environments, including interactive computational notebooks, attempt to combine code and documentation within a single interface. However, such systems typically lack deterministic execution guarantees, enforce weak or absent schema validation between steps, and do not provide robust orchestration capabilities across heterogeneous execution environments. Additionally, outputs generated within notebook environments are often not treated as first-class, version-controlled artifacts, thereby limiting traceability and reproducibility.
Several prior art references have attempted to address aspects of integrating documentation with executable content. For example, U.S. Patent Application Publication No. US20230281378A1, titled “Markdown data content with action binding,” describes techniques for associating executable actions with Markdown content. Similarly, U.S. Patent Application Publication No. US20220405065A1, titled “Model Document Creation in Source Code Development Environments using Semantic-aware Detectable Action Impacts,” discloses methods for generating documents that reflect code changes and their semantic impacts. U.S. Patent No. 11,954,429, titled “Automated notebook completion using sequence-to-sequence transformer,” addresses automated generation and completion of notebook content using machine learning techniques.
In addition to patent literature, various tools and frameworks have emerged that explore executable documentation paradigms. For instance, products such as RUNME provide Markdown-based DevOps notebooks that allow execution of embedded commands. Similarly, the Executable Books Project describes approaches for integrating executable code within documentation formats for reproducible research.
Despite these developments, existing solutions remain limited in several respects. Specifically, prior approaches do not provide a unified, deterministic, and type-validated execution model in which documentation, execution logic, and structured outputs are fully integrated into a single, version-controlled artifact. Moreover, existing systems generally fail to capture execution outputs as first-class entities with associated provenance and lineage metadata embedded directly within the source representation.
Accordingly, there exists a need for improved systems and methods that overcome the aforementioned limitations by enabling unified, reproducible, and auditable workflow execution across heterogeneous technologies within a single, document-centric framework.
SUMMARY OF THE INVENTION
The present disclosure provides systems and methods for executing, orchestrating, and materializing computational workflows using document-based representations. In particular, the disclosed subject matter enables the use of human-readable documents, such as Markdown files, as unified artifacts that combine documentation, executable logic, and structured outputs within a single, coherent framework.
In one aspect, a computer-implemented method is provided for processing a document comprising a plurality of content segments, wherein at least a subset of the content segments correspond to executable instructions. The method includes parsing the document to identify executable segments based on predefined syntactic delimiters, constructing an execution sequence based on at least one of document structure, declared dependencies, or embedded metadata, and executing the identified executable segments using one or more execution environments.
In certain embodiments, non-executable content within the document is preserved as contextual documentation and is not executed, while executable segments are selectively processed by the system. The execution of each segment produces one or more outputs, which are captured as structured artifacts and associated with corresponding segments of the document.
In another aspect, the system includes a validation component configured to enforce type constraints, schema definitions, or structural contracts on inputs and outputs of the executable segments. Such validation ensures deterministic and reproducible execution by preventing propagation of incompatible or ambiguous intermediate results across dependent segments.
In further embodiments, the system supports execution of heterogeneous instruction types within a single document, including but not limited to database queries, scripting commands, application programming interface (API) calls, and machine learning or language model prompts. A modular or plugin-based architecture enables integration with multiple external systems and execution environments without requiring modification of the document structure.
In yet another aspect, the system maintains provenance and lineage information associated with each executed segment and its corresponding outputs. The captured outputs and associated metadata may be stored, versioned, and referenced within the same document or across multiple documents, thereby enabling traceability, auditability, and reproducibility of the workflow.
In some embodiments, the system is implemented as a runtime engine that processes the document, executes the identified segments in a sequential or dependency-aware manner, and materializes outputs for downstream consumption, deployment, or analysis. The document thereby functions as an executable specification, an operational workflow, and a record of execution.
Accordingly, the disclosed systems and methods provide a unified, document-centric approach to workflow orchestration that reduces fragmentation between documentation, code, and execution artifacts, while improving reliability, traceability, and scalability across heterogeneous computational environments.
Referring now to FIG. 1, an exemplary system 100 for executing and orchestrating multi-language workflows using document-based representations is illustrated. The system 100 includes a user or authoring system 102, a document 104, a runtime execution system 106, one or more external execution environments 108A–108N, an artifact storage repository 110, a provenance and metadata store 112, and one or more consumer systems 114. The user or authoring system 102 is communicatively coupled to the document 104. The document 104 is provided as input to the runtime execution system 106. The runtime execution system 106 is operatively coupled to the one or more external execution environments 108A–108N, the artifact storage repository 110, and the provenance and metadata store 112. The artifact storage repository 110 and the provenance and metadata store 112 are further communicatively coupled to the one or more consumer systems 114.
In operation, the user or authoring system 102 is configured to generate the document 104. The document 104 comprises a plurality of content segments, including one or more non-executable content segments and one or more executable content segments. The one or more non-executable content segments includes human-readable text, while the one or more executable content segments includes instructions configured to be executed by the runtime execution system 106.
The document 104 is received by the runtime execution system 106. The runtime execution system 106 is configured to process the document 104 and to identify the one or more executable content segments within the document 104 based on one or more predefined syntactic delimiters or structural indicators. The runtime execution system 106 is further configured to selectively execute the one or more executable content segments while preserving the one or more non-executable content segments as contextual information.
The runtime execution system 106 is configured to invoke at least one of the one or more external execution environments 108A–108N to execute the one or more executable content segments. The one or more external execution environments 108A–108N may include, without limitation, a database system, an operating system shell, an application programming interface (API) service, or a machine learning service. The runtime execution system 106 transmits execution instructions to the one or more external execution environments 108A–108N and receives one or more outputs generated in response to execution of the one or more executable content segments. In some embodiments, the runtime execution system 106 determines an execution order of the one or more executable content segments based on at least one of document structure, implicit ordering, or dependency relationships.
The runtime execution system 106 is further configured to store the one or more outputs in the artifact storage repository 110. The artifact storage repository 110 is configured to persist the one or more outputs as structured artifacts associated with the document 104. The runtime execution system 106 is additionally configured to generate metadata associated with execution of the one or more executable content segment.
The runtime execution system 106 stores the metadata in the provenance and metadata store 112. The metadata includes, without limitation, execution parameters, timestamps, dependency relationships, and lineage information corresponding to the one or more outputs. The provenance and metadata store 112 thereby maintains traceability of execution events associated with the document 104.
The one or more consumer systems 114 are configured to access at least one of the artifact storage repository 110 or the provenance and metadata store 112. The one or more consumer systems 114 utilize the one or more outputs and the metadata for downstream processing, reporting, visualization, or integration into one or more applications.
Accordingly, the system 100 provides a unified architecture in which the document 104 serves as a combined representation of documentation and executable workflow logic, and in which execution results and associated metadata are persistently stored and made available for downstream consumption.
Referring now to FIG. 2, an expanded view of the runtime execution system 106 of FIG. 1 is illustrated. The runtime execution system 106 includes a document ingestion module 202, a parsing module 204, a segment classification module 206, an orchestration module 208, a validation module 210, an execution engine 212, an environment adapter layer 214, an output capture module 216, an artifact materialization module 218, a provenance and metadata generator 220, and a document binding module 222. The document ingestion module 202 is communicatively coupled to the parsing module 204. The parsing module 204 is communicatively coupled to the segment classification module 206. The segment classification module 206 is communicatively coupled to the orchestration module 208. The orchestration module 208 is communicatively coupled to the validation module 210 and the execution engine 212. The execution engine 212 is operatively coupled to the environment adapter layer 214. The execution engine 212 is further communicatively coupled to the output capture module 216. The output capture module 216 is communicatively coupled to the artifact materialization module 218 and the provenance and metadata generator 220. The artifact materialization module 218 and the provenance and metadata generator 220 are each communicatively coupled to the document binding module 222.
In operation, the document ingestion module 202 is configured to receive the document 104 and to prepare the document 104 for processing by normalizing formatting, encoding, or structural representations. The document ingestion module 202 provides the document 104 to the parsing module 204.
The parsing module 204 is configured to analyze the document 104 and to identify the plurality of content segments within the document 104. The plurality of content segments may include text segments, code segments, or mixed-format segments, and the like as discussed above. The parsing module 204 generates a structured representation of the document 104 that preserves ordering and positional information of the plurality of content segments.
The segment classification module 206 is configured to classify the plurality of content segments into the one or more executable content segments and the one or more non-executable content segments. The segment classification module 206 performs the classification based on one or more predefined syntactic delimiters or structural indicators associated with the plurality of content segments. In some embodiments, the one or more predefined syntactic delimiters include fenced code blocks, quoted segments, tagged sections, or other identifiable markers that designate executable instructions. The segment classification module 206 outputs classification metadata associated with each of the plurality of content segments.
The orchestration module 208 is configured to determine an execution order for the one or more executable content segments. The orchestration module 208 determines the execution order based on at least one of document structure, positional ordering within the document 104, declared dependencies, or inferred relationships between the one or more executable content segments. In some embodiments, the orchestration module 208 constructs an execution graph representing dependencies and execution flow among the one or more executable content segments.
The validation module 210 is configured to validate the one or more executable content segments prior to execution. The validation module 210 applies one or more validation rules, including type validation, schema validation, or structural constraint enforcement, to inputs and expected outputs associated with the one or more executable content segments. The validation module 210 prevents execution of an executable content segment when a validation condition is not satisfied.
The execution engine 212 is configured to execute the one or more executable content segments in accordance with the execution order determined by the orchestration module 208. The execution engine 212 selectively processes only the one or more executable content segments while preserving the one or more non-executable content segments as non-executed contextual data.
The execution engine 212 invokes the environment adapter layer 214 to interface with one or more external execution environments 108A–108N of FIG. 1. The environment adapter layer 214 includes one or more adapters configured to translate execution instructions into environment-specific commands. The one or more adapters enable execution across heterogeneous systems, including database engines, operating system environments, application programming interfaces, and machine learning services.
The output capture module 216 is configured to receive one or more outputs generated by execution of the one or more executable content segments. The output capture module 216 structures the one or more outputs into machine-readable formats and associates the one or more outputs with corresponding executable content segments.
The artifact materialization module 218 is configured to persist the one or more outputs as structured artifacts in the artifact storage repository 110 of FIG. 1. The artifact materialization module 218 associates each of the one or more outputs with identifiers corresponding to the document 104 and the respective executable content segments.
The provenance and metadata generator 220 is configured to generate metadata associated with execution of the one or more executable content segments. The metadata includes, without limitation, execution timestamps, input parameters, execution status, dependency relationships, and lineage information. The provenance and metadata generator 220 provides the metadata to the provenance and metadata store 112 of FIG. 1.
The document binding module 222 is configured to associate the one or more outputs and the metadata with corresponding locations within the document 104. The document binding module 222 enables traceability between the plurality of content segments, the one or more executable content segments, and the one or more outputs generated therefrom.
Accordingly, the runtime execution system 106 provides a modular and extensible architecture for parsing, classifying, validating, executing, and materializing document-based workflows while maintaining structured associations between document content, execution outputs, and provenance metadata.