Orchestration API

The API provides endpoints to manage the complete lifecycle of a workflow instance. This includes:

• Instantiation: Launching a new execution of a workflow definition with specific input parameters.
• Suspension & Resumption: Pausing a running instance and later restarting it from the point of interruption.
• Termination: Gracefully stopping or forcefully killing an instance.
• Cancellation: Aborting a pending or running instance, often triggering any defined compensating transactions. This allows for dynamic, external control over process execution, enabling integration with user interfaces, event systems, or other business logic.

A primary function is to expose the real-time and historical state of workflow executions. Key queries include:

• Instance Status: Retrieve the current state (e.g., RUNNING, COMPLETED, FAILED) of a specific process instance.
• Variable Access: Fetch the values of runtime variables or context data managed by the workflow engine.
• Execution History: Obtain a detailed audit trail of steps executed, decisions made, and events processed.
• Task-Level Detail: Inspect the status and output of individual activities within the workflow. This capability is fundamental for building monitoring dashboards, debugging complex executions, and enabling human-in-the-loop decision points.

The API serves as an entry point for event-driven orchestration, allowing external systems to initiate or influence workflows based on real-world events. Common patterns include:

• Webhook Endpoints: Dedicated API endpoints that accept HTTP callbacks from other services to start a workflow.
• Signal Injection: Sending asynchronous signals or events to a specific running workflow instance to alter its course, often used for conditional branching or human approvals.
• Cron Trigger Management: Programmatically creating, updating, or disabling scheduled triggers (e.g., cron triggers) that launch workflows periodically. This transforms the orchestration engine from a batch scheduler into a reactive component of a distributed system.

Beyond runtime control, the API manages the workflow blueprints themselves, supporting Workflow-as-Code practices. Capabilities include:

• Definition Registration: Deploying new or updated workflow definitions (e.g., DAGs, state machines) described in a Workflow Definition Language (WDL) like YAML or ASL.
• Version Management: Handling multiple versions of a workflow definition, allowing for controlled rollouts and rollbacks.
• Validation: Pre-flight validation of workflow syntax and logic before deployment.
• Metadata Retrieval: Listing available workflows, their versions, and associated metadata. This enables CI/CD pipelines to automate the deployment of orchestration logic.

The API provides endpoints for system-level administration and bulk operations, crucial for platform engineers. This encompasses:

• Bulk Operations: Starting multiple instances, querying instances by filter, or applying actions (pause, resume) to groups of workflows.
• Queue Management: Inspecting and managing task queues, including purging or reprioritizing pending tasks.
• Engine Metrics: Accessing system-level telemetry such as queue depths, active instance counts, and average execution times.
• Maintenance Tasks: Triggering operations like checkpointing or archival of completed instances. These functions are essential for the orchestration observability and reliability of the platform.

The API allows for external intervention in failure scenarios, implementing robust fault tolerance in multi-agent systems. Key features include:

• Retry Invocation: Manually triggering retry logic on a failed task or an entire workflow instance.
• Exception Path Navigation: Directing a failed instance down an alternative error-handling path defined in the workflow.
• State Repair & Override: In advanced systems, allowing authorized administrators to modify the persisted state of a stuck instance to unblock execution.
• Circuit Breaker Control: Querying or resetting circuit breaker states for external service calls. This provides the operational control needed to maintain system resilience without requiring engine restarts.

A workflow engine is the core software component that executes predefined sequences of tasks by managing their state, routing data, and invoking activities according to a defined model. It is the runtime that an Orchestration API controls.

• Primary Function: Interprets workflow definitions and drives their execution.
• Key Responsibilities: State management, task scheduling, dependency resolution, and error handling.
• Examples: Apache Airflow, Temporal, AWS Step Functions, Camunda.

A Workflow Definition Language (WDL) is a domain-specific language or data format used to declaratively specify the structure, tasks, and control flow of an executable workflow. The Orchestration API accepts definitions written in this language to create new process types.

• Purpose: Provides a standardized, machine-readable blueprint for workflows.
• Common Formats: YAML, JSON, or custom DSLs (e.g., Amazon States Language for AWS Step Functions).
• Defines: Tasks, execution order, data flow, error handlers, and retry policies.

A process instance is a single, specific execution of a workflow definition. It maintains its own runtime state, variables, and history, which can be managed independently via the Orchestration API.

• Lifecycle: Created, started, paused, resumed, and terminated via API calls.
• State Persistence: The engine durably stores instance state to survive failures.
• Uniqueness: Each instance has a unique identifier for querying and management.

An activity is a discrete, executable unit of work within a workflow, such as a function call, API request, or human task, which is invoked by the workflow engine. The Orchestration API can often trigger activities directly or query their status.

• Types: System tasks (compute, database calls), service tasks (external APIs), user tasks (human-in-the-loop).
• Execution: Activities are the leaf nodes in a workflow graph that perform the actual business logic.
• Interface: Typically defined by a name, input parameters, and output expectations.

A Directed Acyclic Graph (DAG) is a finite directed graph with no cycles, used in workflow orchestration to model tasks as nodes and their dependencies as edges, ensuring a non-circular execution order. This is the most common structural model for workflows.

• Visual Representation: Provides a clear map of task dependencies and parallel execution paths.
• Acyclic Constraint: Prevents infinite loops in execution logic.
• Engine Role: The workflow engine traverses the DAG, executing tasks only when their dependencies are satisfied.

A state machine is a computational model consisting of a finite number of states, transitions between those states, and actions, used to define and control the execution logic of a workflow or process. Many orchestration engines use state machines as their core execution model.

• Components: States (e.g., 'Pending', 'Running', 'Failed'), Transitions (events that move between states), Actions (logic executed on entry/exit).
• Determinism: For a given input and current state, the next state is predictable.
• Standard: Often defined using standards like the Amazon States Language (ASL).