TaskConstellation: Distributed Task Orchestration

• TaskConstellation is a dynamic, mutable DAG model for orchestrating distributed tasks, ensuring adaptive and efficient execution in heterogeneous environments.
• It enables asynchronous execution of atomic subtasks (TaskStars) with explicit dependency management and runtime graph reconfiguration for fault tolerance.
• Empirical evaluations, such as NebulaBench, demonstrate enhanced parallelism, reduced latency, and robust recovery under both transient and permanent failures.

A TaskConstellation refers to a distributed, dynamically evolving abstraction for orchestrating complex tasks across heterogeneous agents, devices, or platforms. Originally introduced in the context of digital agent orchestration, it has since found applications in satellite constellations, networked robotics, and multi-agent systems. TaskConstellation models the execution, coordination, and adaptation of finely decomposed subtasks (TaskStars) and their explicit dependencies (TaskStarLines) as a mutable directed acyclic graph (DAG). This formalism underpins resilient, adaptive, and efficient task execution in environments characterized by heterogeneity, partial failures, and asynchronous workflows.

1. Formal Definition and Structure

A TaskConstellation is formally a mutable DAG $\mathcal{C} = (\mathcal{T}, \mathcal{E})$, where:

• $\mathcal{T} = \{ t_1, t_2, ..., t_n \}$ is the set of atomic subtasks, each encoded as:

$$t_i = (\text{name}_i, \, \text{description}_i, \, \text{device}_i, \, \text{tips}_i, \, \text{status}_i, \, \text{dependencies}_i)$$

• $\text{name}_i$: symbolic identifier
• $\text{description}_i$: specification of action (natural language or structured)
• $\text{device}_i$: assigned endpoint
• $\text{tips}_i$: optional hints for agent execution
• $$\text{status}_i \in \{\mathsf{PENDING},\mathsf{RUNNING},\mathsf{COMPLETED},\mathsf{FAILED}\}$$
• $\text{dependencies}_i \subseteq \mathcal{T}$: immediate predecessors
  • $\mathcal{E} = \{ e_{i\to j} \}$ is the set of dependency edges (TaskStarLines), each as:

$$e_{i\to j} = (t_i, t_j, \text{type}, \text{description})$$

• $\text{type}$: unconditional/success/conditional, specifying edge semantics, e.g., whether $t_j$ awaits any termination, only successful completion, or custom runtime-evaluated conditions.

The DAG invariant ensures acyclicity, allowing clear topological scheduling and parallelism analysis. The model is designed to support mutability, so that the set of tasks and edges can be edited dynamically in response to runtime events.

2. Core Components and Workflow

The TaskConstellation orchestration is governed by several key components:

• TaskStars: Serve as indivisible executable units. Lifecycle states are $$\mathsf{PENDING} \rightarrow \mathsf{RUNNING} \rightarrow \mathsf{COMPLETED}/\mathsf{FAILED}$$, with the possibility of rescheduling after failure.
• TaskStarLines: Capture explicit control and data dependencies, interpreted by the orchestrator to expose potential concurrency (i.e., ready-to-run tasks).
• Constellation Orchestrator: A runtime agent that monitors DAG state, dispatches ready TaskStars asynchronously (immediately upon satisfaction of all predecessor completion), and manages DAG evolution, guaranteeing single-assignment, acyclicity, and immutability of terminal nodes.
• ConstellationAgent: An LLM-driven ReAct module that proposes atomic, idempotent task edits (e.g., add_task, remove_dependency) applied atomically with assignment lock protection.

Typical lifecycle:

1. TaskConstellation is initially constructed from user intent.
2. Orchestrator asynchronously monitors for ready $t$ with all dependencies $\mathsf{COMPLETED}$; dispatches via Agent Interaction Protocol (AIP).
3. Each TaskStar streams logs/results; completion triggers further DAG advances.
4. The ConstellationAgent issues dynamic graph edits for DAG refinement, e.g., error-triggered fallback task injection, subtask splitting, or dependency rewiring.
5. Adaptive recovery is engaged upon agent failures: failed TaskStars are re-queued, re-dispatched, or omitted per control edge semantics and criticality.

3. Asynchronous Execution and Mutability

A distinguishing characteristic of TaskConstellation is fine-grained asynchronous execution combined with a mutable DAG model:

• Tasks are dispatched immediately when all upstream dependencies are $\mathsf{COMPLETED}$, enabling parallel execution proportional to the inherent DAG width.
• New TaskStars and dependencies can be added on-the-fly, e.g., to accommodate dynamically discovered subtasks or to integrate fallback strategies after partial failures.
• Subtask splitting and branch merging are native operations, supporting runtime task refinement and DAG simplification.
• All edits are submitted via a consistent command protocol, executed atomically to maintain the graph invariants.

Adaptive recovery mechanisms provide robust operation under both transient and permanent device/agent failures. Transient drops result in task retry; permanent drops may lead to agent substitution or dependency relaxation for non-critical paths.

4. The Agent Interaction Protocol: Architecture and Reliability

The execution backbone of distributed TaskConstellation orchestration is the Agent Interaction Protocol (AIP), designed as a five-layer protocol stack over persistent WebSockets:

1. Message Schema Layer: Pydantic-validated message types, including TASK, COMMAND, HEARTBEAT, DEVICE_INFO.
2. Transport Layer: Persistent, ping/pong WebSocket with automatic reconnection.
3. Protocol Orchestration: Registration, session management, and hook ordering.
4. Resilience/Health Layer: Heartbeat tracking, exponential backoff, transparent failover.
5. Endpoint Orchestration: High-level facades for clients and servers.

During task dispatch, TASK messages are sent to endpoints, which may further decompose into device-native COMMANDS. Streaming of COMMAND_RESULTS is leveraged for immediate orchestrator updates.

Key safety checks:

• Single-assignment (I1): No TaskStar is executed on more than one device at a time.
• Acyclicity (I2): Every edit is checked for DAG cycles before commit.
• Immutable terminal nodes (I3): No task with status $\mathsf{RUNNING}$, $\mathsf{COMPLETED}$, or $\mathsf{FAILED}$ is overwritten.

Session continuity is provided for reconnecting agents, with state restoration ensuring execution resumption without loss.

5. Quantitative Evaluation: NebulaBench Results

TaskConstellation's properties were empirically validated in the NebulaBench evaluation:

• Setup: 55 heterogeneous cross-device workflows over 5 machines (Windows desktop, Ubuntu CPUs, Ubuntu A100 GPU). Tasks span 10 categories (logs, system, processes, data, DevOps, networking, browsing, orchestration, GPU, negative tests).
• Metrics:
  • Subtask Completion Rate: 83.3%
  • Task Success Rate: 70.9%
  • Mean Parallelism Ratio: 1.72 (average concurrent subtasks; peak $\sim$3.5)
  • End-to-End Latency: 243.7s average, a 31% reduction over the sequential baseline (352.5s)
• Fault Tolerance: Under injected transient and permanent agent failures, task completion and overall success rates degrade gracefully. Transient drops are automatically retried; permanent losses trigger orchestrator intervention. Global agent loss leads to safe abort.

These results demonstrate that mutable DAG orchestration of fine-grained, interdependent tasks across an open set of endpoints yields both high parallelism and strong resilience.

6. Applicability, Impact, and Broader Connections

TaskConstellation is domain-agnostic: any digital workflow decomposable as dependencies among atomic subtasks can be represented in this formalism. Within AI-powered digital agents, the abstraction allows LLMs and tool-driven agents to collaborate over arbitrary devices, dissolving the OS/device barrier and maximizing workflow flexibility. The underlying model is equally applicable to edge-cloud orchestration, robotic mission planning, and smart industrial systems requiring composable, fail-safe task management.

A key architectural contribution is the explicit exposure of the live DAG, enabling both human and agent-initiated dynamic updates, optimal exploitation of parallelism, and transparent, adaptive recovery. This approach converges with trends in high-throughput/low-latency distributed systems and sets a unified agenda for orchestrating multi-agent intelligence in heterogeneous digital ecosystems.