Agentic AI Tasks: Frameworks & Metrics

• Agentic AI Tasks are autonomous, multi-step processes characterized by dynamic task decomposition and adaptive execution.
• They employ frameworks such as DAG-based graphs to orchestrate independent and dependent tasks, enhancing efficiency and scalability.
• Evaluation metrics like Node F1, Tool F1, and SSI objectively assess both the structural fidelity and operational performance of these systems.

Agentic AI tasks are complex, multi-step activities executed autonomously by artificial intelligence systems endowed with dynamic planning, task decomposition, tool use, and adaptive decision-making. These systems, leveraging LLMs and other advanced neural architectures, move beyond static, reactive automation toward orchestrating entire workflows, making context-aware decisions, and adapting to evolving operational constraints. The following sections survey the foundational frameworks, methodological advancements, evaluation metrics, benchmarking practices, and empirical insights from recent research focused on the capabilities, implementation, and evaluation of agentic AI tasks (Gabriel et al., 29 Oct 2024).

1. Dynamic Task Decomposition and Execution Frameworks

Recent advancements have centered around autonomous frameworks that convert high-level user queries into structured, executable graphs of interdependent tasks. A central feature is the decomposition of input into a Directed Acyclic Graph (DAG), where each node is an actionable subtask and edges denote data or execution dependencies.

Core Components:

• Orchestrator: Transforms a complex query into a task graph, incorporating on-the-fly updates to the DAG as task requirements or tool availability evolve. This is achieved using asynchronous decomposition, enabling independent tasks to run in parallel and dependent tasks to execute sequentially.
• ToolManager: Employs dynamic, context-aware semantic filtering to select and embed those tools most relevant to each graph node. This mitigates the inefficiency of presenting the entire tool repertoire to the LLM at each invocation, enhancing system responsiveness.
• Delegator and GraphExecutor: Assign and execute tasks according to the dependencies encoded in the DAG, routing outputs through inter-task memory buffers and ensuring proper concurrent or sequential execution as dictated by the graph.

These components operationalize a flexible agentic workflow where multi-hop queries—those requiring bridging across several knowledge domains or tool invocations—can be decomposed, scheduled, and dynamically revised as execution proceeds.

2. Evaluation Metrics for Agentic Systems

Assessing agentic AI systems requires novel, multi-faceted metrics that can capture both the structural fidelity of task decomposition and the operational accuracy of task execution.

Introduced Metrics:

• Node F1 Score: Measures the precision and recall with which the agent decomposes a task into correct constituent nodes. Mathematically:

$$\text{F1}_\text{node} = \frac{2 \cdot \text{Precision}_\text{node} \cdot \text{Recall}_\text{node}}{\text{Precision}_\text{node} + \text{Recall}_\text{node}}$$

with precision and recall defined on the set of ground-truth and system-generated nodes.

• Tool F1 Score: Analogous to Node F1 but operates on the agent’s selection of tools per task node, rewarding systems that invoke relevant API calls or function modules with high specificity and coverage.
• Structural Similarity Index (SSI): Quantifies the global similarity between the agent-produced and ground-truth task graphs by integrating node label similarity (e.g., cosine similarity) and edge structure (Edge F1), typically as:

$$\text{SSI} = \frac{\text{Node Label Similarity} + \text{Edge F1 Score}}{2}$$

Additional metrics, such as Graph Edit Distance and Path Length Similarity, provide detailed insight into the agent's structural reasoning across sequential and parallel task settings.

Empirical evidence shows that, for sequential tasks, SSI is the strongest predictor of final answer quality ($r \approx 0.470$, $p < 0.001$), while Tool F1 is pivotal for parallel tasks ($r \approx 0.476$, $p < 0.001$), demonstrating the need for metrics that address both task graph topology and tool application accuracy.

3. Benchmarking and Specialized Datasets

Robust evaluation of agentic task performance requires datasets that reflect diverse, realistic scenarios spanning varied task complexities. The AsyncHow-based dataset developed in this research supports this by providing:

• Over 50 scenarios ranging from simple linear workflows to intricate graphs with multiple branches and dependencies.
• For each, explicit task graph generation functions (producing sequential or parallel graphs), detailed node and edge descriptions, and sequences of tool calls derived from synthetic Python APIs modeling real-world operations.

This dataset enables multifaceted assessment—task decomposition, tool selection, and overall output quality—against ground-truth annotations, supporting statistical analysis and ablation studies across agentic system designs.

4. Asynchronous Decomposition, Real-Time Adaptivity, and Scalability

A defining trait of advanced agentic AI frameworks is their asynchronous and adaptive execution model. By permitting parallel execution of independent subtasks and by enabling on-the-fly reconfiguration of the task graph in response to execution outcomes or tool unavailability, such frameworks achieve:

• Enhanced Responsiveness: Independent subtasks proceed in parallel, reducing critical path delays.
• Increased Scalability: Systems flexibly adjust task granularity according to computational resources and the complexity of the user query.
• Robustness: Dynamic task scheduling ensures ongoing progress even when some subtasks fail or require re-planning due to environmental changes.

Statistical analysis reveals that switching between coarse- and fine-grained decomposition, when coupled with dynamic tool selection and memory-passing between sub-tasks, yields measurable improvements in both throughput and final answer quality for tasks of increasing complexity.

5. Empirical and Statistical Insights

Comprehensive empirical analysis, including regression and correlation studies, establishes that neither structural nor operational metrics alone fully capture agentic system performance. Instead:

• Balanced Evaluation: Sequential task settings demand accurate graph topology and node decomposition, favoring structural metrics (SSI, Node F1).
• Parallel/Asynchronous Tasks: Require efficient, context-aware tool selection, thus operational metrics (Tool F1) dominate outcome prediction.
• Multiple regression models explain up to $\sim 39\%$ of observed variance in final answer quality, underscoring the predictive validity of these new metrics and the need to evaluate systems across both structural and dynamic execution dimensions.

6. Implications and Future Directions

Agentic task frameworks, as defined and evaluated in this research, establish a foundation for autonomous, context-adaptive AI systems capable of orchestrating complex workflows in dynamic environments. Key implications include:

• System Design: Adoption of DAG-based task decomposition, real-time tool adaptation, and asynchronous execution as core architectural motifs for agentic systems across domains.
• Evaluation Best Practices: Commitment to multidimensional metrics, with explicit focus on both task structure (SSI, Node F1) and operational effectiveness (Tool F1), particularly as system complexity and parallelism increase.
• Research Impact: The integration of specialized datasets, novel evaluation metrics, and rigorous statistical analysis provides a robust methodology for benchmarking, ablation, and targeted improvement of next-generation agentic AI systems.

This research delineates the essential ingredients—architectural, methodological, and evaluative—that underpin advanced agentic AI tasks, guiding both future system development and empirical paper of autonomous, adaptable, and scalable AI in real-world applications (Gabriel et al., 29 Oct 2024).