ARTI: Agentic Reasoning & Tool Integration

• ARTI is a paradigm that enables LLMs to autonomously decompose complex tasks using dynamic, DAG-based workflows and context-aware tool selection.
• The framework utilizes modular components such as the orchestrator, delegator, agents, and tool managers to optimize task scheduling and parallel execution.
• Empirical evaluations leverage metrics like Node F1, Tool F1, and SSI to measure workflow fidelity and effective tool integration in both sequential and parallel tasks.

Agentic Reasoning and Tool Integration (ARTI) denotes a paradigm in which LLMs and related agentic systems autonomously decompose complex tasks, plan and coordinate multi-hop workflows, and dynamically select and operate external tools. ARTI seeks to transcend the limitations of static reasoning and manual tool orchestration by embedding flexible, context-sensitive automation and execution strategies into AI systems. This approach is central for enabling scalable, robust, and generalizable agentic architectures that interact with external APIs, databases, software libraries, and other computational resources, particularly for multi-step, real-world tasks (Gabriel et al., 29 Oct 2024).

1. Advanced Agentic Frameworks: Structure and Operation

Modern ARTI frameworks are architected to transform arbitrary user queries into executable workflows via structured task graph generation and tool selection. A central orchestrator component parses multi-hop problems and emits a directed acyclic graph (DAG), where nodes represent discrete tasks and edges encode dependency structure. The modular framework is typically composed of:

• Orchestrator: Converts user queries into DAGs, resolving optimal task decomposition granularity (coarse or fine), and optimizing for sequential or parallel scheduling.
• Delegator: Assigns subtasks to agents, manages data flow (context, outputs), and aggregates local results.
• Agents: LLM-driven modular executors that perform individual subtasks, often capable of generating ad hoc tool invocations (e.g., on-demand Python code).
• ToolManager and Tools: Curates and provides semantic access to a library of Python functions and domain tools, selected via semantic similarity (e.g., using FAISS-based embedding retrieval).
• Executor: Schedules and dispatches tasks as dictated by DAG dependencies—enabling parallelism for independent branches, enforcing correct sequencing otherwise.

Dynamic task decomposition underpins responsiveness and scalability. Systems explicitly optimize for the critical path, minimizing redundant computation when DAGs can be pruned or parallelized. Coarse decompositions reduce orchestration overhead; fine-grained ones maximize operational efficiency for independent subproblems.

Tool calls are selected via contextual semantic matching: each subtask description is embedded, and tools are ranked by vector similarity. For example:

def filter_tools_by_tasks(self, task_list): # Filters available tools for each task using semantic similarity ...

This architecture supports recursive, real-time adaptation—accommodating delayed responses, failure recovery, and evolving external environments.

2. Novel Evaluation Metrics for Agentic Systems

Accurate evaluation of ARTI systems requires metrics that capture both structural workflow fidelity and operational tool-use correctness. The core metrics introduced are:

• Node F1 Score: Measures alignment between predicted and gold graph task nodes.

$$\text{Precision}_{\text{node}} = \frac{TP_{\text{node}}}{TP_{\text{node}} + FP_{\text{node}}}; \quad \text{Recall}_{\text{node}} = \frac{TP_{\text{node}}}{TP_{\text{node}} + FN_{\text{node}}}$$

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

• Tool F1 Score: Evaluates whether the correct tools are chosen and invoked.

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

• Structural Similarity Index (SSI): Synthesizes node label similarity (cosine similarity over node labels between agent and gold graphs) and edge F1 (structural consistency).

$$\text{NodeLabelSim} = \frac{1}{|N_{\text{expected}}|} \sum_{i} \max_j \cos(\text{Label}_i, \text{Label}_j)$$

$$\text{SSI} = \frac{\text{NodeLabelSim} + \text{Edge F1}}{2}$$

Additional metrics include Edge F1, Path Length Similarity, Graph Edit Distance, and a complexity score $|V| + |E|$ that quantifies graph size and density. SSI and node-level metrics most strongly predict success for sequential workflows, while tool-related metrics dominate in parallel contexts.

3. AsyncHow-Based Dataset and Experimental Design

The ASyncHow-based dataset is specifically constructed to benchmark granular ARTI performance across different task complexities. Each dataset instance comprises:

• Scenario name, DAG with gold-standard task nodes/edges, domain-specific Python tool functions
• Predefined sequences of tool calls
• Gold-standard answers and complexity labels ($|V| + |E|$), spanning both parallel and sequential task graphs

This setup enables controlled, repeatable, and domain-agnostic evaluation—agents' task graphs and tool traces are compared directly to gold references, supporting fine-grained error analysis. Automated generation pipelines incorporating LLMs and templated code facilitate scalable scenario creation and validation.

4. Empirical Analysis: Metric Importance and Scalability

Empirical results demonstrate that ARTI system performance is tightly linked to task structure:

• Sequential tasks: Structural metrics (SSI, Node F1, Node Label Similarity) correlate most strongly with correctness. For example, SSI shows $r = 0.470$, Node Label Similarity $r = 0.447$ with answer quality ($p < 0.01$), indicating structural fidelity is critical for chaining dependent actions.
• Parallel tasks: Tool metrics (Tool F1, Precision, Recall) become leading predictors ($\sim$39% of answer variance explained), underscoring the necessity of correct tool orchestration for concurrent execution.
• Complexity: As task graph size and density increase, all metrics degrade, highlighting current agentic limitations with recursive decomposition and dense dependencies.

Metric	Correlation ($r$)	$p$-value
Structural Similarity Index	0.470	$<$0.001
Node Label Similarity	0.447	$<$0.01
Expected Task Complexity	-0.293	$<$0.05

Critical path optimization and dynamic graph adaptation are indispensable for maintaining responsiveness and throughput as complexity scales.

5. Technical Implementation Considerations

ARTI systems, by virtue of their modular, graph-centric design, demand:

• Sufficient computational resources for LLM orchestration, frequent embedding computation (e.g., FAISS similarity), and parallel tool invocation.
• Explicit memory management and context passing between agent modules, including logging, result consolidation, and deterministic execution order enforcement.
• Scalable evaluation pipelines capable of tracking detailed node, edge, and tool-level metrics (leveraging automatic trace comparison and semantic scoring).
• Extensibility to incorporate new tool domains, dynamic tool creation (e.g., agent-generated Python code), and real-time feedback from external systems.

Trade-offs arise between decomposition granularity (fewer, more monolithic nodes vs. over-fragmented graphs), parallelism (potential for racing conditions or redundant computation), and orchestration overhead, all of which must be balanced for deployment.

6. Implications, Limitations, and Future Directions

The ARTI paradigm advances agentic systems' adaptability and robustness, but also exposes new challenges:

• Balanced multidimensional metrics: Evaluation and optimization must jointly consider workflow fidelity (SSI), correct task identification (Node F1), and effective tool usage (Tool F1), calibrated to the task structure (sequential vs. parallel).
• Foundational benchmarking: AsyncHow’s dataset and metric suite offer a replicable, extensible foundation for standardized comparison—critical for reproducible ARTI progress.
• Scalability and generalization: Effective, scalable ARTI frameworks need recursive, context-aware planning, overhead minimization, advanced memory architectures, and robust error handling under environment drift.
• Modularity: Architectures supporting plug-and-play extension, domain transfer, and multi-agent scenarios will underpin robust generalization and adaptation capabilities.

Persistent limitations include performance degradation on high-complexity, dense, multi-hop DAGs, suboptimal recursive decomposition, and coordination overhead in large distributed agent settings. Ongoing work focuses on extending modularity, incorporating learning over evolving tool libraries, and addressing coordination in multi-agent and heterogeneous environments.

---

Agentic Reasoning and Tool Integration, as formalized in this framework, marks the transition toward both structurally rigorous and operationally extensible agentic AI—measured via novel graph-based metrics and supported by domain-agnostic datasets—establishing the technical prerequisites for reliable, adaptive, and scalable autonomy in real-world applications (Gabriel et al., 29 Oct 2024).