CLAIRE Agentic System

• CLAIRE Agentic System is a framework that decomposes complex tasks into directed acyclic graphs, enabling effective orchestration of AI agents and tools.
• It employs novel structural and operational metrics—Node F1, SSI, and Tool F1—to evaluate and enhance performance in both sequential and parallel task execution.
• The system supports real-time adaptation, robust monitoring, and scalability for high-stakes applications through asynchronous workflows and dynamic tool orchestration.

The CLAIRE Agentic System refers to a family of architectures and methodologies for AI agents that autonomously decompose tasks, select and orchestrate tools, optimize execution through dynamic and asynchronous workflows, and are evaluated using novel operational and structural metrics. These agentic systems predominantly leverage LLMs for high-level reasoning and natural-language interface, with a rigorous focus on robustness, scalability, and transparency in complex, multi-step environments.

1. Advanced Agentic Framework and Pipeline Components

The core design of the CLAIRE system centers on dynamic decomposition of multi-hop queries into a directed acyclic graph (DAG) of tasks. The principal pipeline modules include:

• Orchestrator: Receives a user query and, using an LLM, generates a task graph capturing the atomic steps and their dependencies. This supports both coarse- and fine-grained decomposition and allows for optimization, e.g., critical path minimization to reduce end-to-end execution latency.
• Delegator: Manages task assignment to agents or external tools, passing outputs via inter-task buffers to maintain dataflow across graph dependencies.
• Agents and Tools: Agents execute tasks leveraging LLM reasoning, while deterministic Python functions (Tools), filtered via task-aware semantic matching, execute specialized functionality. Only relevant tools are surfaced to minimize system overhead.
• Executor: Orchestrates execution of the DAG, enabling parallel execution of independent tasks to boost throughput and sequencing of dependent steps to ensure correct data propagation.

This framework supports asynchronous task graph processing, real-time adaptation to changing tool availability, and modular integration of new agent modalities or reasoning modules. Both sequential and parallel strategies are accommodated to handle heterogeneity in task complexity.

A representative workflow is as follows:

1. User query → Orchestrator → task graph (DAG).
2. DAG tasks → Delegator → agent/tool selection.
3. Agents/tools process tasks; outputs passed via memory buffers.
4. Executor runs independent nodes in parallel; dependent nodes sequentially.
5. Final output aggregated and surfaced to the user.

LaTeX-formulated metrics for tool use are central to operational rigor: $$\mathrm{Precision}_{\text{tool}} = \frac{TP_{\text{tool}}}{TP_{\text{tool}} + FP_{\text{tool}}}, \quad \mathrm{Recall}_{\text{tool}} = \frac{TP_{\text{tool}}}{TP_{\text{tool}} + FN_{\text{tool}}}, \quad F1_{\text{tool}} = \frac{2 \cdot \mathrm{Precision} \cdot \mathrm{Recall}}{\mathrm{Precision} + \mathrm{Recall}}$$

2. Structural and Operational Evaluation Metrics

CLAIRE introduces three novel metrics for robust evaluation:

• Node F1 Score: Measures fidelity in task node identification versus gold-standard graphs, balancing precision and recall for node detection.
• Structural Similarity Index (SSI): Aggregates node label similarity (typically via cosine similarity) and edge F1 measures to assess not just presence of correct tasks, but preservation of designed interdependencies. SSI is calculated as: $$\mathrm{SSI} = \frac{\mathrm{Node~Label~Similarity} + \mathrm{Edge~F1~Score}}{2}$$
• Tool F1 Score: Applies an F1-like formulation to the set of selected tools versus the expected tool set, focusing on operational alignment.

Empirical analysis demonstrates that SSI is dominantly predictive for answer quality in sequential tasks, while Tool F1 Score is essential for parallel tasks. R² values in regression analyses show that SSI and Tool F1 together explain a substantial portion of answer score variance.

3. Dataset Design for Evaluation

The CLAIRE system’s evaluation leverages a specialized AsyncHow-based dataset containing:

• 50 randomly sampled task graphs covering both sequential and parallel structures.
• Rich annotations: scenario names, gold-standard DAGs, synthetic tool APIs, gold tool call sequences, and final responses.
• Realistic tool behaviors with API-style interfaces (e.g., JSON returns) for deterministic evaluation of agent decisions.

This dataset enables comprehensive analysis of agent adaptation, decomposition fidelity, and operational tool use across diverse task complexities.

4. Empirical Results and System Insights

Extensive experiments highlight key findings:

• Asynchronous decomposition substantially improves responsiveness and scalability, attributable to parallel execution of independent nodes.
• Real-time adaptation: The framework aptly replaces unavailable tools and dynamically schedules tasks, demonstrating robust adaptation.
• Task type dependence: In sequential workflows, structural metrics (SSI, Node F1) outperform operational ones for predicting output quality; the reverse holds for parallel workflows, where tool-related metrics prevail.
• Regression analysis confirms that SSI is a significant predictor for sequential workflows ($R^2 \sim$ 0.36–0.39), and Tool F1 for parallel ones.

This establishes the necessity for balanced evaluation protocols—structural for chain-dependent tasks, operational for parallel, tool-driven tasks.

5. Monitoring, Safety, and Security Dimensions

Advanced CLAIRE deployments benefit from adaptive monitoring algorithms (e.g., AMDM from (Shukla, 28 Aug 2025)). These systems:

• Normalize heterogeneous metrics (using rolling z-scores and EWMAs).
• Apply per-axis dynamic thresholds.
• Perform joint anomaly detection using Mahalanobis distances across axes (capability, safety, robustness, human-centric, and economic).

Empirically, AMDM reduces detection latency and false positive rates for goal drift and safety violations, supporting real-world, high-stakes deployment with robust early warning capabilities.

Security methodologies (Barua et al., 23 Feb 2025) include:

• Reverse Turing Tests for rogue agent detection.
• Multi-agent simulations for deceptive alignment analysis.
• Multi-turn, tool-mediated defenses against many-shot jailbreaks, attaining up to 94% detection accuracy (GEMINI 1.5 pro), but with vulnerabilities increasing under prolonged adversarial prompt exposure.

Best practice signals the need for flexible, multi-layered, and actively monitored protection strategies.

6. Agentic Identity and Alignment

Agentic identity in LLM-based agents is critical for reliability (Perrier et al., 23 Jul 2025): characterized by identifiability, continuity, consistency, persistence, and recovery metrics, which measure ontological stability under stochasticity and perturbation. The CLAIRE system benefits from memory scaffolds and corrective prompt regimes to anchor agent identity, ensuring sustained reasoning and action congruence across sessions.

Probabilistic modeling frameworks (Lee et al., 8 Sep 2025) elaborate on subagent aggregation, emphasizing weighted logarithmic pooling for coherent meta-agent behavior, cloning invariance for recursive composition, and tilt analysis for understanding persona deviations. Alignment strategies, such as manifest-then-suppress protocols for antagonistic subagents, provide theoretical guarantees for misalignment reduction.

7. Applications, Impact, and Future Directions

CLAIRE is applied in domains requiring complex, multi-step decision-making, dynamic adaptation, and tool orchestration. Key application areas include:

• Strategic planning with human-like context variability (Trencsenyi et al., 14 May 2025).
• Knowledge curation and inconsistency detection in large corpora (e.g., Wikipedia, with AUROC up to 75.1% and significant editor confidence improvements (Semnani et al., 27 Sep 2025)).
• Transparent, interpretable decision-making in high-stakes contexts (Agentic Classification Trees with quantitative and semantic-split optimization (Grari et al., 30 Sep 2025)).

Advanced frameworks, such as constitutionally-aligned superego agents (Watson et al., 8 Jun 2025), facilitate secure, contextually compliant AI behavior via modular, dialable rule sets.

Research focuses on expanding the typological characterization of agentic systems (Wissuchek et al., 7 Jul 2025), integrating multi-dimensional monitoring and control, and advancing neuroscientifically-inspired cognitive modules for generalization and robustness (Liu et al., 7 May 2025).

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