Agent-Based Diagnostic Frameworks

• Agent-Based Diagnostic Frameworks are computational architectures that deploy multiple specialized AI agents to collaboratively interpret data and generate diagnoses.
• They integrate domain knowledge graphs, neuro-symbolic reasoning, and iterative chain-of-thought validation to ensure interpretability and high accuracy in critical domains.
• Recent systems achieve up to 96.35% accuracy in non-destructive testing and improve diagnostic outcomes by dynamically orchestrating agent roles and evidence-based reviews.

Agent-Based Diagnostic Frameworks are computational architectures that organize diagnostic reasoning and decision-making tasks across teams of interacting AI agents, often orchestrated by a backbone model that invokes specialized tools, modules, or reasoning procedures. These frameworks have become central to high-stakes domains such as medical diagnostics, industrial inspection, infrastructure failure analysis, and complex system fault management. Recent advances focus on maximizing interpretability, reliability, and adaptability through multi-agent coordination, integration of domain knowledge graphs, neuro-symbolic reasoning, and interactive chain-of-thought validation. This article synthesizes the technical principles and representative systems of agent-based diagnostic frameworks, drawing on recent benchmarks and state-of-the-art methods in both clinical and industrial contexts.

1. Foundational Architectures and Agent Roles

Modern frameworks assign diagnostic sub-tasks to specialized agents that collaborate under the supervision of a central orchestrator or backbone model. For instance, InsightX Agent uses a Large Multimodal Model (LMM) to coordinate between a Sparse Deformable Multi-Scale Detector (SDMSD) and an Evidence-Grounded Reflection (EGR) tool for X-ray nondestructive testing (Liu et al., 20 Jul 2025). The orchestrator dynamically interprets input (such as an X-ray image), infers user intent, dispatches specialized detection models, collects proposals, and iteratively validates them via structured self-assessment. Tool invocation is guided by domain-adapted policies, with formal curricula (e.g., LoRA for weight adaptation and template-based response regularization) and explicit response structures.

Multi-agent decomposition is also central in medical imaging. PathFinder emulates expert pathologist workflows using four agents: Triage Agent (risk stratification), Navigation Agent (importance sampling across gigapixel slides), Description Agent (generates evidence-level findings), and Diagnosis Agent (late fusion over extracted findings) (Ghezloo et al., 13 Feb 2025). Each agent is optimized with a distinct objective—triage with binary cross-entropy, navigation with pixelwise loss, description with instruction-tuning, and diagnosis with multi-class cross-entropy—culminating in interpretable, natural-language reports.

More broadly, frameworks such as MEDDxAgent and Mapis represent diagnostic reasoning as iterative multi-agent processes, incorporating orchestrators, knowledge retrieval modules, simulation agents, and diagnosis strategy agents (Rose et al., 26 Feb 2025, He et al., 17 Dec 2025). These ensure explicit separation of history-taking, evidence synthesis, and strategy selection, operationalizing medical guidelines and integrating knowledge graphs for evidence-based rigor.

2. Tool Integration and Reasoning Protocols

Agentic diagnostic frameworks are defined as much by their integration of external tools and protocolized reasoning as by their model architectures. Essential components include object detectors, evidence reviews, retrieval mechanisms, and protocol-based workflows:

• Sparse Deformable Multi-Scale Detector (SDMSD): Generates dense region proposals, applies NMS with thresholding (e.g., $$NMS(B_{\rm dense}) = \{ b_i \in B_{\rm dense} | \forall b_j \in B_{\rm dense}, c_j > c_i \Rightarrow IoU(b_i,b_j) < \tau_{IoU} \}$$), and employs multi-scale deformable attention for efficient, high-recall detection (Liu et al., 20 Jul 2025).
• Chain-of-Thought Validation: Tools such as EGR execute multi-step evidence review encompassing context assessment, feature-level analysis, false positive filtering, recalibration (e.g., $$c_i^{\rm refined} = c_i \omega_E(d_i) \omega_C(d_i) \omega_X(d_i)$$), and structured reporting with JSON outputs.
• Knowledge-Graph-Grounded Reasoning: Mapis achieves guideline-compliant PCOS diagnosis by sequential agent evaluation over a tripartite knowledge graph, connecting clinical findings, laboratory markers, imaging, and exclusion criteria via graph-augmented retrieval (§ KG construction and schema) (He et al., 17 Dec 2025).
• Protocol Enforcement: In infrastructure diagnostics (e.g., telecom/datacenter), investigation protocols are formalized as finite-state machines with strict tool ordering, grounding outputs in evidence from MCP tool calls and enforcing faithfulness constraints ($x \in \bigcup_{t \leq T} \mathrm{Output}(T_i)$ for any mentioned entity).

3. Interactive, Iterative, and Explainable Decision-Making

Agent-based frameworks depart from static, sequential inference by embracing interactive, iterative, and auditorily transparent diagnostic processes. Key patterns include:

• Multi-Round Review Loops: InsightX and PathFinder use multi-stage agent collaboration, with explicit mapping between proposals, evidence strength ($\epsilon_i$), and confirmatory/rejection states, often paired with CoT rationales (Liu et al., 20 Jul 2025, Ghezloo et al., 13 Feb 2025).
• History-Simulator and Iterative Profile Updating: MEDDxAgent structures history-taking as simulated doctor-patient dialogue, extracting new symptoms at each iteration to refine the differential diagnosis profile and enable successive knowledge queries (Rose et al., 26 Feb 2025).
• Cross-Review and Peer Critique: OpsAgent and specialized frameworks (Catfish Agent (Wang et al., 27 May 2025)) facilitate peer review among expert agents, intentionally injecting dissent (complexity- and tone-calibrated) to disrupt silent agreement and surface overlooked rationales, leading to superior performance in challenging benchmarks.
• Neuro-Symbolic Guardrails: Modal logic (Kripke-model belief states, modal axioms $\Box \varphi$) safeguard hypothesis generation, ensuring logical and physical consistency of agent beliefs and pruning LM-proposed inferences that violate domain constraints (Sulc et al., 15 Sep 2025).

4. Performance Evaluation, Benchmarks, and Comparative Analysis

Agent-based diagnostic frameworks are substantiated by rigorous benchmark evaluations across domain datasets and against state-of-the-art baselines. Representative results include:

Framework	Domain/Dataset	Accuracy	F1-score	Interpretability Features
InsightX Agent	GDXray+ (X-ray NDT)	96.35%	96.35%	EGR chain-of-thought, natural-language evidence
PathFinder	M-Path (melanoma)	74%	—	Patch-level descriptions, slide-level explanations
Mapis	GED PCOS	91.76%	93.52%	JSON evidence chain, KG citations
MEDDxAgent	DDxPlus/Respiratory	+10–15 pp	—	Progress rate, explainability logs
OpsAgent	OPENRCA	+46% (Correct)	—	Auditable CoT, cross-agent review
DiagAgent	DiagBench	+15%	+27pt F1	Rubric-based procedural scoring

InsightX Agent achieves the highest object-detection F1-score compared to YOLOX, Faster R-CNN, DINO, and DeformDETR on GDXray+ (Liu et al., 20 Jul 2025). PathFinder surpasses the average dermatologist pathologist by 9% in melanoma diagnosis (Ghezloo et al., 13 Feb 2025). Mapis outperforms traditional ML, single-agent LLM, and multi-agent medical frameworks by 7–13 pp in PCOS diagnosis (He et al., 17 Dec 2025). MEDDxAgent shows 10–15 pp improvements over single-turn DDx baselines, especially when patient profiles are incomplete (Rose et al., 26 Feb 2025).

5. Generalization, Extensions, and Limitations

Agent-based diagnostic frameworks have demonstrated scalability, protocol adaptability, and strong interpretability in multiple domains:

• Domain Generalization: The agent-tool orchestration can generalize from X-ray NDT to other modalities (CT, ultrasound) by swapping perception modules and adapting knowledge bases (Liu et al., 20 Jul 2025).
• Guideline-Driven Extension: Mapis’ workflow can be transplanted to heart failure, diabetes, or other guideline-driven conditions by updating KG schemata (He et al., 17 Dec 2025).
• Infrastructure Diagnostics: LLM agents in telecom/datacenter environments leverage typed protocol tools (MCP) and strictly ordered steps to ensure reproducibility, safety, and grounding, without brittle hard-coded graph traversals (Tacheny, 12 Jan 2026).
• Limitations: Conservative rejection policies (e.g., EGR) may miss ambiguous or low-contrast defects, and novel/unseen fault types remain a challenge, indicating ongoing risks of hallucination outside domain knowledge (Liu et al., 20 Jul 2025). Experience-accumulation (OpsAgent) and reflection controllers create sustainable adaptation loops, but latency and API costs for multi-agent synthesis remain open issues (Luo et al., 28 Oct 2025).

6. Interpretability, Trustworthiness, and Bias-Mitigation

A central advantage is the ability to generate explicit, stepwise rationales that link predictions to observed evidence:

• Confirmed defects or diagnoses are accompanied by transparent chain-of-thought traces, support metrics (e.g., anomaly score, bounding box fit), and contextual justifications (Liu et al., 20 Jul 2025).
• Multi-agent dialogue systems have been used to surface and mitigate cognitive biases in clinical reasoning, with observed increases from 0% initial diagnosis accuracy to >70% after peer review and structured challenge (Ke et al., 2024).
• Knowledge-graph grounding, JSON outputs, and explicit citation of guideline sections ensure auditability and minimize hallucination (He et al., 17 Dec 2025).

7. Future Directions

Emergent research avenues include adaptive thresholding in reflection tools, multi-modal diagnostic ensembles, dynamic agent routing and protocol selection, reinforcement learning for agent optimization, and human-in-the-loop calibration for trust and coverage. The modular agentic patterns support extensibility to new domains and continual integration of expert knowledge and operational data. The deployment of protocol-gated tool invocation and neuro-symbolic guardrails will be essential for robust, verifiable diagnostics in both industrial and clinical environments.

References: (Liu et al., 20 Jul 2025, Ghezloo et al., 13 Feb 2025, He et al., 17 Dec 2025, Sulc et al., 15 Sep 2025, Rose et al., 26 Feb 2025, Luo et al., 28 Oct 2025, Tacheny, 12 Jan 2026, Ke et al., 2024, Wang et al., 27 May 2025, Marandi et al., 27 May 2025).