Agentic AI

Agentic AI is the computational backbone of AutopathX’s translational engine. By orchestrating multi-agent reasoning, multi-modal spatial omics, and therapeutic simulation across the tumor microenvironment (TME), our AI system moves beyond simple analysis — it thinks, tests, and iterates like a scientist. AutopathX Agentic AI dynamically models the cross-talk between key stromal and immune players, uncovering actionable patterns for precision immunotherapy.

From dendritic cells (DC) to macrophages, TAMs, CAFs, and TILs, our intelligent agents map and simulate immune dynamics across space, time, and treatment regimens. Powered by RAG-based systems, GPU compute, and expansive spatial datasets, Agentic AI doesn’t just follow — it leads translational discovery.

CRC TME-Guided Multi-Agent Framework: AIRE for Stratifying CAR T, TIL Synergies and Immunotherapy Resistance & Combinations

The effort showcases the conceptual foundation of the Agentic Immunotherapy Recommender Engine (AIRE)—a multi-agent framework designed to decode the colorectal cancer tumor microenvironment (CRC TME) and personalize therapy design. AIRE integrates spatial transcriptomics with domain-specific agents representing cellular subtypes such as dendritic cells (DCs), tumor-associated macrophages (TAMs), and cancer-associated fibroblasts (CAFs). Through collaborative reasoning among critic, synthesis, and manager agents, it analyzes key immune signaling motifs (CXCL10–CXCR3, CCL13–CCR2, ACTA2/TNC) and maps immune zonation defined by PD-L1 and IDO1 expression. A federated scoring rubric quantifies node-level relevance—including CXCR7 for DCs, ACTA2 for CAFs, and CCL13 for TAMs—by assessing spatial context and immune interaction strength, ultimately recommending actionable therapeutic strategies.

This agent-based federated graph approach transforms how immunotherapy candidates are prioritized, offering a scalable mechanism for precision decision-making across decentralized biomedical datasets. It enables dynamic integration of single-cell profiles, spatial histology, and clinical endpoints without compromising data ownership or interoperability. By harnessing the collective intelligence of specialized agents, researchers can simulate treatment synergies, identify resistance mechanisms, and stratify patients based on immune architecture—all essential for designing combination therapies that bridge CAR T, DC vaccines, TILs, and checkpoint blockade strategies. This framework marks a pivotal advancement in AI-guided immuno-oncology, paving the way for adaptive, context-aware precision interventions 

Adaptive Multi-Agent Architectures for Spatial Omics-Driven Immunotherapy in Colorectal Cancer Tumor Microenvironments

The framework presents an Adaptive Multi-Agent AI system engineered to transcend the inherent limitations of conventional multi-agent approaches, particularly in decoding the complexity of colorectal cancer (CRC) tumor microenvironments (TMEs). Unlike traditional agents that rely on rigid plan-execute cycles, AIME enables continuous strategy adjustment, domain-aware task decomposition, and real-time instantiation of specialized analytical agents. This dynamic adaptability is essential when navigating the heterogeneous, inflammatory landscapes of CRC TMEs, where evolving cell–cell interactions and spatial gradients present shifting diagnostic and therapeutic challenges.

The architecture integrates multimodal spatial omics platforms—Xenium spatial transcriptomics and CODEX spatial proteomics—through four sequential modules. The first module, Agent Specialization, aligns relevant spatial features (genes, proteins, tissue structures) to immunotherapeutic targets. Next, Dynamic Planning operationalizes therapeutic goals into context-aware subtasks such as niche classification and immune cell profiling. Actor Instantiation crafts tailored agents equipped with subtask-specific prompts and analytic tooling. Finally, Therapeutic Reasoning orchestrates collaborative inference among agents to generate transparent, traceable outputs—mapping immune niches, predicting treatment response, and identifying emergent spatial biomarkers across CRC samples.

This adaptive framework offers significant promise for translational oncology, providing a modular and scalable system capable of navigating the biological complexity inherent to immunotherapeutic decision-making. Its fluid architecture supports iterative reasoning over evolving spatial data, enabling high-resolution biomarker discovery and therapeutic prioritization. As immunotherapy strategies continue to diversify, particularly in the context of spatially resolved inflammation and cellular crosstalk, AIME stands as a powerful computational ally—driving precision diagnostics and augmenting therapeutic design within CRC TMEs.

Orchestrating CRC TME Intelligence: Multi-Agent RAG Meets Federated Knowledge Graphs for Immunotherapy Precision

The effort Illustrated here a forward-thinking architecture in which Multi-Agent RAG systems replace traditional Retrieval-Augmented Generation workflows to tackle the complexity of spatial omics-driven CRC TME analysis. Unlike conventional RAG, which relies on a single LLM for retrieval and response synthesis, Multi-Agent RAG distributes tasks among specialized agents—enhancing interpretability, modularity, and precision. This framework enables agents to retrieve and reason across multi-modal spatial data, such as CODEX imaging, single-cell maps, and immunogenomic signatures. A Federated Knowledge Graph (FKG) built collaboratively by these agents allows integration without compromising patient privacy, harmonizing ontologies like GO, CL, and UMLS to reflect dynamic relationships between genes, cell types, and spatial niches.

Within this orchestration, various agents have domain-specific roles. The Manager Agent delegates tasks like TAM–CAF niche identification or checkpoint overlay. The Planner Agent breaks down objectives into retrieval, synthesis, and validation stages. Specialized Retriever Agents source data from Visium HD, TIMEDB, Human Cell Atlas, and TISCH (Tumor Immune Single-cell Hub), each bringing contextual evidence for signaling motifs or cell–gene proximities. The Tool Creation Agent integrates spatial analysis pipelines like Squidpy and Seurat, while Tool Agents execute them for cell–type deconvolution or cytokine pattern extraction. A Synthesizer Agent builds reasoning chains (e.g. CXCL10 → IDO1 → PD-L1), and a Critic Agent ensures biological plausibility by mapping terms to ontologies like GO:0006955 and CL:0000905, flagging inconsistencies or hallucinations before KG updates.

The figure contrasts system prompts with context engineering—highlighting adaptive context yields superior results in multi-agent reasoning. System prompts are rigid, predefined, and ill-suited for dynamic TME inference. In contrast, context engineering introduces clear, modular task flows suited to agent orchestration. Prompt example (shown) initiates analysis of DC–TAM zones, identifying CXCL10–IDO1 signaling and overlaying LZTS2–PD-L1 checkpoints. Another summarizes how DCs and macrophages shape the immune landscape and immunotherapy targets within CRC TME. These context-aware prompts yield biologically coherent outputs that reflect spatial co-localization, molecular cross-talk, and agent consensus—enhancing reproducibility and interpretability.

Ultimately, this multi-agent, immuno-trained workflow unlocks new dimensions in TME diagnostics. Agents orchestrate across spatial omics layers, integrating cell–cell proximity with signaling axes. The Federated Knowledge Graph architecture captures nuanced immunosuppressive or inflamed zones, built from distributed contributions that respect semantic diversity and data privacy. This enables deeper understanding of the dendritic cell–macrophage interplay, TAM–CAF co-localization, and their impact on T cell exhaustion and therapeutic resistance. Biomarker discovery, patient stratification, and immunotherapy target selection become more robust—reflecting the complexity of cytokine signaling, cell plasticity, and therapeutic modulation across CRC microenvironments.

Immune-Centric Agentic Orchestration for Adaptive Colorectal Cancer Diagnostics

The figure articulates a sophisticated, AI-enabled framework for agentic diagnostics in colorectal cancer (CRC) immunotherapy, integrating spatial transcriptomic profiling with multimodal orchestration of specialized diagnostic agents. Built atop platforms like CODEX and Xenium, the architecture features a federated agentic reasoning core that harmonizes real-time decision-making across pathology, genomics, clinical trial coordination, tumor microenvironment (TME) stratification, and dendritic cell (DC) surveillance. Each agent operates within a modular, human-in-the-loop interface optimized for regulatory deployment, clinical transparency, and diagnostic agility.

Initiating the workflow, the pathology agent undertakes high-resolution spatial segmentation of CRC tissue, extracting regions of interest (ROIs) by identifying lymphocyte-enriched niches through quantitative immune density mapping and morphometric stratification. These annotated ROIs serve as foundational inputs for downstream reasoning agents, informing therapy eligibility, immune phenotyping, and trial readiness. Through real-time tissue parsing, the pathology agent ensures deep visibility into immune infiltration patterns that inform both prognostic stratification and therapeutic targeting.

The DC agent builds on this substrate by deconvoluting heterogeneous dendritic cell populations within the CRC TME. It classifies DC subtypes—cDC1, cDC2, pDC, migratory, and LAMP3+ variants—using marker-driven overlays and proteomic features, reconstructing their functional topology across antigen presentation, cytokine trafficking, and chemokine signaling. Spatial mapping of these subtypes enables the modeling of DC–TAM cross-talk networks, where mutual signaling cascades influence immunotherapy outcomes and TME remodeling. The agent’s extract further highlights DC subtype localization, tumor-type specificity, and role adaptation under inflammatory or immunosuppressive pressure.

Agentic coordination expands in the adaptive cell therapy layer, where orchestrated integration of signaling data is executed across CAR-T, CAR-NK, NK cells, TILs, and DCs. Multi-agent processing pipelines ingest spatial and single-cell omics, reconstructing immune pathway hierarchies and mapping co-expression networks relevant to cell therapy design. Specialized agents invoke logic frameworks to stratify therapy zones and extract actionable markers, adapting to tissue heterogeneity, immune activation thresholds, and cellular proximity scores. Proprietary tools facilitate seamless parsing of spatial proteomes and transcriptomes, transforming heterogeneous molecular inputs into therapy-eligible diagnostic outputs.

This unified agentic architecture represents a translational leap in CRC immunotherapy by facilitating precision diagnostics that are adaptive, scalable, and biologically contextualized. Its capacity to parse multimodal spatial data, coordinate reasoning across agents, and generate interpretable outputs aligns with clinical imperatives around personalized medicine, trial optimization, and biomarker-driven treatment selection. By embedding explainable AI principles and modular task allocation, the system advances the diagnostic interface from static annotation to dynamic reasoning, offering a deployable blueprint for next-generation oncologic decision support.