# Architecture of the Multi-Agent Veterinary Platform

The AgenticPet platform operates as a cohesive ecosystem of specialized AI agents, orchestrated to perform complex clinical diagnostic workflows with high reliability and contextual awareness.

<figure><img src="https://agentic-pet.gitbook.io/agentic/~gitbook/image?url=https%3A%2F%2F2093794410-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FxPMgKUrafT7SX6X0QY4A%252Fuploads%252Fu2Y6jKOqEF2l0Gm6kyu2%252Fhome_screen_b2b.png%3Falt%3Dmedia%26token%3De2182dab-7c06-4195-b248-a4821e5b758d&#x26;width=768&#x26;dpr=4&#x26;quality=100&#x26;sign=fa5e1b48&#x26;sv=2" alt=""><figcaption></figcaption></figure>

### The Agentic Ecosystem and Role Specialization <a href="#the-agentic-ecosystem-and-role-specialization" id="the-agentic-ecosystem-and-role-specialization"></a>

An enterprise-grade AI agent is fundamentally defined as a software entity endowed with the capacity for planning, reasoning, and autonomous action execution. Each agent in the AgenticPet ecosystem is defined by a unique configuration that enables specialized reasoning and operational focus. Their internal mechanisms guide task execution and facilitate interaction with external resources, supporting informed and context-aware decision processes.

The architectural efficiency is derived from a modular agent-based framework that disaggregates cognitive functions across specialized roles. Central coordination mechanisms manage task orchestration and knowledge synthesis, while distributed specialist agents operate within their respective domains, each equipped with domain-specific reasoning capabilities. This separation of concerns enables scalable expertise distribution and optimized decision pathways. The framework maintains semantic consistency through unified knowledge management and context preservation across agent interactions. By decoupling expert reasoning from coordination logic, the system achieves higher efficiency in resource allocation and maintains coherence in complex multi-faceted problem-solving scenarios.

### Multi-Agent Coordination via Unified Context Management <a href="#multi-agent-coordination-via-unified-context-management" id="multi-agent-coordination-via-unified-context-management"></a>

Effective collaboration among specialized agents hinges upon standardized context sharing and orchestration mechanisms. The system achieves this through a unified coordination framework that facilitates seamless information exchange and task delegation.

#### **Context-Driven Collaboration Architecture** <a href="#context-driven-collaboration-architecture" id="context-driven-collaboration-architecture"></a>

The coordination model operates on a context-centric paradigm where centralized state management serves as the common reference point for distributed agents. This shared informational substrate enables agents to maintain semantic consistency and operational awareness across concurrent processing tasks.

The platform employs a layered communication strategy optimized for both responsiveness and efficiency:

* **Event-Driven Coordination:** Agents operate reactively within a task delegation framework, where completion signals from specialized agents trigger downstream processing stages. For instance, upon completing domain-specific analysis, agents publish their findings to the coordination layer, which then orchestrates subsequent analytical phases or synthesis operations.
* **Context Query and Retrieval:** Agents retain the ability to actively retrieve contextual information from the shared state repository, providing flexibility for asynchronous dependency resolution and multi-stage reasoning chains.

The architectural decision to maintain unified context management for operational data is critical. Multi-agent systems inherently face scalability challenges related to context coherence and coordination efficiency. The implementation mandates that all transient operational data maintains consistent accessibility and observability patterns, while maintaining clear separation between stateless coordination logic and persistent data storage layers. This separation ensures optimal performance characteristics and enables granular monitoring of data lifecycle management across the system.

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#### Orchestration Logic and Dynamic Workflow <a href="#orchestration-logic-and-dynamic-workflow" id="orchestration-logic-and-dynamic-workflow"></a>

The Orchestrator is the overseeing process that manages the entire lifecycle of a diagnostic query, ensuring efficiency and control. Its primary functions include managing task complexity by decomposing complex requests into smaller, manageable subtasks, coordinating agents by assigning subtasks to the most specialized entity, and handling dependencies by ensuring seamless data flow between sequential tasks.

AgenticPet implements an adaptive workflow system to handle the inherent non-linearity of clinical diagnosis. This system enables dynamic selection of agents based on real-time task requirements and contextual decision-making. If, for example, initial blood analysis yields an ambiguous finding, the execution path can be altered at runtime to initiate a supplemental imaging analysis request, demonstrating the platform's capacity for adaptive problem-solving.

#### Conflict Resolution via Multi-Agent Arbitration and LLM Adjudication <a href="#conflict-resolution-via-multi-agent-arbitration-and-llm-adjudication" id="conflict-resolution-via-multi-agent-arbitration-and-llm-adjudication"></a>

In a multi-agent system where specialized entities navigate shared data and resources (diagnostic observations), conflict resolution is a fundamental requirement. Discrepancies can arise, particularly when agents relying on different models or modalities (e.g., a Hematology Agent and a Clinical History Agent) produce contradictory intermediate results.

AgenticPet implements a sophisticated conflict resolution protocol leveraging large language models as neutral arbiters. When specialized agents produce divergent diagnostic conclusions, the system does not force consensus through simple averaging or voting. Instead, the conflicting findings are escalated to a specialized arbitration layer where a coordinator LLM examines the evidence, reasoning chains, and supporting data from each agent's perspective. This LLM-based judge evaluates the quality of evidence, consistency with clinical patterns, and logical coherence of each agent's conclusion, producing a reasoned synthesis that acknowledges valid insights from multiple perspectives while identifying which evidence stream is most reliable given the clinical context.

This approach preserves diagnostic nuance: rather than eliminating disagreement through consensus, the arbitration process captures the uncertainty signal itself, which becomes critical for downstream clinical decision-making. The LLM-as-judge mechanism documents its reasoning throughout the arbitration process, creating transparent, auditable records of how conflicts were resolved and which evidence ultimately informed the synthesized recommendation. This transparency is essential for clinical accountability and enables human reviewers to understand the basis for the system's reasoning.

Critically, this conflict resolution mechanism is not autonomous. When the arbitration process identifies irresolvable tensions between agent findings, significant evidence gaps, or scenarios where the uncertainty signals suggest clinical ambiguity, the system mandates escalation to human expert review. Rather than forcing a potentially unsafe autonomous conclusion, the platform routes complex, ambiguous cases to veterinary specialists, ensuring that patient safety remains paramount and that human expertise guides final diagnostic and therapeutic decisions.
