Agentic AI System Architecture

An Agentic AI System Architecture is an AI architecture model that organizes agentic system components into specialized software architecture layers (to enable agent-based capabilitys through layered organization).

• AKA: Agent Architecture Pattern, Agent System Architecture, Intelligent Agent Framework, Cognitive Agent Architecture, Autonomous System Architecture Model.
• Context:
  • It can typically be instantiated in Agentic System Architectures.
  • It can typically organize Agent Components through layer hierarchys.
  • It can typically manage Agent Lifecycle through development layers.
  • It can typically handle Agent Logic through reasoning layers.
  • It can typically support Agent Interaction through communication layers.
  • It can typically implement Inter-layer Communication Patterns through standardized interfaces, message passing protocols, and component coordination mechanisms.
  • It can typically manage Agent Data Flow through input processing layers, information transformation layers, and output generation layers.
  • It can typically define Agent Behavior Model through goal representation, action selection mechanisms, and outcome evaluation processes.
  • It can typically support Cognitive Process through perception mechanisms, belief formation, and decision-making components.
  • It can typically enable Autonomous Operation through self-regulation systems, environmental adaptation, and independent goal pursuit.
  • It can typically implement Agent Execution Model through action sequence planning, resource allocation, and task prioritization.
  • It can typically maintain Architectural Coherence through design principle adherence, pattern consistency, and architectural integrity checks.
  • ...
  • It can often implement Agent Learning through adaptation layers.
  • It can often provide Tool Integration through interface layers.
  • It can often manage Agent Deployment through serving layers.
  • It can often include Agent Governance Layers for action validation, constraint enforcement, and safety boundary maintenance.
  • It can often implement Performance Monitoring Layers for agent behavior assessment, effectiveness measurement, and quality assurance.
  • It can often support Architecture Evolution through component upgradeability, dynamic reconfiguration, and incremental enhancement.
  • It can often enable Agent Interoperability through standardized message formats, common protocol adherence, and cross-architecture integration patterns.
  • It can often incorporate Collaborative Capability through multi-agent coordination protocols, role-based interaction models, and collective decision mechanisms.
  • It can often implement Explainability Layers for reasoning transparency, decision justification, and action traceability.
  • It can often support Personalization Mechanisms through user preference modeling, interaction history analysis, and adaptive behavior adjustment.
  • It can often handle Conflict Resolution through priority-based arbitration, negotiation protocols, and compromise strategys.
  • It can often provide Error Handling Framework for failure detection, recovery mechanisms, and graceful degradation.
  • It can often incorporate Domain Adaptation Layers for context-specific knowledge application, domain constraint adherence, and specialized capability activation.
  • ...
  • It can range from being a Simple Agentic Architecture to being a Complex Agentic Architecture, depending on its agent capability.
  • It can range from being a Specialized Agent Architecture to being a General Agent Architecture, depending on its agent scope.
  • It can range from being a Monolithic Agent Architecture to being a Distributed Agent Architecture, depending on its deployment topology.
  • It can range from being a Reactive Agent Architecture to being a Deliberative Agent Architecture, depending on its decision-making approach.
  • It can range from being a Standalone Agent Architecture to being a Collaborative Agent Architecture, depending on its interaction dependency.
  • It can range from being a Deterministic Agent Architecture to being a Probabilistic Agent Architecture, depending on its uncertainty handling.
  • It can range from being a Static Agent Architecture to being an Adaptive Agent Architecture, depending on its learning capability.
  • It can range from being a Human-dependent Agent Architecture to being an Autonomous Agent Architecture, depending on its supervision requirement.
  • ...
  • It can integrate with Model Architecture for AI models.
  • It can connect to Tool Architecture for external tools.
  • It can utilize Knowledge Architecture for information access.
  • It can implement Security Architecture for protected operation.
  • It can incorporate User Experience Architecture for human interaction.
  • It can leverage Data Architecture for information management.
  • It can align with Enterprise Architecture for organizational integration.
  • It can interface with IoT Architecture for physical world interaction.
  • It can coordinate with Service Mesh Architecture for distributed service communication.
  • ...
• Examples:
  • Agentic System Core Layers, such as:
    • Agentic System Planning Layers for strategy-based agents.
    • Agentic System Reasoning Layers for decision-based agents.
    • Agentic System Learning Layers for adaptive agents.
    • Agentic System Perception Layers for environment-sensing agents.
    • Agentic System Knowledge Layers for information-aware agents.
    • Agentic System Execution Layers for action-performing agents.
    • Agentic System Goal Management Layers for objective-oriented agents.
    • Agentic System Belief Management Layers for state-representation agents.
    • Agentic System Meta-reasoning Layers for self-reflective agents.
    • Agentic System Attention Management Layers for focus-control agents.
  • Agentic System Interaction Layers, such as:
    • Agentic System Tool Integration Layers for tool-using agents.
    • Agentic System API Connection Layers for service-based agents.
    • Agentic System Environment Interface Layers for environment-aware agents.
    • Agentic System Human Interaction Layers for human-collaborative agents.
    • Agentic System Agent Communication Layers for multi-agent cooperation.
    • Agentic System Sensory Processing Layers for multimodal-input agents.
    • Agentic System Output Generation Layers for multimodal-response agents.
    • Agentic System Social Protocol Layers for socially-aware agents.
    • Agentic System Negotiation Layers for compromise-capable agents.
    • Agentic System Feedback Processing Layers for critique-responsive agents.
  • Agentic System Memory Layers, such as:
    • Agentic System Context Management Layers for context-aware agents.
    • Agentic System Knowledge Base Layers for knowledge-driven agents.
    • Agentic System State Tracking Layers for stateful agents.
    • Agentic System Experience Storage Layers for learning-enabled agents.
    • Agentic System Episodic Memory Layers for history-aware agents.
    • Agentic System Working Memory Layers for active-computation agents.
    • Agentic System Semantic Memory Layers for conceptual-knowledge agents.
    • Agentic System Procedural Memory Layers for skill-retention agents.
    • Agentic System Memory Consolidation Layers for knowledge-integrating agents.
    • Agentic System Memory Retrieval Layers for recall-optimized agents.
  • Agentic System Safety Layers, such as:
    • Agentic System Action Validation Layers for safe-operation agents.
    • Agentic System Ethical Reasoning Layers for value-aligned agents.
    • Agentic System Monitoring Layers for self-regulatory agents.
    • Agentic System Explanation Layers for transparent agents.
    • Agentic System Boundary Enforcement Layers for constraint-adherent agents.
    • Agentic System Risk Assessment Layers for hazard-aware agents.
    • Agentic System Human Oversight Layers for supervision-ready agents.
    • Agentic System Anomaly Detection Layers for error-detecting agents.
    • Agentic System Fail-safe Mechanism Layers for graceful-degradation agents.
    • Agentic System Regulatory Compliance Layers for legally-compliant agents.
  • Agentic System Implementation Patterns, such as:
    • Microservice-based Agent Architectures for distributed agent deployment.
    • Event-driven Agent Architectures for reactive agent behavior.
    • Hierarchical Agent Architectures for multi-level decision making.
    • Pipeline-based Agent Architectures for sequential processing.
    • Modular Agent Architectures for component-based construction.
    • Actor Model Agent Architectures for message-passing concurrency.
    • Blackboard Agent Architectures for shared-memory collaboration.
    • Subsumption Agent Architectures for layered-behavior control.
    • BDI Agent Architectures for belief-desire-intention reasoning.
    • Contract Net Agent Architectures for task-allocation negotiation.
    • MAPE-K Agent Architectures for autonomic computing.
    • Hybrid Reactive-Deliberative Architectures for balanced decision making.
  • Modern Agent Architecture Paradigms, such as:
    • LLM-based Agent Architectures for language-powered reasoning.
    • Multi-agent Coordination Architectures for collaborative problem solving.
    • Hybrid Symbolic-Neural Architectures for combined reasoning approaches.
    • Self-improving Agent Architectures for capability enhancement.
    • Continual Learning Architectures for knowledge accumulation.
    • Transformer-based Agent Architectures for attention-mechanism reasoning.
    • Federated Agent Architectures for privacy-preserving collaboration.
    • Embodied Agent Architectures for physical-world interaction.
    • Neuro-symbolic Agent Architectures for integrated reasoning approaches.
    • Swarm Intelligence Architectures for emergent collective behavior.
    • Digital Twin Agent Architectures for physical system representation.
    • Cognitive Architectures for human-inspired information processing.
  • Domain-Specific Agent Architecture Models, such as:
    • Conversational Agent Architecture Models for dialogue-based interaction.
    • Autonomous Vehicle Agent Architecture Models for transportation system.
    • Financial Trading Agent Architecture Models for market operation.
    • Healthcare Agent Architecture Models for patient care support.
    • Industrial Control Agent Architecture Models for manufacturing process.
    • Smart Home Agent Architecture Models for residential automation.
    • Enterprise Assistant Agent Architecture Models for business process support.
    • Scientific Discovery Agent Architecture Models for research acceleration.
    • Educational Agent Architecture Models for learning facilitation.
    • Creative Production Agent Architecture Models for artistic generation.
    • Game Agent Architecture Models for interactive entertainment.
    • Security Operation Agent Architecture Models for threat detection.
  • ...
• Counter-Examples:
  • Traditional Software Architecture Model, which lacks agent-specific capabilitys.
  • AI Model Architecture Model, which focuses on model execution rather than agent behavior.
  • Service Architecture Model, which handles request processing rather than autonomous action.
  • Data Pipeline Architecture, which manages information flow without autonomous decision-making.
  • Static Application Architecture, which lacks adaptive reasoning capability.
  • Cloud Infrastructure Architecture, which addresses resource provision rather than cognitive processing.
  • Database Architecture, which organizes data storage without intentional behavior.
  • UI/UX Architecture, which structures user interface without autonomous agency.
  • Serverless Architecture, which manages function execution rather than continuous operation.
  • Microkernel Architecture, which minimizes kernel services without goal-directed behavior.
  • Event-Driven Architecture, which reacts to event occurrence without intentional planning.
  • Batch Processing Architecture, which handles sequential jobs without interactive adaptation.
• See: AI Agent Pattern, Agent System Design, AI Layer Model, Agent Development Lifecycle, Intelligent System Architecture, Multi-agent System Framework, Cognitive Architecture Model, Autonomous System Design Pattern, BDI Agent Model, Reactive Agent Framework, Deliberative Agent System, Agent Communication Protocol, Agent Learning System, Agent Governance Framework, Agent Safety Design, Agent Perception System, Agent Action Selection Mechanism.

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References

2025-04-30

• Perplexity.ai
  • Question: What is a Conversational RAG Based Agent Architecture for VSaaS Products? Explain the Layered Agent Architecture Approach to Intelligent Surveillance.
  • Answer: The integration of RAG into VSaaS platforms marks a paradigm shift in how security systems process and interact with Visual Data. By combining Real Time Video Analytics with conversational interfaces, this Agent Architecture enables dynamic querying of Surveillance Footage, contextual understanding of security events, and Proactive Threat Mitigation.
    • Core Components of VSaaS Enhanced by RAG:
      • Video Data Ingestion and Processing Layer:
        • Modern VSaaS platforms rely on distributed camera networks that generate petabytes of unstructured video data.
        • Semantic Chunking: Video streams are partitioned into temporally coherent segments using motion detection and object recognition algorithms.
        • Multimodal Embeddings: Visual Data, Audio Data, and Textual Metadata are encoded into unified vector representations using models like CLIP.
        • Event Correlation Engine: A rules-based subsystem links detected objects with environmental sensors to build contextual narratives.
    • Layered Agentic Architecture for Conversational Interaction:
      • Perception Layer: Environmental Sensing and Data Acquisition:
        • Functions as the system's sensory apparatus.
        • Camera Health Monitoring: Proactive diagnostics using IoT protocols to detect obscured lenses.
        • Dynamic Frame Prioritization: Bandwidth-aware video sampling increases capture rates during security alerts.
        • Metadata Enrichment: On-edge processing units tagging footage with spatiotemporal coordinates.
      • Reasoning Layer: Contextual Retrieval and Intent Recognition:
        • Decomposes User Queries: Natural language inputs parsed into temporal filters, object classes, and anomaly criteria.
        • Executes Multimodal Retrieval: Combines semantic search with traditional SQL queries.
        • Assembles Response Context: Aggregates relevant video clips, sensor readings, and historical patterns.
      • Action Layer: Response Generation and System Orchestration:
        • Natural Language Generation: The RAG-augmented LLM synthesizes retrieved video metadata into conversational responses.
        • Proactive Alerting: When the retrieval subsystem detects high-risk patterns, the agent triggers SMS/push notifications.
        • Workflow Automation: Integration with physical security systems to lock doors or activate alarms.
    • Security and Compliance Architecture:
      • Adopting MAESTRO's seven-layer model, the architecture embeds:
        • Foundation Model Safeguards: Model alignment techniques to prevent hallucinated security alerts.
        • Data Layer Protections: Encryption of video streams and differential privacy for metadata embeddings.
        • Agent Framework Security: OAuth 2.0 authentication for API endpoints and containerized execution.
        • Compliance Auditing: Automated logging of all user queries and agent actions for GDPR/CCPA reporting.
    • Use Case Implementation Patterns:
      • Real Time Threat Investigation:
        • Query: "Find all vehicles circling building B in the last hour."
        • Retrieval: Semantic search for "vehicle" embeddings near geo-fenced coordinates.
        • Response: Detailed information about matching vehicles with license plates and timestamps.
      • Predictive Maintenance Automation:
        • Agent Detection: Recurrent "motion blur" artifacts in camera footage.
        • Proactive Action: Service ticket generation with diagnostic details.
    • Future Directions: Toward Multimodal Agent Ecosystems:
      • Emerging architectures propose federated learning across VSaaS providers.
      • Cross Platform Retrieval: Querying neighboring city cameras during amber alerts.
      • Explainable AI Overlays: Highlighting decisive video frames.
      • Ethical AI Layers: Implementing Kendall's mobility layer to automatically blur bystanders.
  • Citations:

[1] https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/
[2] https://smythos.com/ai-agents/agent-architectures/intelligent-agent-architecture/
[3] https://vectorize.io/designing-agentic-ai-systems-part-1-agent-architectures/
[4] https://smythos.com/ai-agents/multi-agent-systems/multi-agent-system-architecture/
[5] https://www.restack.io/p/agent-architecture-answer-layered-agent-architecture-cat-ai
[6] https://www.amplework.com/blog/ai-agent-lifecycle-management-scaling-digital-transformation/
[7] https://smythos.com/ai-agents/agent-architectures/layered-agent-architectures/
[8] https://cloudsecurityalliance.org/blog/2025/02/06/agentic-ai-threat-modeling-framework-maestro

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