Architecture¶

Comprehensive architectural overview

Understand the design and structure of 400+ modules and 237 enterprise features

Enterprise Architecture

Part of 237 enterprise modules with production-ready infrastructure patterns. See Enterprise Documentation.

System Layers

5-layer architecture

Explore
Components

Core building blocks

View
Data Flow

Request lifecycle

Understand
Diagrams

Visual architecture

View All

Overview¶

Design Philosophy

AgenticAI Framework is built on a modular, event-driven architecture that enables scalable and maintainable agentic applications with 400+ modules including 237 enterprise-grade modules.

Core Design Principles¶

Modularity: 400+ independently composable modules with single responsibilities
Extensibility: 237 enterprise modules across 14 categories
Observability: Built-in monitoring, tracing, and APM throughout
Safety: Guardrails, security, and compliance at every layer
Performance: Optimized for high-throughput enterprise scenarios
Scalability: Horizontal scaling with DDD patterns and CQRS

Enterprise Architecture¶

237 Enterprise Modules

The framework includes a comprehensive enterprise layer with modules organized into 14 categories:

Category	Modules	Key Components
API Management	15	Gateway, Versioning, Lifecycle, Analytics
Security & Compliance	18	Encryption, Auth, RBAC, PII Detection
Data Processing	16	Pipeline, ETL, Lineage, Quality
ML/AI Infrastructure	14	Inference, Feature Store, RAG, Embeddings
Messaging & Events	12	Broker, Pub/Sub, Event Sourcing, CQRS
Infrastructure	20	Load Balancer, Circuit Breaker, Service Mesh
DevOps & Deployment	15	Canary, Blue-Green, Chaos Engineering
Domain-Driven Design	12	Aggregate, Saga, Bounded Context
Storage & Caching	14	Cache Manager, Redis, Distributed Cache
Observability	16	Tracing, Metrics, Alerting, APM
Workflow & Orchestration	12	Engine, Scheduler, State Machine
Integration Connectors	18	ServiceNow, GitHub, Cloud APIs
Governance	10	Policy, Access Control, Quota Manager
Performance	15	Router, Connection Pooling, Throttle

High-Level Design (HLD)¶

System Overview¶

Architecture Layers

The framework is organized into 5 distinct layers, each with specific responsibilities:

graph TB
    subgraph "Layer 1: Application Layer"
        UA[" User Application<br/>Custom AI Applications"]
        API[" Framework APIs<br/>Python SDK Interface"]
    end

    subgraph "Layer 2: Agent Orchestration"
        AM[" Agent Manager<br/>Lifecycle & Coordination"]
        A1[" Specialized Agent 1<br/>Domain Expert"]
        A2[" Specialized Agent 2<br/>Task Executor"]
        AN[" Agent N<br/>Custom Role"]
    end

    subgraph "Layer 3: Task & Process Management"
        TM[" Task Manager<br/>Task Queue & Scheduling"]
        PM[" Process Manager<br/>Workflow Orchestration"]
        CM[" Communication Manager<br/>Inter-Agent Messages"]
    end

    subgraph "Layer 4: Core Intelligence Services"
        LM[" LLM Manager<br/>Model Integration"]
        MM[" Memory Manager<br/>State Persistence"]
        KM[" Knowledge Manager<br/>Information Retrieval"]
        GM[" Guardrail Manager<br/>Safety & Compliance"]
    end

    subgraph "Layer 5: Infrastructure & Integration"
        MON[" Monitoring System<br/>Metrics & Logs"]
        CONFIG[" Configuration Manager<br/>Settings & Secrets"]
        HUB[" Hub<br/>Agent Discovery"]
        SEC[" Security Manager<br/>Auth & Validation"]
    end

    subgraph "External Systems"
        LLMS[" LLM Providers<br/>OpenAI, Anthropic, Azure"]
        DB[" Databases<br/>Redis, PostgreSQL, MongoDB"]
        APIS[" External APIs<br/>REST, GraphQL"]
        TOOLS[" MCP Tools<br/>External Tools"]
    end

    UA --> API
    API --> AM
    AM --> A1 & A2 & AN
    A1 & A2 & AN --> TM
    TM --> PM & CM
    PM --> LM & MM & KM
    GM -."validates".-> LM & MM & KM
    MON -."observes".-> AM & TM & PM
    CONFIG --> AM & TM & PM
    HUB --> A1 & A2 & AN
    SEC -."protects".-> API & AM
    LM --> LLMS
    MM & KM --> DB
    CM --> APIS
    PM --> TOOLS

    classDef layer1 fill:#e3f2fd,stroke:#1976d2,stroke-width:2px
    classDef layer2 fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
    classDef layer3 fill:#e8f5e9,stroke:#388e3c,stroke-width:2px
    classDef layer4 fill:#fff3e0,stroke:#f57c00,stroke-width:2px
    classDef layer5 fill:#fce4ec,stroke:#c2185b,stroke-width:2px
    classDef external fill:#eceff1,stroke:#455a64,stroke-width:2px

    class UA,API layer1
    class AM,A1,A2,AN layer2
    class TM,PM,CM layer3
    class LM,MM,KM,GM layer4
    class MON,CONFIG,HUB,SEC layer5
    class LLMS,DB,APIS,TOOLS external

Component Interaction Diagram¶

Request Flow Through the System

This sequence diagram demonstrates how a typical user request flows through all layers of the AgenticAI Framework, showcasing the interaction between components.

Request Flow Steps:

User Submits Request (Step 1-2)
User sends request through API
API validates and routes to Agent Manager
Agent Assignment (Step 3-4)
Agent Manager selects appropriate agent based on capabilities
Agent receives task assignment
Task created in Task Manager queue
Input Validation (Step 5-6)
Guardrails validates user input for safety
Checks for malicious content, PII, policy violations
Returns validation result (pass/fail)
Context Retrieval (Step 7-8)
Task Manager queries Memory for relevant historical data
Retrieves past interactions, user preferences, learned patterns
Provides context for better response generation
Response Generation (Step 9-12)
LLM Manager called to generate response
Before returning, output passes through Guardrails
Guardrails ensures response is safe, compliant, and appropriate
Approved output returned to Task Manager
Result Storage & Monitoring (Step 13-14)
Generated response stored in Memory for future context
Metrics logged to Monitoring system:
- Latency, token usage, cost
- Agent performance, success rate
- Resource utilization
Response Return (Step 15-18)
Task marked as complete
Agent reports status to Agent Manager
Response flows back through API
User receives final result

Continuous Monitoring: - All operations continuously observed by Monitoring system - Real-time metrics, alerts, and health checks - Full traceability for debugging and optimization

Key Principles: - \ud83d\udd12 Security First: Guardrails validate at input and output - \ud83d\udcbe Context-Aware: Memory provides historical context - \ud83d\udcca Observable: Every step monitored and logged - \ud83d\udd04 Asynchronous: Non-blocking operations where possible - \ud83d\udee1\ufe0f Resilient: Error handling at every layer

sequenceDiagram
    participant User
    participant API
    participant AgentMgr as Agent Manager
    participant Agent
    participant TaskMgr as Task Manager
    participant LLMMgr as LLM Manager
    participant Memory
    participant Guardrails
    participant Monitor

    User->>API: Submit Request
    API->>AgentMgr: Route to Agent
    AgentMgr->>Agent: Assign Task
    Agent->>TaskMgr: Create Task

    TaskMgr->>Guardrails: Validate Input
    Guardrails-->>TaskMgr: Validation Result

    TaskMgr->>Memory: Retrieve Context
    Memory-->>TaskMgr: Historical Data

    TaskMgr->>LLMMgr: Generate Response
    LLMMgr->>Guardrails: Validate Output
    Guardrails-->>LLMMgr: Approved Output
    LLMMgr-->>TaskMgr: Generated Response

    TaskMgr->>Memory: Store Result
    TaskMgr->>Monitor: Log Metrics

    TaskMgr-->>Agent: Task Complete
    Agent-->>AgentMgr: Report Status
    AgentMgr-->>API: Response
    API-->>User: Final Result

    Note over Monitor: Continuous observability

Data Flow Architecture¶

End-to-End Data Processing Pipeline

This flowchart illustrates how data flows from input to output through various processing stages and storage layers.

Input Stage: - \ud83d\udc65 User Input: Direct user requests via UI/API - \ud83c\udf10 External Data: Third-party APIs, webhooks, integrations

Processing Pipeline:

Input Validation
Schema validation
Type checking
Sanitization
Security scanning
Context Enrichment
Add user profile data
Inject relevant historical context
Append system state
Task Processing
Execute business logic
Coordinate with other services
Apply transformations
Response Generation
LLM invocation
Template rendering
Data formatting
Output Filtering
PII masking
Content moderation
Quality checks

Storage Layers:

Cache Layer: Hot data for <1ms access
Active sessions
Frequently accessed data
LLM response cache
Short-term Memory: Fast access (1-10ms)
Recent interactions
Session state
Temporary results
Long-term Storage: Persistent data (10-100ms)
User profiles
Historical records
Audit trail

Output Channels: - \u2705 User Response: Primary output to user - \ud83d\udcdd Audit Logs: Compliance and security tracking - \ud83d\udcca Metrics: Performance and business analytics

Data Flow Guarantees: - \ud83d\udd12 All sensitive data encrypted in transit and at rest - \ud83d\udcbe All state changes persisted to durable storage - \ud83d\udcdd All operations logged for audit trail - \ud83d\udd04 Failed operations automatically retried with exponential backoff

flowchart LR
    subgraph Input
        UI[User Input]
        EXT[External Data]
    end

    subgraph Processing
        VAL[Input Validation]
        CTX[Context Enrichment]
        PROC[Task Processing]
        GEN[Response Generation]
        FILTER[Output Filtering]
    end

    subgraph Storage
        MEM[(Short-term Memory)]
        LTM[(Long-term Storage)]
        CACHE[(Cache Layer)]
    end

    subgraph Output
        RES[User Response]
        LOG[Audit Logs]
        METRIC[Metrics]
    end

    UI --> VAL
    EXT --> VAL
    VAL --> CTX

    CTX --> MEM
    MEM --> PROC
    CACHE --> PROC

    PROC --> GEN
    GEN --> FILTER

    FILTER --> RES
    FILTER --> LOG
    PROC --> METRIC

    PROC --> LTM
    LTM --> CTX

    style VAL fill:#ffeb3b
    style FILTER fill:#ffeb3b
    style MEM fill:#4caf50
    style LTM fill:#4caf50
    style CACHE fill:#4caf50

Deployment Architecture¶

Scalability Options

The framework supports multiple deployment patterns:

graph TB
    subgraph "Development Environment"
        DEV[" Local Development<br/>Single Process<br/>In-Memory Storage"]
    end

    subgraph "Production - Single Node"
        API1[" API Server<br/>FastAPI/Flask"]
        AGENT1[" Agent Pool<br/>ThreadPool Executor"]
        REDIS1[(" Redis<br/>Memory & Cache")]
        DB1[(" PostgreSQL<br/>Persistent Storage")]

        API1 --> AGENT1
        AGENT1 --> REDIS1
        AGENT1 --> DB1
    end

    subgraph "Production - Distributed"
        LB[" Load Balancer<br/>nginx/ALB"]

        subgraph "API Tier"
            API2[" API 1"]
            API3[" API 2"]
            API4[" API N"]
        end

        subgraph "Agent Tier"
            WORKER1[" Worker 1<br/>Agent Pool"]
            WORKER2[" Worker 2<br/>Agent Pool"]
            WORKER3[" Worker N<br/>Agent Pool"]
        end

        subgraph "Message Queue"
            MQ[" RabbitMQ/Redis Queue<br/>Task Distribution"]
        end

        subgraph "Storage Tier"
            REDIS2[(" Redis Cluster<br/>Distributed Cache")]
            DB2[(" PostgreSQL<br/>Primary DB")]
            DB3[(" PostgreSQL<br/>Replica")]
            VECTOR[(" Vector DB<br/>Pinecone/Weaviate")]
        end

        subgraph "Monitoring"
            PROM[" Prometheus<br/>Metrics"]
            GRAF[" Grafana<br/>Dashboards"]
            ELK[" ELK Stack<br/>Logs"]
        end

        LB --> API2 & API3 & API4
        API2 & API3 & API4 --> MQ
        MQ --> WORKER1 & WORKER2 & WORKER3
        WORKER1 & WORKER2 & WORKER3 --> REDIS2
        WORKER1 & WORKER2 & WORKER3 --> DB2
        DB2 -."replication".-> DB3
        WORKER1 & WORKER2 & WORKER3 --> VECTOR

        API2 & API3 & API4 --> PROM
        WORKER1 & WORKER2 & WORKER3 --> PROM
        PROM --> GRAF
        API2 & API3 & API4 --> ELK
        WORKER1 & WORKER2 & WORKER3 --> ELK
    end

    DEV -."evolves to".-> API1
    API1 -."scales to".-> LB

    classDef dev fill:#e1f5fe,stroke:#01579b
    classDef prod fill:#f3e5f5,stroke:#4a148c
    classDef storage fill:#e8f5e9,stroke:#1b5e20
    classDef monitor fill:#fff3e0,stroke:#e65100

    class DEV dev
    class API1,AGENT1 prod
    class REDIS1,DB1 storage
    class LB,API2,API3,API4,WORKER1,WORKER2,WORKER3,MQ prod
    class REDIS2,DB2,DB3,VECTOR storage
    class PROM,GRAF,ELK monitor

Core Components¶

Agent Manager¶

Central Orchestration

The Agent Manager is the central orchestrator for all agents in the system.agents in the system.agents in the system.

Class Diagram - Agent Management¶

classDiagram
    class Agent {
        +str id
        +str name
        +str role
        +List~str~ capabilities
        +Dict config
        +str status
        +List memory
        +start() void
        +pause() void
        +resume() void
        +stop() void
        +execute_task(callable, args) Any
    }

    class AgentManager {
        -Dict~str,Agent~ agents
        -Queue task_queue
        +register_agent(Agent) void
        +get_agent(str) Agent
        +list_agents() List~Agent~
        +remove_agent(str) void
        +broadcast(str) void
        +assign_task(Task, Agent) void
    }

    class ContextManager {
        -int max_tokens
        -List~Context~ contexts
        -int current_tokens
        +add_context(str, float) void
        +get_context_summary() str
        +get_stats() Dict
        +clear() void
        -prune_contexts() void
    }

    class Task {
        +str id
        +str name
        +str description
        +int priority
        +List~str~ dependencies
        +str status
        +Any result
        +execute() Any
        +cancel() void
        +retry() void
    }

    AgentManager "1" --> "*" Agent: manages
    Agent "1" --> "1" ContextManager: uses
    Agent "1" --> "*" Task: executes
    AgentManager "1" --> "*" Task: queues agents in the system.

Agent Lifecycle State Machine¶

stateDiagram-v2
    [*] --> Initialized: create()

    Initialized --> Running: start()
    Initialized --> Terminated: destroy()

    Running --> Paused: pause()
    Running --> Executing: execute_task()
    Running --> Terminated: stop()

    Paused --> Running: resume()
    Paused --> Terminated: stop()

    Executing --> Running: task_complete()
    Executing --> Error: task_failed()
    Executing --> Terminated: stop()

    Error --> Running: retry()
    Error --> Terminated: stop()

    Terminated --> [*]

    note right of Initialized
        Agent created with
        name, role, capabilities
    end note

    note right of Running
        Agent ready to
        accept tasks
    end note

    note right of Executing
        Agent actively
        processing task
    end note

Responsibilities: - Agent lifecycle management (create, start, stop, destroy) - Agent registration and discovery - Inter-agent communication coordination - Resource allocation and load balancing

Key Interfaces:

Python
class AgentManager:
    def register_agent(self, agent: Agent) -> None
    def get_agent(self, agent_id: str) -> Optional[Agent]
    def list_agents(self) -> List[Agent]
    def broadcast(self, message: str) -> None
    def remove_agent(self, agent_id: str) -> None

Agent¶

Individual autonomous entities that execute tasks and make decisions.

Properties: - Identity: Unique ID, name, and role - Capabilities: List of what the agent can do - Configuration: Runtime parameters and settings - State: Current status and execution context - Memory: Access to short-term and long-term storage

Lifecycle: 1. Initialization: Agent is created with configuration 2. Registration: Agent registers with AgentManager 3. Activation: Agent becomes ready to receive tasks 4. Execution: Agent processes tasks and communicates 5. Deactivation: Agent stops processing new tasks 6. Cleanup: Agent releases resources

Task Manager¶

Coordinates task execution across agents and manages dependencies.

Features: - Task queuing and prioritization - Dependency resolution - Parallel and sequential execution - Task result aggregation - Error handling and retry logic

Task Lifecycle:

stateDiagram-v2
    [*] --> Created
    Created --> Queued
    Queued --> Running
    Running --> Completed
    Running --> Failed
    Failed --> Retrying
    Retrying --> Running
    Retrying --> Failed
    Completed --> [*]
    Failed --> [*]

Memory Manager¶

Provides multi-tiered storage for agents and the system.

Memory Types: - Short-term: Fast access, temporary data (RAM) - Long-term: Persistent storage (disk/database) - External: Distributed storage systems

Features: - Automatic memory management - Cache optimization - Memory compression - Data lifecycle policies

LLM Manager¶

Abstracts language model interactions and provides a unified interface.

Capabilities: - Multi-provider support (OpenAI, Anthropic, etc.) - Model switching and load balancing - Request/response caching - Rate limiting and quota management - Model performance monitoring

Knowledge Manager¶

Handles information retrieval and knowledge base integration.

Components: - Retrieval Engine: Search and ranking algorithms - Indexing System: Document processing and storage - Cache Layer: Fast access to frequently used information - Integration APIs: Connect to external knowledge sources

Guardrail Manager¶

Ensures safe and compliant agent behavior.

Guardrail Types: - Input validation: Check incoming data - Output filtering: Validate generated content - Behavior monitoring: Track agent actions - Compliance checking: Ensure regulatory adherence

Communication Patterns¶

Agent-to-Agent Communication¶

Python
# Direct communication
agent1.send_message(agent2, "Hello from Agent 1")

# Broadcast communication
agent_manager.broadcast("System maintenance in 5 minutes")

# Event-driven communication
agent1.emit_event("task_completed", {"task_id": "123"})
agent2.on_event("task_completed", handle_completion)

Task Coordination¶

Python
# Sequential execution
process = Process("DataPipeline", strategy="sequential")
process.add_task(collect_data)
process.add_task(process_data)
process.add_task(analyze_data)

# Parallel execution
process = Process("ParallelAnalysis", strategy="parallel")
process.add_task(analyze_sentiment)
process.add_task(extract_entities)
process.add_task(classify_topic)

Data Flow¶

Request Processing Flow¶

Input Validation: Guardrails check incoming requests
Task Creation: Request converted to executable tasks
Agent Selection: Appropriate agent(s) chosen for execution
Execution: Agent processes the task using available resources
Result Validation: Output checked by guardrails
Response Generation: Results formatted and returned

Memory Access Pattern¶

sequenceDiagram
    participant A as Agent
    participant M as Memory Manager
    participant ST as Short-term
    participant LT as Long-term
    participant EXT as External

    A->>M: retrieve("key")
    M->>ST: check short-term
    alt found in short-term
        ST-->>M: return value
        M-->>A: return value
    else not found
        M->>LT: check long-term
        alt found in long-term
            LT-->>M: return value
            M->>ST: cache in short-term
            M-->>A: return value
        else not found
            M->>EXT: check external
            EXT-->>M: return value
            M->>LT: store in long-term
            M->>ST: cache in short-term
            M-->>A: return value
        end
    end

Scalability Patterns¶

Horizontal Scaling¶

Agent Distribution: Spread agents across multiple processes/machines
Load Balancing: Distribute tasks based on agent capacity
Service Mesh: Microservice architecture for large deployments

Vertical Scaling¶

Resource Optimization: Efficient memory and CPU usage
Caching Strategies: Reduce redundant computations
Connection Pooling: Reuse database and API connections

Security Architecture¶

Multi-Layer Security¶

Input Layer: Validate and sanitize all inputs
Processing Layer: Monitor agent behavior and resource usage
Output Layer: Filter and validate all outputs
Storage Layer: Encrypt data at rest and in transit
Communication Layer: Secure inter-agent and external communications

Access Control¶

Python
# Role-based access control
agent = Agent(
    name="SecureAgent",
    role="DataProcessor",
    capabilities=["read_data", "process_data"], # No write permissions
    config={
        "security_level": "high",
        "allowed_resources": ["database_read", "api_public"]
    }
)

Error Handling Strategy¶

Error Categories¶

System Errors: Infrastructure failures, network issues
Agent Errors: Logic errors, capability mismatches
Data Errors: Invalid inputs, corrupted data
Security Errors: Unauthorized access, policy violations

Recovery Mechanisms¶

Retry Logic: Automatic retry with exponential backoff
Fallback Strategies: Alternative execution paths
Circuit Breakers: Prevent cascade failures
Graceful Degradation: Reduced functionality when components fail

Performance Considerations¶

Optimization Strategies¶

Lazy Loading: Load resources only when needed
Batch Processing: Group similar operations
Asynchronous Execution: Non-blocking operations
Resource Pooling: Reuse expensive resources
Monitoring-Driven Optimization: Use metrics to guide improvements

Bottleneck Identification¶

CPU Bound: Long-running computations
I/O Bound: Database and API calls
Memory Bound: Large data processing
Network Bound: External service dependencies

Extension Points¶

Custom Components¶

Python
# Custom agent
class MyCustomAgent(Agent):
    def __init__(self, name, specialized_config):
        super().__init__(name, "CustomRole", ["custom_capability"], specialized_config)

    def custom_method(self):
        # Custom implementation
        pass

# Custom guardrail
class BusinessLogicGuardrail(Guardrail):
    def __init__(self):
        super().__init__("BusinessLogic", self.validate_business_rules)

    def validate_business_rules(self, data):
        # Custom validation logic
        return True

Plugin Architecture¶

Python
# Register custom tools
hub = Hub()
hub.register_service("CustomTool", my_custom_tool)

# Register custom LLM provider
llm_manager = LLMManager()
llm_manager.register_model("custom-model", my_custom_llm_function)

Deployment Patterns¶

Single-Node Deployment¶

All components run in a single process
Suitable for development and small applications
Easy to debug and monitor

Multi-Node Deployment¶

Components distributed across multiple machines
Better scalability and fault tolerance
Requires service discovery and coordination

Containerized Deployment¶

Docker
# Example Dockerfile for AgenticAI application
FROM python:3.9-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .
CMD ["python", "app.py"]

Cloud-Native Deployment¶

Kubernetes orchestration
Auto-scaling based on load
Service mesh for communication
Observability stack integration

This architecture provides a solid foundation for building scalable, maintainable, and secure agentic applications while remaining flexible enough to accommodate diverse use cases and requirements.