Architecture Diagrams¶

Comprehensive HLD and LLD diagrams

Visualize system design across 400+ modules and 237 enterprise features

Enterprise Diagrams

Part of 237 enterprise modules with comprehensive architecture documentation. See Enterprise Documentation.

Overview¶

This page provides a complete reference of all architectural diagrams used throughout the AgenticAI Framework documentation. These diagrams help visualize system design, component interactions, data flows, and operational patterns.

Using These Diagrams

All diagrams use Mermaid.js syntax, which is natively supported by:

GitHub (Markdown files, Wiki, Issues, PRs)
MkDocs with Material theme
Confluence, Notion, and many other platforms

Simply copy the diagram code blocks into your documentation!

System Architecture¶

Complete System Overview¶

System Architecture Overview

This diagram illustrates the complete end-to-end system architecture of the AgenticAI Framework, showing all layers from client applications to data storage.

Key Components:

External Clients Layer: Entry points for web, API, and CLI users
API Gateway Layer: Handles authentication, rate limiting, and load balancing
Application Layer: Protocol-specific servers (REST, WebSocket, gRPC)
Agent Orchestration: Manages agent lifecycle, pooling, and scheduling
Processing Layer: Task execution, workflow orchestration, and process management
Intelligence Layer: Core AI capabilities (LLM, Memory, Knowledge, Guardrails)
Infrastructure Layer: Cross-cutting concerns (monitoring, logging, caching, queuing)
Data Layer: Persistent storage across multiple database types
External Services: Third-party AI providers and cloud services

Data Flow: 1. Clients send requests through the load balancer 2. Requests pass through authentication and rate limiting 3. Routed to appropriate protocol handler (REST/WebSocket/gRPC) 4. Agent Manager orchestrates agent selection and task assignment 5. Tasks are executed through the workflow engine 6. Intelligence layer provides AI capabilities (LLM, memory, knowledge) 7. Guardrails validate all AI operations for safety 8. Results are stored in appropriate databases 9. Monitoring and logging capture all operations

Color Coding: - Blue: External systems and clients - Purple: Gateway and security - Green: Application and orchestration - Orange: Processing and workflows - Red: Intelligence and AI services - Yellow-Green: Infrastructure services - Cyan: Data storage

graph TB
    subgraph "External Clients"
        WEB[Web Applications]
        API_CLIENTS[API Clients]
        CLI[Command Line Tools]
    end

    subgraph "API Gateway Layer"
        LB[Load Balancer]
        AUTH[Authentication]
        RATE[Rate Limiter]
    end

    subgraph "Application Layer"
        REST[REST API]
        WEBSOCKET[WebSocket Server]
        GRPC[gRPC Server]
    end

    subgraph "Agent Orchestration Layer"
        AM[Agent Manager]
        AP[Agent Pool]
        AS[Agent Scheduler]
    end

    subgraph "Processing Layer"
        TM[Task Manager]
        PM[Process Manager]
        WF[Workflow Engine]
    end

    subgraph "Intelligence Layer"
        LLM[LLM Manager]
        MEM[Memory Manager]
        KNOW[Knowledge Base]
        GUARD[Guardrails]
    end

    subgraph "Infrastructure Layer"
        MON[Monitoring]
        LOG[Logging]
        CACHE[Caching]
        QUEUE[Message Queue]
    end

    subgraph "Data Layer"
        REDIS[(Redis)]
        POSTGRES[(PostgreSQL)]
        MONGO[(MongoDB)]
        VECTOR[(Vector DB)]
        S3[(Object Storage)]
    end

    subgraph "External Services"
        OPENAI[OpenAI]
        ANTHROPIC[Anthropic]
        AZURE[Azure Services]
        AWS[AWS Services]
    end

    WEB & API_CLIENTS & CLI --> LB
    LB --> AUTH --> RATE
    RATE --> REST & WEBSOCKET & GRPC

    REST & WEBSOCKET & GRPC --> AM
    AM --> AP & AS
    AP & AS --> TM
    TM --> PM --> WF

    WF --> LLM & MEM & KNOW
    GUARD -.validates.-> LLM & MEM & KNOW

    MON & LOG -.observes.-> AM & TM & PM & WF
    QUEUE --> TM
    CACHE --> MEM

    MEM --> REDIS & POSTGRES & MONGO
    KNOW --> VECTOR & POSTGRES
    LLM --> CACHE

    LLM --> OPENAI & ANTHROPIC & AZURE
    PM --> AWS

    MON --> POSTGRES
    LOG --> S3

    classDef external fill:#e3f2fd,stroke:#1976d2
    classDef gateway fill:#f3e5f5,stroke:#7b1fa2
    classDef app fill:#e8f5e9,stroke:#388e3c
    classDef processing fill:#fff3e0,stroke:#f57c00
    classDef intel fill:#fce4ec,stroke:#c2185b
    classDef infra fill:#f1f8e9,stroke:#689f38
    classDef data fill:#e0f7fa,stroke:#0097a7

    class WEB,API_CLIENTS,CLI external
    class LB,AUTH,RATE gateway
    class REST,WEBSOCKET,GRPC app
    class AM,AP,AS,TM,PM,WF processing
    class LLM,MEM,KNOW,GUARD intel
    class MON,LOG,CACHE,QUEUE infra
    class REDIS,POSTGRES,MONGO,VECTOR,S3 data

Microservices Architecture¶

Microservices Design Pattern

This diagram shows the distributed microservices architecture for deploying AgenticAI at scale.

Service Breakdown:

Agent Service (Port 8001): Manages agent lifecycle, registration, and state
Task Service (Port 8002): Handles task creation, scheduling, and execution
Memory Service (Port 8003): Provides distributed memory storage and retrieval
LLM Service (Port 8004): Manages LLM provider connections and request routing
Knowledge Service (Port 8005): Handles knowledge base operations and vector search

Support Services:

Auth Service (Port 9001): Centralized authentication and authorization
Monitor Service (Port 9002): Metrics collection and health checks
Log Service (Port 9003): Centralized logging aggregation

Infrastructure Components:

API Gateway (Kong/Nginx): Single entry point with routing, rate limiting, SSL
Service Mesh (Istio/Linkerd): Service-to-service communication, observability, security
Message Bus (Kafka): Asynchronous event-driven communication between services

Benefits of This Architecture:

Independent Scaling: Scale each service based on its specific load
Fault Isolation: Failures in one service don't cascade to others
Technology Flexibility: Each service can use optimal technology stack
Easier Updates: Deploy services independently without full system downtime
Team Autonomy: Different teams can own and develop separate services

Service Communication: 1. Synchronous: Direct HTTP/gRPC calls through service mesh 2. Asynchronous: Events published to Kafka for eventual consistency 3. Authentication: All services verify requests with Auth Service 4. Observability: All traffic flows through service mesh for monitoring

graph LR
    subgraph "Client Applications"
        CLIENT[Clients]
    end

    subgraph "API Gateway"
        GW[API Gateway<br/>Kong/Nginx]
    end

    subgraph "Core Services"
        AS[Agent Service<br/>:8001]
        TS[Task Service<br/>:8002]
        MS[Memory Service<br/>:8003]
        LS[LLM Service<br/>:8004]
        KS[Knowledge Service<br/>:8005]
    end

    subgraph "Support Services"
        AUTH_SVC[Auth Service<br/>:9001]
        MON_SVC[Monitor Service<br/>:9002]
        LOG_SVC[Log Service<br/>:9003]
    end

    subgraph "Message Bus"
        KAFKA[Apache Kafka]
    end

    subgraph "Service Mesh"
        ISTIO[Istio/Linkerd]
    end

    CLIENT --> GW
    GW --> ISTIO
    ISTIO --> AS & TS & MS & LS & KS

    AS & TS & MS & LS & KS --> KAFKA
    AS & TS & MS & LS & KS --> AUTH_SVC
    AS & TS & MS & LS & KS --> MON_SVC
    AS & TS & MS & LS & KS --> LOG_SVC

    style CLIENT fill:#4caf50
    style GW fill:#2196f3
    style KAFKA fill:#ff9800
    style ISTIO fill:#9c27b0

Agent Architecture¶

Agent Communication Patterns¶

Inter-Agent Communication Patterns

This diagram illustrates the four primary communication patterns supported by AgenticAI for agent-to-agent interaction.

Pattern 1: Point-to-Point (Direct Messaging)

Use Case: When one agent needs to send a specific message to another agent
Characteristics: Direct, synchronous, guaranteed delivery
Example: Agent A requests data processing from Agent B
Best For: Simple, targeted interactions between two agents

Pattern 2: Publish-Subscribe (Pub-Sub)

Use Case: Broadcasting events to multiple interested agents
Characteristics: Asynchronous, one-to-many, decoupled
Example: Agent publishes "data_updated" event, multiple agents subscribe
Best For: Event-driven architectures, notifications, state changes
Benefits: Publishers don't need to know subscribers, easy to add new subscribers

Pattern 3: Request-Reply (RPC-style)

Use Case: When an agent needs a response from another agent
Characteristics: Synchronous, bidirectional, correlation tracking
Example: Agent requests analysis from another agent and waits for result
Best For: Service-oriented interactions, API-like calls between agents
Flow: Request → Broker routes to handler → Handler processes → Reply

Pattern 4: Broadcast (Fan-Out)

Use Case: Sending messages to all agents without subscription
Characteristics: Fire-and-forget, no acknowledgment, all agents receive
Example: Emergency shutdown signal sent to all agents
Best For: System-wide announcements, alerts, coordination signals

Choosing the Right Pattern:

Pattern	Latency	Coupling	Reliability	Use When
Point-to-Point	Low	High	High	Direct, targeted communication
Pub-Sub	Medium	Low	Medium	Multiple consumers needed
Request-Reply	Medium	Medium	High	Response required
Broadcast	Low	Low	Low	System-wide notifications

graph TB
    subgraph "Communication Patterns"
        subgraph "Point-to-Point"
            A1[Agent A] -->|Direct Message| A2[Agent B]
        end

        subgraph "Publish-Subscribe"
            PUB[Publisher Agent]
            TOPIC[Message Topic]
            SUB1[Subscriber 1]
            SUB2[Subscriber 2]
            SUB3[Subscriber N]

            PUB --> TOPIC
            TOPIC --> SUB1 & SUB2 & SUB3
        end

        subgraph "Request-Reply"
            REQ[Requesting Agent] -->|Request| BROKER[Message Broker]
            BROKER -->|Route| RESP[Responding Agent]
            RESP -->|Reply| BROKER
            BROKER -->|Return| REQ
        end

        subgraph "Broadcast"
            BCAST[Broadcaster]
            R1[Receiver 1]
            R2[Receiver 2]
            R3[Receiver N]

            BCAST -.->|Broadcast| R1 & R2 & R3
        end
    end

Agent Collaboration Model¶

Multi-Agent Collaboration Workflow

This diagram demonstrates how multiple specialized agents collaborate to solve complex tasks through task decomposition and coordination.

Collaboration Flow:

Complex Task Arrival
A high-level task that's too complex for a single agent
Example: "Generate a comprehensive market analysis report"
Task Decomposition
Coordinator breaks down the task into manageable subtasks
Each subtask matches the capabilities of specialized agents
Specialized Agent Assignment
Data Agent: Skilled in web scraping, API calls, data collection
Analysis Agent: Expert in statistics, machine learning, pattern recognition
Report Agent: Specializes in data visualization, report writing, formatting
Parallel Execution
Each agent works independently on their subtask
Agents may communicate if dependencies exist
Result Coordination
All agents report completed work to Coordinator Agent
Coordinator ensures all subtasks are complete
Handles any failures or retries
Result Synthesis
Coordinator merges all results intelligently
Resolves conflicts or inconsistencies
Creates cohesive final output

Coordinator Agent Responsibilities: - Task planning and decomposition - Agent selection and assignment - Progress monitoring - Error handling and recovery - Result aggregation - Quality validation

Benefits of This Model: - Specialization: Each agent focuses on what it does best - Parallelism: Subtasks executed concurrently for faster completion - Scalability: Add more specialized agents as needed - Maintainability: Easier to update individual agent capabilities - Reusability: Agents can participate in multiple workflows

Color Coding: - Green: Input task - Orange: Coordination and orchestration - Blue: Final output

graph TB
    TASK[Complex Task] --> DECOMP[Task Decomposition]

    DECOMP --> SUB1[Subtask 1:<br/>Data Collection]
    DECOMP --> SUB2[Subtask 2:<br/>Analysis]
    DECOMP --> SUB3[Subtask 3:<br/>Reporting]

    SUB1 --> A1[Data Agent<br/>Skills: scraping, API calls]
    SUB2 --> A2[Analysis Agent<br/>Skills: statistics, ML]
    SUB3 --> A3[Report Agent<br/>Skills: visualization, writing]

    A1 -->|Data| COORD[Coordinator Agent]
    A2 -->|Insights| COORD
    A3 -->|Report| COORD

    COORD --> MERGE[Result Synthesis]
    MERGE --> RESULT[Final Output]

    style TASK fill:#4caf50
    style COORD fill:#ff9800
    style RESULT fill:#2196f3

Data Architecture¶

Multi-Tier Storage Strategy¶

Intelligent Data Tiering for Performance & Cost

This diagram illustrates the multi-tier storage architecture that automatically manages data based on access patterns, optimizing both performance and cost.

Tier 1: Hot Tier (Milliseconds Access)

In-Memory Cache: Session-level data, ultra-fast access
Size: 100-500 MB per instance
TTL: Duration of session
Use: Active agent state, current task context
Redis Cache: Distributed caching layer
Size: 1-10 GB
TTL: Seconds to minutes
Use: Frequently accessed data, LLM response cache, rate limiting

Tier 2: Warm Tier (Sub-second Access)

Redis Persistent: Durable Redis with AOF/RDB
Size: 10-100 GB
TTL: Hours to days
Use: Agent memory, session data, task queues
PostgreSQL Hot: Actively queried relational data
Size: 100 GB - 1 TB
Indexed tables for fast queries
Use: Agent metadata, task definitions, user data

Tier 3: Cold Tier (1-5 seconds Access)

PostgreSQL Archive: Partitioned historical data
Size: 1-10 TB
Partitioned by date for efficient queries
Use: Historical task results, audit logs, agent history
MongoDB: Flexible document storage
Size: 1-10 TB
Use: Unstructured data, logs, agent outputs, knowledge base

Tier 4: Frozen Tier (5+ seconds Access)

S3/Blob Storage: Compressed archives
Size: 10+ TB
Use: Long-term backups, large files, model artifacts
Glacier/Archive: Ultra-cheap archival
Size: Unlimited
Access time: Minutes to hours
Use: Compliance data, long-term retention

Data Lifecycle:

Creation: New data starts in Hot Tier (memory/Redis)
Promotion: Frequently accessed data stays in Hot Tier
Aging: Less accessed data moves to Warm Tier
Archival: Old data moves to Cold Tier
Retention: Compliance data moves to Frozen Tier
Retrieval: On-demand data fetched from lower tiers

Access Pattern Optimization:

Access Frequency	Target Tier	Expected Latency	Cost per GB
Multiple/sec	Hot (Redis)	< 1ms	$$$$$
Multiple/min	Warm (Redis Persist)	< 10ms	$$$$
Few times/day	Warm (PostgreSQL)	< 100ms	$$$
Weekly/Monthly	Cold (Archive)	1-5s	$$
Rarely	Frozen (Glacier)	Minutes	$

Cost Optimization Strategy: - Hot tier: 5-10% of data, 80-90% of reads - Warm tier: 20-30% of data, 10-15% of reads - Cold tier: 50-60% of data, 3-5% of reads - Frozen tier: 10-20% of data, < 1% of reads

graph LR
    subgraph "Hot Tier (Milliseconds)"
        REDIS_CACHE[Redis Cache<br/>TTL: Seconds-Minutes<br/>Size: 1-10 GB]
        MEMORY[In-Memory<br/>TTL: Session<br/>Size: 100-500 MB]
    end

    subgraph "Warm Tier (Sub-second)"
        REDIS_PERSIST[Redis Persistent<br/>TTL: Hours-Days<br/>Size: 10-100 GB]
        POSTGRES_HOT[PostgreSQL Hot<br/>Indexed Tables<br/>Size: 100 GB - 1 TB]
    end

    subgraph "Cold Tier (1-5 seconds)"
        POSTGRES_ARCHIVE[PostgreSQL Archive<br/>Partitioned Tables<br/>Size: 1-10 TB]
        MONGO[MongoDB<br/>Document Store<br/>Size: 1-10 TB]
    end

    subgraph "Frozen Tier (5+ seconds)"
        S3[S3/Blob Storage<br/>Compressed Archives<br/>Size: 10+ TB]
        GLACIER[Glacier/Archive<br/>Long-term Storage<br/>Size: Unlimited]
    end

    APP[Application] --> MEMORY
    MEMORY -.promote.-> REDIS_CACHE
    REDIS_CACHE -.persist.-> REDIS_PERSIST
    REDIS_PERSIST -.archive.-> POSTGRES_HOT
    POSTGRES_HOT -.partition.-> POSTGRES_ARCHIVE
    POSTGRES_ARCHIVE -.compress.-> S3
    S3 -.archive.-> GLACIER

    GLACIER -.restore.-> S3
    S3 -.load.-> POSTGRES_ARCHIVE
    POSTGRES_ARCHIVE -.promote.-> POSTGRES_HOT

    style MEMORY fill:#4caf50
    style REDIS_CACHE fill:#8bc34a
    style REDIS_PERSIST fill:#ffc107
    style POSTGRES_HOT fill:#ff9800
    style POSTGRES_ARCHIVE fill:#ff5722
    style S3 fill:#9e9e9e
    style GLACIER fill:#607d8b

Data Replication Strategy¶

graph TB
    subgraph "Primary Region (US-East)"
        PRIMARY[(Primary DB<br/>Read/Write)]
        REPLICA1[(Read Replica 1)]
        REPLICA2[(Read Replica 2)]

        PRIMARY -.sync replication.-> REPLICA1
        PRIMARY -.sync replication.-> REPLICA2
    end

    subgraph "Secondary Region (US-West)"
        SECONDARY[(Secondary DB<br/>Read/Write)]
        REPLICA3[(Read Replica 1)]

        SECONDARY -.sync replication.-> REPLICA3
    end

    subgraph "DR Region (EU)"
        DR[(DR Database<br/>Read Only)]
    end

    PRIMARY -.async replication.-> SECONDARY
    PRIMARY -.async replication.-> DR

    APP_EAST[Apps US-East] --> REPLICA1 & REPLICA2
    APP_WEST[Apps US-West] --> SECONDARY & REPLICA3
    APP_EU[Apps EU] --> DR

    APP_EAST -.writes.-> PRIMARY
    APP_WEST -.writes.-> SECONDARY

    PRIMARY <-.conflict resolution.-> SECONDARY

    style PRIMARY fill:#4caf50
    style SECONDARY fill:#8bc34a
    style DR fill:#ff9800

Security Architecture¶

Authentication & Authorization Flow¶

End-to-End Security Flow

This sequence diagram shows the complete authentication and authorization flow for securing AgenticAI operations.

Phase 1: Login & Token Issuance (Steps 1-8)

User initiates login through client application
Client sends credentials to API Gateway
Gateway forwards to dedicated Auth Service
Auth Service queries database to verify credentials
Database returns user record with assigned roles
Auth Service generates JWT token with embedded claims:
User ID
Roles and permissions
Expiration time (typically 15-60 minutes)
Refresh token (for re-authentication)
Tokens returned through Gateway to Client
User sees successful login

Phase 2: Secure Token Storage

Client stores JWT securely (httpOnly cookie or secure storage)
Refresh token stored separately for token renewal
Never store tokens in localStorage (XSS vulnerability)

Phase 3: Authenticated Request (Steps 9-14)

User requests agent action
Client includes JWT in Authorization header: Bearer <token>
Gateway validates token:
Verifies cryptographic signature
Checks expiration timestamp
Extracts user claims (ID, roles)
Request forwarded to Agent Service with user context
Agent performs Role-Based Access Control (RBAC) check

Phase 4: Authorization Decision

If Authorized: Agent executes action, returns result
If Unauthorized: Agent returns 403 Forbidden, client shows error

Security Features:

JWT Signing: Cryptographically signed tokens prevent tampering
Token Expiration: Short-lived tokens limit exposure window
Refresh Tokens: Renew access without re-entering credentials
RBAC: Fine-grained permission control based on user roles
Audit Trail: All security events logged for compliance

Token Claims Example:

JSON
{
  "sub": "user_12345",
  "roles": ["agent_creator", "task_executor"],
  "permissions": ["agents:read", "agents:write", "tasks:execute"],
  "exp": 1703001600,
  "iat": 1703000000
}

sequenceDiagram
    participant User
    participant Client
    participant Gateway as API Gateway
    participant Auth as Auth Service
    participant Agent as Agent Service
    participant DB as User DB

    User->>Client: Login (username, password)
    Client->>Gateway: POST /auth/login
    Gateway->>Auth: Forward credentials

    Auth->>DB: Verify credentials
    DB-->>Auth: User record + roles

    Auth->>Auth: Generate JWT token
    Auth-->>Gateway: JWT token + refresh token
    Gateway-->>Client: Authentication response
    Client-->>User: Login successful

    Note over Client: Store tokens securely

    User->>Client: Request agent action
    Client->>Gateway: POST /agents/execute<br/>Header: Bearer <JWT>

    Gateway->>Gateway: Validate JWT signature
    Gateway->>Gateway: Check token expiration
    Gateway->>Gateway: Extract claims (user_id, roles)

    Gateway->>Agent: Forward request + user context
    Agent->>Agent: Check permissions (RBAC)

    alt Authorized
        Agent->>Agent: Execute action
        Agent-->>Gateway: Success response
        Gateway-->>Client: Result
        Client-->>User: Action completed
    else Unauthorized
        Agent-->>Gateway: 403 Forbidden
        Gateway-->>Client: Access denied
        Client-->>User: Permission error
    end

Security Layers¶

Defense in Depth Security Architecture

This diagram illustrates the multi-layered security approach that protects AgenticAI at every level of the stack.

Layer 1: Network Security (Perimeter Defense)

Web Application Firewall (WAF)
Blocks common web attacks (SQL injection, XSS)
Custom rules for API protection
Managed rule sets from OWASP Top 10
DDoS Protection
Rate-based rules to detect volumetric attacks
Traffic shaping and throttling
CloudFlare/AWS Shield integration
SSL/TLS Termination
All traffic encrypted with TLS 1.3
Certificate management and auto-renewal
Forward secrecy enabled

Layer 2: API Security (Access Control)

Authentication (JWT/OAuth2)
Token-based authentication
Multi-factor authentication (MFA) support
Session management
Authorization (RBAC/ABAC)
Role-Based Access Control for user roles
Attribute-Based Access Control for fine-grained permissions
Policy enforcement points
Rate Limiting & Throttling
Per-user, per-IP rate limits
Adaptive throttling based on load
Quota management for API usage

Layer 3: Application Security (Input Protection)

Input Validation
Schema validation for all requests
Type checking and range validation
Whitelist-based validation
Data Sanitization
Remove malicious payloads
Encode special characters
Normalize input formats
Injection Detection
SQL injection prevention
Command injection detection
LDAP/NoSQL injection guards
XSS Prevention
Content Security Policy (CSP)
Output encoding
DOM sanitization

Layer 4: Data Security (Information Protection)

Encryption at Rest
AES-256 encryption for databases
Encrypted file systems
Key rotation policies
Encryption in Transit
TLS for all network communication
mTLS for service-to-service
VPN for admin access
PII Masking
Automatic detection of sensitive data
Tokenization for credit cards, SSNs
Masking in logs and exports
Audit Logging
Comprehensive security event logging
Tamper-proof log storage
SIEM integration

Layer 5: Infrastructure Security (Foundation)

Secrets Management
HashiCorp Vault, AWS Secrets Manager
API keys, passwords, certificates
Automatic rotation
Identity & Access Management (IAM)
Principle of least privilege
Service accounts for automation
Regular access reviews
Network Isolation
VPC/VNET segmentation
Private subnets for data tier
Security groups and NACLs
Container Security
Image scanning for vulnerabilities
Runtime protection
Pod security policies

Security Posture:

Layer	Protects Against	Key Controls
Network	DDoS, Man-in-middle	WAF, SSL, DDoS protection
API	Unauthorized access	Authentication, Authorization
Application	Injection, XSS	Input validation, Sanitization
Data	Data breaches	Encryption, Masking, Audit
Infrastructure	Privilege escalation	IAM, Secrets, Isolation

Color Coding by Severity: - Red: Critical (Network perimeter) - Orange: High (Authentication) - Yellow: Medium (Input validation) - Green: Important (Data protection) - Blue: Foundation (Infrastructure)

graph TB
    subgraph "Layer 1: Network Security"
        WAF[Web Application Firewall]
        DDoS[DDoS Protection]
        SSL[SSL/TLS Termination]
    end

    subgraph "Layer 2: API Security"
        AUTHN[Authentication<br/>JWT/OAuth2]
        AUTHZ[Authorization<br/>RBAC/ABAC]
        RATE_LIMIT[Rate Limiting]
        THROTTLE[Throttling]
    end

    subgraph "Layer 3: Application Security"
        INPUT_VAL[Input Validation]
        SANITIZE[Data Sanitization]
        INJECTION_GUARD[Injection Detection]
        XSS_GUARD[XSS Prevention]
    end

    subgraph "Layer 4: Data Security"
        ENCRYPT_REST[Encryption at Rest]
        ENCRYPT_TRANSIT[Encryption in Transit]
        PII_MASK[PII Masking]
        AUDIT[Audit Logging]
    end

    subgraph "Layer 5: Infrastructure Security"
        SECRETS[Secrets Management]
        IAM[Identity & Access Management]
        NETWORK_ISO[Network Isolation]
        CONTAINER_SEC[Container Security]
    end

    REQUEST[Incoming Request] --> WAF
    WAF --> DDoS --> SSL
    SSL --> AUTHN --> AUTHZ
    AUTHZ --> RATE_LIMIT --> THROTTLE
    THROTTLE --> INPUT_VAL --> SANITIZE
    SANITIZE --> INJECTION_GUARD --> XSS_GUARD
    XSS_GUARD --> ENCRYPT_TRANSIT
    ENCRYPT_TRANSIT --> APP[Application Logic]
    APP --> ENCRYPT_REST --> PII_MASK
    PII_MASK --> AUDIT

    SECRETS -.provides.-> APP
    IAM -.controls.-> APP
    NETWORK_ISO -.isolates.-> APP
    CONTAINER_SEC -.protects.-> APP

    style WAF fill:#f44336
    style AUTHN fill:#ff9800
    style INPUT_VAL fill:#ffc107
    style ENCRYPT_REST fill:#4caf50
    style SECRETS fill:#2196f3

Observability¶

Monitoring Stack¶

graph TB
    subgraph "Application Layer"
        APP1[App Instance 1]
        APP2[App Instance 2]
        APP3[App Instance N]
    end

    subgraph "Metrics Collection"
        PROM[Prometheus]
        PUSHGW[Push Gateway]
        EXPORTERS[Exporters<br/>Node, Redis, Postgres]
    end

    subgraph "Logging"
        FLUENTD[Fluentd/Fluentbit]
        ELASTIC[Elasticsearch]
        KIBANA[Kibana]
    end

    subgraph "Tracing"
        JAEGER[Jaeger]
        TEMPO[Grafana Tempo]
    end

    subgraph "Visualization"
        GRAFANA[Grafana Dashboards]
    end

    subgraph "Alerting"
        ALERT_MGR[Alert Manager]
        PAGERDUTY[PagerDuty]
        SLACK[Slack]
        EMAIL[Email]
    end

    APP1 & APP2 & APP3 -->|metrics| PROM
    APP1 & APP2 & APP3 -->|logs| FLUENTD
    APP1 & APP2 & APP3 -->|traces| JAEGER

    EXPORTERS --> PUSHGW --> PROM

    PROM --> GRAFANA
    PROM --> ALERT_MGR

    FLUENTD --> ELASTIC
    ELASTIC --> KIBANA
    ELASTIC --> GRAFANA

    JAEGER --> GRAFANA
    JAEGER --> TEMPO

    ALERT_MGR --> PAGERDUTY & SLACK & EMAIL

    style PROM fill:#e6522c
    style ELASTIC fill:#00bfb3
    style GRAFANA fill:#f46800
    style JAEGER fill:#60d0e4

Best Practices¶

CI/CD Pipeline¶

graph LR
    CODE[Code Push] --> GIT[GitHub]
    GIT --> TRIGGER[Webhook Trigger]

    TRIGGER --> BUILD[Build Stage]
    BUILD --> TEST[Test Stage]
    TEST --> SCAN[Security Scan]
    SCAN --> DOCKER[Docker Build]
    DOCKER --> PUSH[Push to Registry]

    PUSH --> DEV[Deploy to Dev]
    DEV --> INT_TEST[Integration Tests]
    INT_TEST --> STAGING[Deploy to Staging]

    STAGING --> APPROVAL{Manual<br/>Approval}
    APPROVAL -->|Approved| PROD[Deploy to Production]
    APPROVAL -->|Rejected| NOTIFY[Notify Team]

    PROD --> HEALTH[Health Check]
    HEALTH --> SMOKE[Smoke Tests]
    SMOKE --> MONITOR[Monitor Metrics]

    style CODE fill:#4caf50
    style PROD fill:#2196f3
    style APPROVAL fill:#ff9800

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Diagram Types

flowchart/graph: Process flows, system architecture
sequenceDiagram: Interaction flows, API calls
stateDiagram: State machines, lifecycles
classDiagram: Object models, class relationships
gantt: Timelines, project schedules
erDiagram: Database schemas, entity relationships

Cloud Architecture Diagrams¶

Multi-Region Deployment¶

graph TB
    subgraph "Global Load Balancer"
        GLB[CloudFlare / Route 53<br/>Global Traffic Manager]
    end

    subgraph "Region: US-EAST"
        subgraph "Availability Zone 1"
            LB1_AZ1[Load Balancer]
            APP1_AZ1[App Servers]
            CACHE1_AZ1[(Redis Cache)]
        end

        subgraph "Availability Zone 2"
            LB1_AZ2[Load Balancer]
            APP1_AZ2[App Servers]
            CACHE1_AZ2[(Redis Cache)]
        end

        RDS1[(RDS Primary<br/>PostgreSQL)]
        RDS1_READ[(Read Replicas)]
        S3_1[(S3 Bucket<br/>Primary)]
    end

    subgraph "Region: EU-WEST"
        subgraph "Availability Zone 1"
            LB2_AZ1[Load Balancer]
            APP2_AZ1[App Servers]
            CACHE2_AZ1[(Redis Cache)]
        end

        subgraph "Availability Zone 2"
            LB2_AZ2[Load Balancer]
            APP2_AZ2[App Servers]
            CACHE2_AZ2[(Redis Cache)]
        end

        RDS2[(RDS Secondary<br/>PostgreSQL)]
        RDS2_READ[(Read Replicas)]
        S3_2[(S3 Bucket<br/>Replica)]
    end

    subgraph "DR Region: AP-SOUTH"
        DR_APP[DR App Servers]
        DR_DB[(DR Database)]
    end

    GLB --> LB1_AZ1 & LB1_AZ2 & LB2_AZ1 & LB2_AZ2

    LB1_AZ1 --> APP1_AZ1
    LB1_AZ2 --> APP1_AZ2
    LB2_AZ1 --> APP2_AZ1
    LB2_AZ2 --> APP2_AZ2

    APP1_AZ1 & APP1_AZ2 --> CACHE1_AZ1 & CACHE1_AZ2
    APP2_AZ1 & APP2_AZ2 --> CACHE2_AZ1 & CACHE2_AZ2

    APP1_AZ1 & APP1_AZ2 --> RDS1 & RDS1_READ
    APP2_AZ1 & APP2_AZ2 --> RDS2 & RDS2_READ

    RDS1 -.replication.-> RDS2
    S3_1 -.replication.-> S3_2

    RDS1 -.backup.-> DR_DB
    S3_1 -.backup.-> DR_DB

    style GLB fill:#4caf50
    style RDS1 fill:#ff9800
    style RDS2 fill:#ff9800
    style DR_DB fill:#f44336

Container Orchestration (Kubernetes)¶

graph TB
    subgraph "Kubernetes Cluster"
        subgraph "Ingress Layer"
            INGRESS[Nginx Ingress<br/>Controller]
            CERT[Cert Manager<br/>SSL/TLS]
        end

        subgraph "Application Namespace"
            subgraph "Agent Deployment"
                AGENT_POD1[Agent Pod 1<br/>Container + Sidecar]
                AGENT_POD2[Agent Pod 2<br/>Container + Sidecar]
                AGENT_POD3[Agent Pod N<br/>Container + Sidecar]
            end

            subgraph "Task Deployment"
                TASK_POD1[Task Pod 1]
                TASK_POD2[Task Pod 2]
            end

            SVC_AGENT[Agent Service<br/>ClusterIP]
            SVC_TASK[Task Service<br/>ClusterIP]

            HPA_AGENT[Horizontal Pod<br/>Autoscaler]
            HPA_TASK[HPA]
        end

        subgraph "Data Namespace"
            REDIS_STATEFUL[Redis StatefulSet]
            POSTGRES_STATEFUL[PostgreSQL StatefulSet]

            PVC_REDIS[(PVC Redis)]
            PVC_PG[(PVC PostgreSQL)]
        end

        subgraph "Monitoring Namespace"
            PROMETHEUS[Prometheus]
            GRAFANA[Grafana]
            JAEGER[Jaeger]
        end

        subgraph "System Components"
            KUBE_DNS[CoreDNS]
            METRICS[Metrics Server]
        end
    end

    subgraph "External"
        USERS[Users]
        LLM_API[LLM APIs<br/>OpenAI/Anthropic]
    end

    USERS --> INGRESS
    INGRESS --> SVC_AGENT & SVC_TASK

    SVC_AGENT --> AGENT_POD1 & AGENT_POD2 & AGENT_POD3
    SVC_TASK --> TASK_POD1 & TASK_POD2

    HPA_AGENT -.scales.-> AGENT_POD1 & AGENT_POD2 & AGENT_POD3
    HPA_TASK -.scales.-> TASK_POD1 & TASK_POD2

    AGENT_POD1 & AGENT_POD2 & AGENT_POD3 --> REDIS_STATEFUL
    AGENT_POD1 & AGENT_POD2 & AGENT_POD3 --> POSTGRES_STATEFUL

    REDIS_STATEFUL --> PVC_REDIS
    POSTGRES_STATEFUL --> PVC_PG

    AGENT_POD1 & AGENT_POD2 & AGENT_POD3 -.metrics.-> PROMETHEUS
    PROMETHEUS --> GRAFANA

    AGENT_POD1 & AGENT_POD2 & AGENT_POD3 -.traces.-> JAEGER

    AGENT_POD1 & AGENT_POD2 & AGENT_POD3 --> LLM_API

    style INGRESS fill:#4caf50
    style PROMETHEUS fill:#e6522c
    style GRAFANA fill:#f46800
    style LLM_API fill:#ff9800

State Diagrams¶

Agent Lifecycle State Machine¶

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

    Initialized --> Starting: start()
    Starting --> Active: startup_complete
    Starting --> Failed: startup_error

    Active --> Processing: execute_task()
    Processing --> Active: task_complete
    Processing --> Error: task_error

    Active --> Paused: pause()
    Paused --> Active: resume()

    Active --> Stopping: stop()
    Paused --> Stopping: stop()
    Processing --> Stopping: stop(force=True)

    Error --> Active: retry()
    Error --> Stopping: stop()

    Stopping --> Stopped: cleanup_complete
    Stopped --> [*]

    Failed --> [*]

    note right of Active
        Agent ready to
        accept tasks
    end note

    note right of Processing
        Executing task
        Cannot be paused
    end note

    note right of Error
        Recoverable error
        Can retry
    end note

Task Execution State Machine¶

stateDiagram-v2
    [*] --> Created: submit_task()

    Created --> Queued: add_to_queue()
    Queued --> Assigned: assign_to_agent()

    Assigned --> Running: start_execution()
    Running --> Validating: execution_complete

    Validating --> Completed: validation_passed
    Validating --> Failed: validation_failed

    Running --> Paused: pause_request()
    Paused --> Running: resume_request()

    Running --> Cancelled: cancel_request()
    Queued --> Cancelled: cancel_request()
    Assigned --> Cancelled: cancel_request()

    Running --> RetryPending: execution_error
    RetryPending --> Queued: retry_scheduled
    RetryPending --> Failed: max_retries_exceeded

    Completed --> [*]
    Failed --> [*]
    Cancelled --> [*]

    note right of Running
        Task executing
        on assigned agent
    end note

    note right of RetryPending
        Waiting for retry
        attempt
    end note

Sequence Diagrams¶

Complete Agent Task Execution Flow¶

sequenceDiagram
    autonumber

    participant User
    participant API as API Gateway
    participant AM as Agent Manager
    participant Agent
    participant TM as Task Manager
    participant LLM as LLM Service
    participant Memory
    participant Monitor

    User->>API: POST /tasks
    activate API

    API->>API: Authenticate & Authorize
    API->>TM: Create Task
    activate TM

    TM->>TM: Validate Task
    TM->>TM: Enqueue Task
    TM-->>API: Task ID
    deactivate TM

    API-->>User: 202 Accepted {task_id}
    deactivate API

    Note over TM,AM: Task Assignment

    TM->>AM: Request Available Agent
    activate AM

    AM->>AM: Select Agent by Capability
    AM-->>TM: Agent ID
    deactivate AM

    TM->>Agent: Assign Task
    activate Agent

    Agent->>Monitor: Record Start
    activate Monitor
    Monitor-->>Agent: Logged
    deactivate Monitor

    Agent->>Memory: Load Context
    activate Memory
    Memory-->>Agent: Context Data
    deactivate Memory

    Agent->>LLM: Generate Response
    activate LLM

    LLM->>LLM: Process Prompt
    LLM-->>Agent: LLM Response
    deactivate LLM

    Agent->>Agent: Process Response

    Agent->>Memory: Store Result
    activate Memory
    Memory-->>Agent: Stored
    deactivate Memory

    Agent->>Monitor: Record Completion
    activate Monitor
    Monitor->>Monitor: Update Metrics
    Monitor-->>Agent: Logged
    deactivate Monitor

    Agent-->>TM: Task Result
    deactivate Agent

    TM->>TM: Update Task Status

    Note over User,TM: User Polls for Result

    User->>API: GET /tasks/{task_id}
    activate API
    API->>TM: Get Task Status
    activate TM
    TM-->>API: Task Result
    deactivate TM
    API-->>User: 200 OK {result}
    deactivate API

Multi-Agent Collaboration Flow¶

sequenceDiagram
    autonumber

    participant Coord as Coordinator<br/>Agent
    participant A1 as Research<br/>Agent
    participant A2 as Analysis<br/>Agent
    participant A3 as Report<br/>Agent
    participant Hub as Agent Hub
    participant Comm as Communication<br/>Manager

    Coord->>Coord: Decompose Complex Task

    Note over Coord,A1: Phase 1: Research

    Coord->>Comm: Broadcast Task Start
    Comm->>A1: Task: Research Topic
    activate A1

    A1->>Hub: Discover Data Sources
    Hub-->>A1: Source List

    A1->>A1: Collect Data
    A1-->>Comm: Research Complete
    Comm-->>Coord: Data Collected
    deactivate A1

    Note over Coord,A2: Phase 2: Analysis

    Coord->>Comm: Send Data to Analysis
    Comm->>A2: Task: Analyze Data
    activate A2

    A2->>A2: Process Data
    A2->>A2: Generate Insights
    A2-->>Comm: Analysis Complete
    Comm-->>Coord: Insights Ready
    deactivate A2

    Note over Coord,A3: Phase 3: Reporting

    Coord->>Comm: Send Insights to Report
    Comm->>A3: Task: Generate Report
    activate A3

    A3->>A3: Create Report
    A3->>A3: Format Output
    A3-->>Comm: Report Complete
    Comm-->>Coord: Final Report
    deactivate A3

    Coord->>Coord: Aggregate Results
    Coord->>Coord: Return to User

Deployment Timeline¶

CI/CD Pipeline Flow¶

graph LR
    subgraph "Development"
        DEV_CODE[Write Code]
        DEV_TEST[Local Tests]
        DEV_COMMIT[Git Commit]
    end

    subgraph "CI Pipeline"
        CI_TRIGGER[Webhook Trigger]
        CI_BUILD[Build]
        CI_LINT[Lint & Format]
        CI_TEST[Run Tests]
        CI_SECURITY[Security Scan]
        CI_DOCKER[Build Docker]
    end

    subgraph "CD Pipeline"
        CD_PUSH[Push to Registry]
        CD_DEV[Deploy to Dev]
        CD_INT_TEST[Integration Tests]
        CD_STAGE[Deploy to Staging]
        CD_SMOKE[Smoke Tests]
        CD_APPROVAL{Manual<br/>Approval}
        CD_PROD[Deploy to Production]
        CD_MONITOR[Monitor]
    end

    DEV_CODE --> DEV_TEST --> DEV_COMMIT
    DEV_COMMIT --> CI_TRIGGER

    CI_TRIGGER --> CI_BUILD
    CI_BUILD --> CI_LINT
    CI_LINT --> CI_TEST
    CI_TEST --> CI_SECURITY
    CI_SECURITY --> CI_DOCKER

    CI_DOCKER --> CD_PUSH
    CD_PUSH --> CD_DEV
    CD_DEV --> CD_INT_TEST
    CD_INT_TEST --> CD_STAGE
    CD_STAGE --> CD_SMOKE
    CD_SMOKE --> CD_APPROVAL

    CD_APPROVAL -->|Approved| CD_PROD
    CD_APPROVAL -->|Rejected| DEV_CODE

    CD_PROD --> CD_MONITOR
    CD_MONITOR -.feedback.-> DEV_CODE

    style DEV_CODE fill:#4caf50
    style CI_TRIGGER fill:#2196f3
    style CD_APPROVAL fill:#ff9800
    style CD_PROD fill:#f44336

Entity Relationship Diagrams¶

Core Data Model¶

erDiagram
    AGENT ||--o{ TASK : executes
    AGENT ||--o{ MEMORY : owns
    AGENT }o--|| LLM : uses
    AGENT }o--o{ CAPABILITY : has

    TASK ||--o{ TASK_RESULT : produces
    TASK }o--|| WORKFLOW : "part-of"
    TASK ||--o{ TASK_DEPENDENCY : depends-on

    MEMORY }o--|| AGENT : "belongs-to"
    MEMORY ||--o{ MEMORY_ENTRY : contains

    KNOWLEDGE_BASE ||--o{ DOCUMENT : stores
    DOCUMENT ||--o{ EMBEDDING : has

    USER ||--o{ AGENT : creates
    USER ||--o{ TASK : submits

    AGENT {
        string id PK
        string name
        string role
        string status
        jsonb config
        timestamp created_at
        timestamp updated_at
    }

    TASK {
        string id PK
        string agent_id FK
        string name
        string description
        int priority
        string status
        jsonb input
        timestamp created_at
        timestamp updated_at
    }

    TASK_RESULT {
        string id PK
        string task_id FK
        jsonb output
        string status
        int duration_ms
        timestamp completed_at
    }

    MEMORY {
        string id PK
        string agent_id FK
        string key
        jsonb value
        int ttl
        timestamp expires_at
    }

    MEMORY_ENTRY {
        string id PK
        string memory_id FK
        text content
        jsonb metadata
        timestamp created_at
    }

    LLM {
        string id PK
        string provider
        string model
        jsonb config
    }

    CAPABILITY {
        string id PK
        string name
        string description
        jsonb parameters
    }

    WORKFLOW {
        string id PK
        string name
        jsonb definition
        timestamp created_at
    }

    USER {
        string id PK
        string email
        string name
        timestamp created_at
    }

    KNOWLEDGE_BASE {
        string id PK
        string name
        string backend
        jsonb config
    }

    DOCUMENT {
        string id PK
        string kb_id FK
        text content
        jsonb metadata
        timestamp created_at
    }

    EMBEDDING {
        string id PK
        string document_id FK
        vector embedding
        int dimension
    }

Architecture Guide - Complete architectural documentation
Agents Module - Agent design and implementation
Tasks Module - Task management architecture
Memory Module - Memory system design
LLMs Module - LLM integration architecture
Deployment Guide - Production deployment
Best Practices - Development guidelines