AI Agent Architecture
A technical deep dive into the architecture that powers NAYAFlOW's AI agent orchestration platform.
Platform Architecture Overview
NAYAFlOW's architecture is built on a modular, scalable foundation that enables seamless orchestration of AI agents across complex workflows and enterprise systems.
Microservices Architecture
Distributed, containerized services provide flexibility and scalability for enterprise deployment across cloud and on-premises environments.
Extensible Agent Framework
Supports multiple agent architectures (LangGraph, CrewAI, AutoGen) with a unified interface for consistent development and deployment.
Enterprise Integration
Pre-built connectors to common enterprise systems (CRM, ERP, databases) with a secure API gateway for custom integrations.
Figure 1: High-level architecture of the NAYAFlOW platform
Agent Orchestration Patterns
NAYAFlOW supports multiple orchestration patterns that can be implemented across different agent frameworks.
ReAct Pattern
Combines reasoning and action in a synergistic loop, allowing agents to reason about their observations before taking the next action.
Implementation Details:
- •Thought: Internal reasoning about current state
- •Action: Execution based on reasoning
- •Observation: Environment feedback
- •Iteration: Continuous improvement cycle
Tool-Augmented Pattern
Extends agent capabilities through integration with external tools, APIs, and data sources, enabling real-world interactions.
Implementation Details:
- •Tool Selection: Dynamic choosing of appropriate tools
- •Tool Invocation: Properly formatted API calls
- •Result Integration: Processing tool responses
- •Tool Library: Expandable ecosystem of capabilities
Multi-Agent Collaboration
Enables multiple specialized agents to work together on complex tasks, with structured communication and role-based responsibility allocation.
Implementation Details:
- •Role Definition: Specialized agent capabilities
- •Communication Protocol: Structured message passing
- •Task Allocation: Dynamic work distribution
- •Conflict Resolution: Mechanisms for handling disagreements
Autonomous Agent Pattern
Self-driven agent architecture that maintains its own goals, memory, and planning capabilities without continuous human intervention.
Implementation Details:
- •Goal Management: Setting and refining objectives
- •Memory System: Maintaining relevant context
- •Planning Module: Creating execution strategies
- •Self-Reflection: Evaluating progress and adapting
Technical Implementation Details
LangGraph
State-of-the-art framework for building stateful, multi-agent applications with LLMs using a graph-based approach.
Key capabilities:
- Stateful graph execution
- Human-in-the-loop interactions
- Persistent memory management
- Advanced error handling
CrewAI
Framework for orchestrating role-based autonomous AI agents, designed for collaborative tasks with minimal code.
Key capabilities:
- Role-based agent design
- Pre-built agent templates
- Collaborative task execution
- Simplified agent communication
AutoGen
Open-source framework for building conversational AI systems with multiple agents that can work together to solve complex tasks.
Key capabilities:
- Customizable conversation flows
- Multi-agent conversations
- Human-in-the-loop integration
- Tool use and function calling
