
1. Key Features
1.1. Addresses more complex, multi-domain tasks with
1.1.1. Enhanced precision
1.1.2. Contextual understanding
1.2. Incorporates autonoumous agents
1.2.1. Dynamic decision-making
1.2.2. Iterative reasoning
1.2.3. adaptive retrieval strategies
1.3. Reduces latency through
1.3.1. optimized workflows
1.3.2. iteratively refines output
2. Architectural Frameworks
2.1. Single-Agent Agentic Router : Router
2.1.1. Workflow
2.1.1.1. 1. Query Submission and Evaluation
2.1.1.1.1. Coordinating agent receives the query and analyzes it to determine most suitable sources of information
2.1.1.2. 2. Knowledge Source Selection
2.1.1.2.1. Coordinating agent chooses from a variety of retrieval options
2.1.1.3. 3. Data Integration and LLM Synthesis
2.1.1.3.1. Once the relevant data is retrieved its passed to the LLM
2.1.1.3.2. LLM synthesizes the gathered information, integrating insights from multiple sources into a coherent and contextually relevant response
2.1.1.4. 4. Output Generation
2.1.1.4.1. System delivers a comprehensive user-facing answer to the user's query
2.1.1.4.2. the response is presented in an actionable, concise format, may optionally includes references or citations to the sources used
2.1.2. Key Features and Advantages
2.1.2.1. centralized simplicity
2.1.2.1.1. single agent handles all retrieval and routing tasks
2.1.2.1.2. making the architecture straightforward to design, implement and maintain
2.1.2.2. efficiency & resource optimization
2.1.2.2.1. with fewer agents and simpler coordination, the system demands fewer computational resources and can handle queries more quickly
2.1.2.3. dynamic routing
2.1.2.3.1. agent evaluates each query in real-time
2.1.2.3.2. selects the most appropriate knowledge source
2.1.2.4. versatility across tools
2.1.2.4.1. supports variety of data sources an d external APIs
2.1.2.5. ideal for simpler systems
2.1.2.5.1. suited for applications with
2.1.3. Centralized decision-making system, where single agent manages
2.1.3.1. retrieval
2.1.3.2. routing
2.1.3.3. integration of information
2.1.4. Simplified architecture by consolidating tasks into one unified agent
2.1.5. Overview Diagram
2.1.5.1. Overview of Single Agentic RAG
2.2. Multi-Agent Agent RAG Systems
2.2.1. Workflow
2.2.1.1. 1. Query submission
2.2.1.1.1. query received by a coordinator agent or master retrieval agent
2.2.1.2. 2. specialied retrieval agents
2.2.1.2.1. Query is distributed among multiple retrieval agents each focusing on specific type of data source or task, examples
2.2.1.3. 3. tools access and data retrieval
2.2.1.3.1. each agent routes the query to the appropriate tools or data sources within its domain
2.2.1.3.2. the retrieval process is executed in parallel, allowing for efficient processing of diverse query types
2.2.1.4. 4. data integration and LLM synthesis
2.2.1.4.1. Once retrieval is complete, data from all agents is passed to a LLM.
2.2.1.5. 5. Output Generation
2.2.1.5.1. system generates a comprehensive response, which is delivered back to the user in an actionable and concise format
2.2.2. Key Features and Advantages
2.2.2.1. Modularity
2.2.2.1.1. each agent operates independently, allowing seamless additiona or removal of agents based on system requirements
2.2.2.2. Scalability
2.2.2.2.1. Parallel processing by multiple agents enables the system to handle high query volumes efficiently
2.2.2.3. Task Specialization
2.2.2.3.1. each agent is optimized for a specific type of query or data source, improving accuracy and retrieval relevance
2.2.2.4. Efficiency
2.2.2.4.1. by distributing tasks across specialized agents, the system minimizes bottlenecks and enhances performance for complex workflows
2.2.2.5. Versatility
2.2.2.5.1. suitable for applications spanning multiple domain, including research, analytics, decision-making and customer support
2.2.3. designed to handle complex workflows and diverse query types by leveraging mutiple specialized agents
2.2.4. distributes responsibilities across multiple agents, each optimized for a specific role or a data source
2.2.4.1. reasoning
2.2.4.2. retrieval
2.2.4.3. response generation
2.2.5. Overview Diagram
2.2.5.1. Overview of Multi-Agent Agentic RAG Systems
2.3. Agenctic Document Workflows
2.3.1. enables end-to-end knowledge work automation
2.3.2. orchestrates complex document-centric processes, integrating
2.3.2.1. document parsing
2.3.2.2. retrieval
2.3.2.3. reasoning
2.3.2.4. structured outputs with intelligent agents
2.3.3. ADW Addresses limitations of IDP by
2.3.3.1. maintaining state
2.3.3.2. coordinating multi-step workflows
2.3.3.3. applying domain-specific logic to documents
2.3.4. Workflow
2.3.4.1. document parsing and information structuring
2.3.4.1.1. documents are parsed to extract relevant data fields
2.3.4.1.2. structured data is organized for downstream processing
2.3.4.2. state maintenance across processes
2.3.4.2.1. maintains state about document context, ensuring consistency and relevance across multi-step workflows
2.3.4.2.2. tracks the progression of the document through various processing steps
2.3.4.3. knowledge retrieval
2.3.4.3.1. relevant references are retrieved from external knowledge bases or vector indexes
2.3.4.3.2. retrieves real-time, domain-specific guidelines for enhanced decision-making
2.3.4.4. agentic orchestration
2.3.4.4.1. intelligent agents apply business rules
2.3.4.4.2. perform multi-hop reasoning
2.3.4.4.3. generate actionable recommendations
2.3.4.4.4. orchestrates components such as parsers, retrievers, and external APIs for seamless integration
2.3.4.5. actionable output generation
2.3.4.5.1. outputs are presented in structured formats, tailored to specific use cases
2.3.4.5.2. recommendations and extracted insights are synthesized into concise and actionable reports
2.3.5. Overview diagram
2.3.5.1. Overview of Agentic Document Workflow (ADW)
2.3.6. Key features and advantages
2.3.6.1. state maintenance
2.3.6.1.1. tracks document context and workflow stage, ensuring consistency across processes.
2.3.6.2. multi-step orchestration
2.3.6.2.1. handles complex workflows involving multiple components and external tools
2.3.6.3. domain-specific intelligence
2.3.6.3.1. applies tailored business rules and guidelines for precise recommendations
2.3.6.4. scalability
2.3.6.4.1. Supports large-scale document processing with modular and dynamic agent integration
2.3.6.5. enhanced productivity
2.3.6.5.1. automates repetitive tasks while augmenting human expertise in decision-making
2.4. Comparison
2.4.1. 1. Focus
2.4.1.1. Agentic RAG: Multi-agent collaboration and reasoning
2.4.1.2. Traditional RAG: Isolated retrieval and generation tasks
2.4.1.3. Agentic Document Workflows: Document-centric end-to-end workflows
2.4.2. 2. Context Maintenance
2.4.2.1. Agentic Document Workflows: Maintains state across multi-step workflows
2.4.2.2. Traditional RAG: Limited
2.4.2.3. Agentic RAG: Enabled through memory modules
2.4.3. 3. Dynamic Adaptability
2.4.3.1. Traditional RAG: Minimal
2.4.3.2. Agentic RAG: High
2.4.3.3. Agentic Document Workflows: Tailored to document workflows
2.4.4. 4. Workflow Orchestration
2.4.4.1. Traditional RAG: Absent
2.4.4.2. Agentic RAG: Orchestrates multi-agent tasks
2.4.4.3. Agentic Document Workflows: Integrates multi-step document processing
2.4.5. 5. Use of External Tools/APIs
2.4.5.1. Traditional RAG: Basic integration (e.g., retrieval tools)
2.4.5.2. Agentic RAG: Extends via tools like APIs and knowledge bases
2.4.5.3. Agentic Document Workflows: Deeply integrates business rules and domain-specific tools
2.4.6. 6. Scalability
2.4.6.1. Traditional RAG: Limited to small datasets or queries
2.4.6.2. Agentic RAG: Scalable for multi-agent systems
2.4.6.3. Agentic Document Workflows: Scales for multi-domain enterprise workflows
2.4.7. 7. Complex Reasoning
2.4.7.1. Traditional RAG: Basic (e.g., simple Q&A)
2.4.7.2. Agentic RAG: Multi-step reasoning with agents
2.4.7.3. Agentic Document Workflows: Structured reasoning across documents
2.4.8. 8. Primary Applications
2.4.8.1. Traditional RAG: QA systems, knowledge retrieval
2.4.8.2. Agentic RAG: Multi-domain knowledge and reasoning
2.4.8.3. Agentic Document Workflows: Contract review, invoice processing, claims analysis
2.4.9. 9. Strengths
2.4.9.1. Traditional RAG: Simplicity, quick setup
2.4.9.2. Agentic RAG: High accuracy, collaborative reasoning
2.4.9.3. Agentic Document Workflows: End-to-end automation, domain-specific intelligence
2.4.10. 10. Challenges
2.4.10.1. Traditional RAG: Poor contextual understanding
2.4.10.2. Agentic RAG: Coordination complexity
2.4.10.3. Agentic Document Workflows: Resource overhead, domain standardization
3. Core Components of an AI Agent
3.1. LLM (with defined Role and Task)
3.1.1. agent's primary reasoning engine
3.1.2. agent's dialogue interface
3.1.3. interprets user queries
3.1.4. generates response
3.1.5. maintains coherence
3.2. Memory (Short-Term and Long-Term)
3.2.1. Short Term
3.2.1.1. tracks immediate conversation state
3.2.2. Long Term
3.2.2.1. Stores accumulated knowledge and agent experiences
3.3. Planning (Reflection and Self-Critique)
3.3.1. Guides agents iterative reasoning process using
3.3.1.1. Reflection
3.3.1.2. Query Routing
3.3.1.3. Self-critique
3.3.1.4. Ensures complex task are broken down effectively
3.4. Tools (Vector Search, APIs, Web Search, etc.)
3.4.1. Expands agents capabilities beyond text generation
3.4.2. enables access to
3.4.2.1. external resources
3.4.2.2. real-time data
3.4.2.3. specialized computations
4. Architecture Diagram
4.1. image
5. Agentic Patterns
5.1. Reflection
5.1.1. Foundational design pattern
5.1.2. Allows agents to iteratively evaluate and refine their outputs
5.1.3. By incorporating self-feedback mechanism agents can
5.1.3.1. identify and address
5.1.3.1.1. errors
5.1.3.1.2. inconsistencies
5.1.3.1.3. areas for improvement
5.1.3.1.4. enhancing performance across tasks like
5.1.4. Involves prompting agent to critique its output
5.1.4.1. correctness
5.1.4.2. style
5.1.4.3. efficiency
5.1.4.4. then incorporating this feedback into subsequent iterations
5.1.5. In multi-agent systems reflection can involve agents with distinct roles
5.1.5.1. one agent generates output
5.1.5.2. second agent critiques them
5.1.6. Overview Diagram
5.1.6.1. Overview of Agentic Self-Reflection
5.2. Planning
5.2.1. enables agents to autonomously decompose complex tasks into smaller manageable substasks, essential for
5.2.1.1. multi-hop reasoning
5.2.1.2. iterative problem-solving in dynamic and uncertain scenarios
5.2.2. Overview Diagram
5.2.2.1. Overview of Agentic Planning
5.2.3. leveraging this agents can dynamically determine sequence of steps to accomplish a larger objective
5.2.4. this adaptability allows agents to handle tasks that cannot be predefined, ensuring flexibility in decision-making
5.2.5. can produce less predicatable outcomes compared to deterministic workflow like Reflection
5.2.6. suited for tasks requiring dynamic adaption, where predefined worklfows are insufficient
5.3. Tools Use
5.3.1. enables agents to extend their capabilities by interacting with
5.3.1.1. external tools
5.3.1.2. APIs
5.3.1.3. computational resources
5.3.2. Overview Diagram
5.3.2.1. Overview of Tool Use
5.3.3. allows agents to
5.3.3.1. gather information
5.3.3.2. perform computations
5.3.3.3. manipulate data beyond their pre-trained knowledge
5.3.4. by dynamically integrating tools into workflows, agents
5.3.4.1. can adapt to complex tasks
5.3.4.2. to provide more
5.3.4.2.1. accurate output
5.3.4.2.2. contextually relevant output
5.3.5. challenges
5.3.5.1. optimizing selection of tools, particularly in contexts with a large number of available options
5.4. Multi-Agent
5.4.1. enables
5.4.1.1. task specialization
5.4.1.2. parallel processing
5.4.2. agents communicate and share intermediate results, ensuring overall workflow remains efficient and coherent
5.4.3. improves scalability and adaptability of complex workflows by distributing subtasks among specialized agents
5.4.4. allows developers to decompose intricate tasks into smaller, manageable subtasks assigned to different agents
5.4.5. enhances task performance
5.4.6. provides a robust framework for managing complex interactions
5.4.7. each agent operates with its own memory and workflow, which can include the use of tools, reflection, or planning, enabling dynamic and collaborative problem-solving
5.4.8. Overview Diagram
5.4.8.1. Overview of MultiAgent
5.4.9. challenges
5.4.9.1. less predictable compared to more mature workflows, like Reflection and Tool Use
5.4.10. emerging frameworks
5.4.10.1. AutoGen
5.4.10.2. Crew AI
5.4.10.3. LangGraph