Agent Protocol Landscape - The industry is moving towards interoperability through three open protocols for agentic frameworks [1] - These protocols create a universal language for agents, enabling different frameworks to work together [3] Key Protocols - AG-UI (Agent-User Interaction) facilitates bidirectional communication between agent backends and frontends, enabling interactive agent experiences within applications [1][2] - A2A (Agent-to-Agent) is a protocol for multi-agent coordination, task delegation, and intent sharing across systems [3][5] - MCP (Model Context Protocol) is the standard for agents connecting to tools, data, and workflows [5] Interoperability and Integration - Protocols eliminate the need for point-to-point integrations, allowing developers to build to protocols instead [3] - Frameworks like LangGraph, CrewAI, and Agno can be integrated into the same frontend without rewriting UI logic [3] - CopilotKit unifies the entire stack into one framework, simplifying the implementation of these protocols [4] Example Workflow - A LangGraph agent retrieves data via MCP, delegates analysis to a CrewAI agent via A2A, and streams results to a React app via AG-UI [6]

Agent Protocol Landscape - The industry is converging on three open protocols for agent interoperability: AG-UI (Agent-User Interaction), MCP (Model Context Protocol), and A2A (Agent-to-Agent) [1][2] - These protocols are complementary layers of a stack, not competing standards, facilitating a universal language for agents [2] - Protocols enable integration of frameworks like LangGraph, CrewAI, and Agno into the same frontend without rewriting UI logic [3] Protocol Functionality - AG-UI enables bidirectional connection between agentic backends and frontends, creating interactive agents within applications [1][2] - MCP standardizes how agents connect to tools, data, and workflows [2] - A2A facilitates multi-agent coordination, enabling task delegation and intent sharing across systems [2][5] Framework Integration - CopilotKit unifies the entire protocol stack into one framework, providing generative UI support and production-ready infrastructure [3][4] - An example workflow involves a LangGraph agent pulling data via MCP, delegating analysis to a CrewAI agent via A2A, and streaming results to a React app via AG-UI [6] Development Focus - Protocols allow developers to focus on building agent capabilities instead of integration mechanics, as interoperability is handled automatically [3]

Group 1 - The rise of AI agents is significantly impacting business operations, human-machine collaboration, and national security, necessitating a focus on their safety, interpretability, and reliability [1][2] - 2023 is recognized as the year of generative AI, with 2024 moving towards practical applications of AI, and 2025 being termed the year of AI agents, which are autonomous systems designed to perform specific tasks with minimal human intervention [2] - AI agents are expected to have substantial economic and geopolitical implications, especially when integrated into critical workflows in sensitive sectors like finance, healthcare, and defense [2] Group 2 - AI agent systems typically operate on top of large language models (LLMs) and consist of four foundational components: perception, reasoning, action, and memory [3] - The architecture of AI agents includes a supporting infrastructure stack for model access, memory storage, task coordination, and external tool integration, with multi-agent systems allowing for collaboration among agents [3][6] - The emergence of general-purpose AI systems that can flexibly apply across different environments and industries is accelerating, with ongoing efforts to establish cybersecurity, interoperability, and governance standards [6] Group 3 - AI agents enhance cybersecurity by autonomously assisting network personnel in critical tasks such as continuous monitoring, vulnerability management, threat detection, incident response, and decision-making [7] - Continuous monitoring and vulnerability management are improved through AI agents that automatically identify vulnerabilities and prioritize fixes based on business impact, significantly enhancing efficiency [8] - Real-time threat detection and intelligent response capabilities are achieved through multi-agent collaboration, reducing average response times by over 60% [9] - AI agents help address the global cybersecurity talent shortage by automating over 70% of alert false positives, saving security analysts significant time and improving overall operational efficiency [10] Group 4 - The architecture of AI agents is divided into four main layers: perception, reasoning, action, and memory, each with distinct security considerations and risks [11] - The perception module faces risks such as adversarial data injection, which can compromise data integrity and confidentiality [13] - The reasoning module is vulnerable to exploitation of underlying model flaws, which can lead to incorrect decision-making and erode trust in AI agents [14] - The action module is sensitive to attacks that exploit the agent's ability to interact with external systems, necessitating strict output validation and access control [15] - The memory module is crucial for maintaining context and can be targeted for memory tampering, which may distort the agent's understanding and future actions [16] Group 5 - The rise of AI agents signifies a transformative shift in how emerging technologies interact with and influence the digital world, marking a breakthrough from passive human-supervised models to autonomous systems capable of reasoning and learning from experience [18]

RT Avi Chawla (@_avichawla)I just built my own multi-agent deep researcher!It uses a 100% local LLM and MCP.Here's an overview of how it works:- User submits a query- Web agent searches with Bright Data MCP tool- Research agents generate insights using platform-specific tools- Response agent crafts a coherent answer with citationsTech stack:- Bright Data MCP for real-time web access- CrewAI for multi-agent orchestration- Ollama to locally serve GPT-OSSWhy Bright Data MCP?To build this workflow, we needed to ...

Technology Stack - The multi-agent deep researcher utilizes a 100% local LLM and MCP [1] - The system employs CrewAI for multi-agent orchestration and Ollama to locally serve GPT-OSS [2] Web Access Solution - Bright Data Web MCP is used to gather information from several sources, addressing issues like IP blocks and CAPTCHA blocks [1] - Bright Data MCP offers platform-specific tools compatible with major agent frameworks [2] - Bright Data MCP provides real-time web access [2] Workflow - The workflow involves a user submitting a query, followed by a web agent searching with the Bright Data MCP tool [2] - Research agents generate insights using platform-specific tools, and a response agent crafts a coherent answer with citations [2]

Core Viewpoint - The article discusses the evolution and differentiation between AI Agents and Agentic AI, highlighting their respective roles in automating tasks and collaborating on complex objectives, with a focus on the advancements since the introduction of ChatGPT in November 2022 [2][10][57]. Group 1: Evolution of AI Technology - The development of AI technology has progressed from early expert systems like MYCIN to modern AI Agents and Agentic AI, marking a significant paradigm shift in capabilities [10][11]. - ChatGPT's release in November 2022 is identified as a pivotal moment that catalyzed the evolution of AI Agents, transitioning from passive responders to more autonomous systems capable of executing multi-step tasks [12][24]. - The introduction of frameworks like AutoGPT and BabyAGI in 2023 signifies the formal establishment of AI Agents, which integrate LLMs with external tools to perform complex tasks [12][24]. Group 2: Characteristics of AI Agents - AI Agents are defined as modular systems driven by LLMs and LIMs, designed for task automation, filling the gap where generative AI lacks execution capabilities [13][16]. - Three core features distinguish AI Agents from traditional automation scripts: autonomy, task-specificity, and reactivity [16][17]. - The integration of tools allows AI Agents to overcome limitations of static knowledge and hallucination issues, enabling them to perform real-time data retrieval and processing [19][20]. Group 3: Agentic AI and Multi-Agent Collaboration - Agentic AI represents a shift towards multi-agent collaboration, where multiple AI Agents work together to achieve complex goals, enhancing system-level intelligence [24][27]. - The architecture of Agentic AI includes dynamic task decomposition and shared memory, facilitating efficient collaboration among specialized agents [33][36]. - Real-world applications of Agentic AI demonstrate its advantages in various fields, such as healthcare and agriculture, where multiple agents coordinate to optimize processes [37][38]. Group 4: Challenges and Future Directions - Both AI Agents and Agentic AI face challenges, including causal reasoning deficits and coordination issues among multiple agents [48][50]. - Proposed solutions include enhancing retrieval-augmented generation (RAG), implementing causal modeling, and establishing shared memory architectures to improve collaboration and decision-making [49][53]. - The future roadmap emphasizes the need for deeper causal reasoning, transparency in decision-making, and ethical governance to ensure the responsible deployment of AI technologies [56][59].

Core Viewpoint - The article discusses the evolution and differentiation between AI Agents and Agentic AI, highlighting their respective roles in automating tasks and collaborating on complex objectives, with a focus on the advancements since the introduction of ChatGPT in November 2022 [2][10][57]. Group 1: Evolution of AI Technology - The emergence of ChatGPT in November 2022 marked a pivotal moment in AI development, leading to increased interest in AI Agents and Agentic AI [2][4]. - The historical context of AI Agents dates back to the 1970s with systems like MYCIN and DENDRAL, which were limited to rule-based operations without learning capabilities [10][11]. - The transition to AI Agents occurred with the introduction of frameworks like AutoGPT and BabyAGI in 2023, enabling these agents to autonomously complete multi-step tasks by integrating LLMs with external tools [12][13]. Group 2: Definition and Characteristics of AI Agents - AI Agents are defined as modular systems driven by LLMs and LIMs for task automation, addressing the limitations of traditional automation scripts [13][16]. - Three core features distinguish AI Agents: autonomy, task specificity, and reactivity [16][17]. - The dual-engine capability of LLMs and LIMs is essential for AI Agents, allowing them to operate independently and adapt to dynamic environments [17][21]. Group 3: Transition to Agentic AI - Agentic AI represents a shift from individual AI Agents to collaborative systems that can tackle complex tasks through multi-agent cooperation [24][27]. - The key difference between AI Agents and Agentic AI lies in the introduction of system-level intelligence, enabling broader autonomy and the management of multi-step tasks [27][29]. - Agentic AI systems utilize a coordination layer and shared memory to enhance collaboration and task management among multiple agents [33][36]. Group 4: Applications and Use Cases - The article outlines various applications of Agentic AI, including automated fund application writing, collaborative agricultural harvesting, and clinical decision support in healthcare [37][43]. - In these scenarios, Agentic AI systems demonstrate their ability to manage complex tasks efficiently through specialized agents working in unison [38][43]. Group 5: Challenges and Future Directions - The article identifies key challenges facing AI Agents and Agentic AI, including causal reasoning deficits, coordination bottlenecks, and the need for improved interpretability [48][50]. - Proposed solutions include enhancing retrieval-augmented generation (RAG), implementing causal modeling, and establishing governance frameworks to address ethical concerns [52][53]. - Future development paths for AI Agents and Agentic AI focus on scaling multi-agent collaboration, domain customization, and evolving into human collaborative partners [56][59].

Product Overview - xpander is a production-ready backend solution for AI Agents, managing memory, tools, states, version control, and guardrails [1] - The solution is designed to be plug-and-play and fully self-hostable [1] - It is compatible with various frameworks such as CrewAI, Agno, and Langchain [1] Technology and Implementation - xpander addresses the need for a functional backend in the development and deployment of AI Agents [1]

Core Viewpoint - The article discusses various mainstream AI Agent frameworks, highlighting their unique features and suitable application scenarios, emphasizing the growing importance of AI in automating complex tasks and enhancing collaboration among agents [1]. Group 1: Mainstream AI Agent Frameworks - Current mainstream AI Agent frameworks are diverse, each focusing on different aspects and applicable to various scenarios [1]. - The frameworks discussed include LangGraph, AutoGen, CrewAI, Smolagents, and RagFlow, each with distinct characteristics and use cases [1][2]. Group 2: CrewAI - CrewAI is an open-source multi-agent coordination framework that allows autonomous AI agents to collaborate as a cohesive team to complete tasks [3]. - Key features of CrewAI include: - Independent architecture, fully self-developed without reliance on existing frameworks [4]. - High-performance design focusing on speed and resource efficiency [4]. - Deep customizability, supporting both macro workflows and micro behaviors [4]. - Applicability across various scenarios, from simple tasks to complex enterprise automation needs [4][7]. Group 3: LangGraph - LangGraph, created by LangChain, is an open-source AI agent framework designed for building, deploying, and managing complex generative AI agent workflows [26]. - It utilizes a graph-based architecture to model and manage the complex relationships between components in AI workflows [28]. Group 4: AutoGen - AutoGen is an open-source framework from Microsoft for building agents that collaborate through dialogue to complete tasks [44]. - It simplifies AI development and research, supporting various large language models (LLMs) and advanced multi-agent design patterns [46]. - Core features include: - Support for agent-to-agent dialogue and human-machine collaboration [49]. - A unified interface for standardizing interactions [49][50]. Group 5: Smolagents - Smolagents is an open-source Python library from Hugging Face aimed at simplifying the development and execution of agents with minimal code [67]. - It supports various functionalities, including code execution and tool invocation, while being model-agnostic and easily extensible [70]. Group 6: RagFlow - RagFlow is an end-to-end RAG solution focused on deep document understanding, addressing challenges in data processing and answer generation [75]. - It supports various document formats and intelligently identifies document structures to ensure high-quality data input [77][78]. Group 7: Summary of Frameworks - Each AI Agent framework has unique characteristics and suitable application scenarios: - CrewAI is ideal for multi-agent collaboration and complex task automation [80]. - LangGraph is suited for state-driven multi-step task orchestration [81]. - AutoGen is designed for dynamic dialogue processes and research tasks [86]. - Smolagents is best for lightweight development and rapid prototyping [86]. - RagFlow excels in document parsing and multi-modal data processing [86].

RT Avi Chawla (@_avichawla)After MCP, A2A, & AG-UI, there's another Agent protocol (open-source).ACP (Agent Communication Protocol) is a standardized, RESTful interface for Agents to discover and coordinate with other Agents, regardless of their framework (CrewAI, LangChain, etc.).Here's how it works:- Build your Agents and host them on ACP servers.- The ACP server will receive requests from the ACP Client and forward them to the Agent.- ACP Client itself can be an Agent to intelligently route requests to t ...