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 ...

Context Engineering Overview - Context engineering is defined as the art and science of filling the context window with the right information at each step of an agent's trajectory [2][4] - The industry categorizes context engineering strategies into writing context, selecting context, compressing context, and isolating context [2][12] - Context engineering is critical for building agents because they typically handle longer contexts [10] Context Writing and Selection - Writing context involves saving information outside the context window, such as using scratch pads for note-taking or memory for retaining information across sessions [13][16][17] - Selecting context means pulling relevant context into the context window, including instructions, facts, and tools [12][19][20] - Retrieval-augmented generation (RAG) is used to augment the knowledge base of LLMs, with code agents being a large-scale application [27] Context Compression and Isolation - Compressing context involves retaining only the most relevant tokens, often through summarization or trimming [12][30] - Isolating context involves splitting up context to help an agent perform a task, with multi-agent systems being a primary example [12][35] - Sandboxing can isolate token-heavy objects from the LLM context window [39] Langraph Support for Context Engineering - Langraph, a low-level orchestration framework, supports context engineering through features like state objects for scratchpads and built-in long-term memory [44][45][48] - Langraph facilitates context selection from state or long-term memory and offers utilities for summarizing and trimming message history [50][53] - Langraph supports context isolation through multi-agent implementations and integration with sandboxes [55][56]

Agent Communication Protocol (ACP) Overview - ACP (Agent Communication Protocol) is introduced as a new open-source Agent protocol [1] - The protocol facilitates Agent discovery and coordination, irrespective of their underlying framework (e g CrewAI, LangChain) [1] - ACP utilizes a standardized, RESTful interface [1] Resource and Contact Information - Avi Chawla (@_avichawla) shares tutorials and insights on DS, ML, LLMs, and RAGs [1] - A link is provided for further details on how ACP works (https://t co/q6xFvQKYgw) [1]

Agent & MCP Server Development - Cloudflare and Work OS are collaborating to promote the idea that agents acting on behalf of users need the same credentials and authorization as user-facing projects [1] - The industry is moving towards more fine-grained authorization for AI agents, potentially authorizing per-line changes, per-tool changes, or even network connections [20] - Cloudflare offers a free tier for Durable Objects, which can be used for persistent storage in agents [3] Cloudflare's Offerings - Cloudflare provides compute cloud workers, AI model hosting, vectorized inference, vector database, SQL database, durable objects, video streaming, and image optimization [2] - Cloudflare workers have bindings that allow interaction with other Cloudflare products and other companies' products [3] - Cloudflare's agents framework includes an OAuth framework for setting up authorization, enabling easy identification of the worker or agent acting on behalf of a user [5] MCP Server Demo & Use Case - A basic MCP server was built using Cloudflare and Work OS, which is available for users to check out and run [6] - The demo showcases ordering a shirt via an agent, demonstrating how agents can act on behalf of users with proper authorization [9][10][11] - The demo uses Cloudflare's key-value storage to save order data, accessible through the interface [12] - Durable Objects can store data directly on the context associated with a worker object, unique for each user [14][16] Security & Authorization - The industry emphasizes the importance of audit trails with OAuth tools to track agent interactions, including reasons for interaction, the user on whose behalf it acted, and the outcome [21] - The industry needs to consider users as deputies who have access to tools and can potentially misuse them [21]

AI Platform & Business Model - Poe平台提供用户通过订阅访问多种语言模型和代理的能力 [1] - Poe的Bot创建者每年收入数百万美元 (millions) [1] - 推理模型正在推动增长 [1] Consumer AI Usage - 揭示了消费者在使用AI方面的惊人模式 [1] AI Development Challenges - 在快速变化的AI领域中构建产品面临独特的挑战 [1] - 规划周期已从数年缩短至仅两个月 [1]

Generative AI and Agent Technology - Amazon Web Services (AWS) specializes in generative AI, evolving from machine learning [1] - The presentation focuses on deploying generative AI agents to cloud scale, targeting both developers and leaders [1] - The core components of an agent include a model for natural language understanding, a prompt defining the agent's role, an agentic loop for processing input and using tools, history for maintaining context, and tools for external interaction [1][2] - AWS Bedrock offers a suite of capabilities for building generative AI components, including models from Anthropic, Meta, and Mistral [2] - Amazon Bedrock Agents is a fully managed service for deploying agents without infrastructure management [2] Practical Implementation and Tools - The demonstration uses a simple Python agent with a dice rolling tool, initially running locally on a laptop with the Llama 3 8 billion parameter model [1] - The agent is configured with instructions (similar to a prompt) and action groups, which connect to tools [2] - Lambda functions are used to host the tools, enabling them to perform various actions, including interacting with other AWS services [2] - The AWS console provides a user interface for creating and configuring agents, including defining parameters and descriptions for tools [3][4][5][6][7][8][9][10][11][12][13][14][15] - Amazon Q developer is integrated into the console's code editor, offering code suggestions [17][18][19][20][21] Deployment and Scalability - The presentation emphasizes deploying agents to a production-ready, cloud-scale environment [1] - Infrastructure as code frameworks like Terraform, Palumi, and CloudFormation can be used for deployment [3] - AWS offers free courses on deeplearning.ai with AWS environments for experimenting with Amazon Bedrock Agents [25]

Graph RAG Applications & Performance - Graph RAG is a key enabler for building effective AI applications, especially when paired with agents [1] - Graph RAG excels at semantic understanding and can perform global queries over a code repository [2][3] - Graph RAG can be used for code translation from Python to Rust, outperforming direct LLM translation [4][9] - Graph RAG can be applied to large codebases like Doom (100,000 lines of code, 231 files) for documentation and feature development [10][12][13] - Graph RAG, when combined with GitHub Copilot coding agent, enables complex multi-file modifications, such as adding jump capability to Doom [18][20] Benchmark QED & Lazy Graph - Benchmark QED is a new open-source tool for measuring and evaluating Graph RAG systems, focusing on local and global quality metrics [21][22] - Benchmark QED includes AutoQ (query generation), AutoE (evaluation using LLM as a judge), and AutoD (dataset summarization and sampling) [22] - Lazy Graph RAG demonstrates dominant performance against vector RAG on data local questions, winning 92%, 90%, and 91% of the time against 8K, 120K, and 1 million token context windows respectively [29][30] - Lazy Graph RAG can achieve performance at a tenth of the cost compared to using a 1 million token context window [32] - Lazy Graph RAG is being incorporated into Azure AI and Microsoft Discovery Platform [34]

Knowledge Graph Architecture & Agentic Workflows - Knowledge graphs can enhance agentic workflows by enabling reasoning and question decomposition, moving beyond simple vector searches [4] - Knowledge graphs facilitate the expression of simple data models to agents, aiding in accurate information retrieval and expansion with more data [5] - The integration of knowledge graphs allows for more precise question answering through a more expressive data model [22] Data Modeling & Entity Extraction - Data modeling should focus on defining key entities and their relationships, such as people, skills, and activities [17] - Entity extraction from unstructured documents, like resumes, can be used to create a graph database representing these relationships [18] - Pydantic classes and Langchain can be used for entity extraction workflows to decompose documents and extract JSON data containing skills and accomplishments [19][20] Benefits of Graph Databases - Graph databases enable flexible queries and high performance for complex traversals across skills, systems, domains, and accomplishments [30] - Graph databases allow for easy addition of new data and relationships, which is crucial for rapid iteration and adaptation in agentic systems [37] - Graph databases facilitate the creation of tools to find collaborators based on shared projects and domains [39] Practical Application: Employee Skills Analysis - The presentation uses an employee graph example to demonstrate skills analysis, similarity searches, and identification of skill gaps [5] - Initial attempts to answer questions using only document embeddings are inaccurate, highlighting the need for entity extraction and metadata [9] - By leveraging a knowledge graph, the system can accurately answer questions about the number of developers with specific skills, such as Python, and identify similar employees based on skill sets [24][25]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):How Agents test Agents, clearly explained (with code): ...

Agent Testing Methodology - Traditional testing methods are inadequate for evaluating Agents due to the lack of fixed inputs and exact outputs when dealing with language [1] - The industry utilizes Agent-based testing, simulating Users and Judges to assess Agent performance [1] - The process involves testing Agents with Agents to evaluate their responses in a more realistic and dynamic environment [1]