Consumer Software Industry Concerns - Consumer software CEOs are worried about agents taking over, leading to a paradigm shift away from web pages [1] - The rise of agents could make the traditional browser obsolete, reducing it to a glorified markup reader [6] Perplexity's Strategic Opportunities - Perplexity has the potential to replace Bloomberg in the financial information and data sector, a market worth $100 billion [4][5] - Focusing on a specific vertical and owning it is a good strategy for Perplexity, especially with unique data sources [10] Apple's Potential Acquisition of Perplexity - There is speculation that Apple might acquire or partner with Perplexity for distribution, given the Justice Department case against Google [9] - However, acquiring Perplexity may not solve Apple's AI challenges, as Apple has missed previous AI opportunities [10] Bloomberg's Weaknesses - Bloomberg's terminal is considered atrocious, terrible, and limited, presenting an opportunity for a better product [4] - Bloomberg's core usability and UI have not evolved, despite its messaging platform being valuable for trading [14]

Key Features of Stagehand - Bridges the gap between brittle traditional automation tools like Playwright and Selenium, and unpredictable full-agent solutions like OpenAI Operator [1][3] - Employs AI for navigating unfamiliar pages and code (Playwright) for executing known tasks [3] - Allows previewing AI actions before execution [3] - Caches repeatable actions to conserve tokens [3] - Compatible with SOTA computer use models with minimal code [3] - Available in both Python and TypeScript SDK [3] Stagehand Ecosystem - Includes an open-source MCP server [2] - Aims to provide a browser automation framework for Agents suitable for production environments [1]

RT Avi Chawla (@_avichawla)6 no-code LLMs, Agents, and RAG builder tools for AI engineers:(open-source and production-grade) ...

6 no-code LLMs, Agents, and RAG builder tools for AI engineers:(open-source and production-grade) ...

MCP Overview & Architecture - MCP (Micro Control Plane) is defined as a pluggable architecture for agents, contextualized within an enterprise cloud service [5][6] - Sentry's MCP server was initially built as a fun project and is biased towards Sentry's application monitoring services [4][5] - The industry views MCP as a potential solution for integrating services into various agents, enabling bug fixes and workflow enhancements within editors [7][8][25] Implementation & Challenges - Implementing MCP involves complexities around OAUTH 21%, requiring solutions like Cloudflare Shim for proxying OAUTH 2 API [16][17] - A key challenge is that MCP cannot simply sit on top of Open API; systems need to be designed around how agents and models react to provided context [19][20][21] - Current client support for native authentication is still evolving, with some clients like Cursor experiencing breakage [22] Security & Best Practices - Security is a major concern, particularly with the standard IO interface, and random MCP tools should not be allowed within organizations [27] - For B2B SaaS companies, focusing on OAUTH with remote environments is crucial for integrating services into agents [25] - Companies should avoid simply proxying Open API and exposing it as tools, as this yields poor results; intentional design and context provision are necessary [30] Agent-Centric Approach - The industry should focus on building agents, viewing MCP as a plug-in architecture to leverage the value of LLMs [39][40] - Exposing agents through the MCP architecture, particularly in B2B settings, is seen as a significant value unlock [42] - Optimizing for context in workflows and understanding data is crucial when designing agents, with a focus on providing structured information like Markdown for language models [31][50]

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]