Agent Key Layers - Tools help Agents connect to the external world [1] - Memory helps Agents remember [1] - Agents still can't learn from experience [1] Learning Gap - Karpathy mentioned a key gap in Agents' ability to learn from experience in his recent podcast [1]

Agent Learning & Development - Current agents lack continual learning, hindering their ability to build intuition and expertise through experience [1][2] - A key challenge is enabling agents to learn from interactions and develop heuristics, similar to how humans master skills [1][2] - Composio is developing infrastructure for a shared learning layer, allowing agents to evolve and accumulate skills collectively [3] - This "skill layer" provides agents with an interface to interact with tools and build practical knowledge [4] Industry Trends & Alignment - Anthropic is exploring similar approaches, codifying agent behaviors as reusable skills [4] - The industry is moving towards a design pattern where agents progressively turn experience into composable skills [4] Composio's Solution - Composio's collective AI learning layer enables agents to share knowledge, allowing them to handle API edge cases and develop real intuition [5] - This approach facilitates continual learning, where agents accumulate skills through interaction rather than just memorizing [5]

AI Agent Infrastructure - The industry is focused on building a real-time context layer for AI Agents across numerous data sources [2] - Airweave offers an open-source context retrieval layer as a solution [2] Technical Challenges & Solutions - Companies face the challenge of querying data spread across multiple sources like Gmail and Drive [2] - A common but potentially insufficient solution is embedding everything in a vector database and using RAG (Retrieval-Augmented Generation) [2]

Next, repeat these steps for the 2nd server to host the Smolagents Agent and its LLM.- Line 1-10 → Imports + define the Server & the LLM.- Line 12 → Decorate the method.- Line 21-28 → Define the Agent with a web search tool.- Line 31 → Serve the Agent.Finally, we use an ACP client to connect both agents in a workflow.- Line 6-7 → Connect the client to both servers.- Line 11-14 → Invoke the first agent to receive an output.- Line 18-21 → Pass the output to the next agent for enhancement.Next, run the two ser ...

ACP is a standardized, RESTful interface for Agents to discover and coordinate with other Agents, regardless of their framework.Just like A2A, it lets Agents communicate with Agents. There are some differences, which we shall discuss later.Here's how it works:- Build the Agents and host them on ACP servers.- The ACP server receives requests from the ACP Client and forwards them to the Agent.- ACP Client itself can be an Agent to intelligently route requests to the Agents (like MCP Client does).Let's dive in ...

Transaction Volume - Virtuals Protocol transactions increased by 500% in one week following x402 integration [1] - The introduction of payment rails triggered a significant surge in transaction volume [1] Latent Demand - Elizaos has over 50,000 agents awaiting similar unlocking [1] - Infrastructure that enables latent demand represents an undervalued opportunity in the crypto market [1]

Industry Trend - The industry is moving towards an era where every website must be "Agent-ready" [1] - Agents, not humans, will be making purchases in the future [1] - Agents will be responsible for finding the best options [1] Technological Advancement - Postman's latest Guidebook on building AI-ready APIs is highlighted as a crucial document for developers [1]

LangChain Essentials Course Highlights - LangChain releases a new LangChain Essentials course for learning the basics of LangChain in an hour [1] - The course focuses on building agents using the `create_agent` abstraction [2] - The pre-built agent utilizes a ReAct-style architecture for reasoning and acting with tools [3] Agent Architecture and Scalability - The agent is built on LangGraph to balance flexibility with pre-built abstraction benefits [4] - The agent is designed to be scalable, resilient to failures, and allows for human intervention [3] - The agent can dynamically select prompts and models, with optional middleware for customization [4] Course Content - The course covers features of the `create_agent` abstraction through building increasingly sophisticated agents [5] - The course utilizes LangChain building blocks including messages, tools, and models [5]

Langchain Evolution & Strategy - Langchain started as an open-source package and has evolved into Typescript packages, Langchain, and Langraph [1][2] - The industry focus has shifted from easy prototyping to production-ready solutions, leading to the launch of Langraph [7] - Langchain 1.0 is built on top of Langraph, combining ease of use with production-ready runtime [16] Langraph Features & Benefits - Langraph was launched to provide more controllability and customization for users transitioning to production [8][9] - Langraph includes utilities like durable execution environments, error recovery from checkpoints, and streaming capabilities [13][14] - Langraph allows for deterministic steps and workflows, making it suitable for complex applications [39] Langchain 1.0 & Create Agent Abstraction - Langchain 1.0 aims to be the easiest way to get started with generative AI, specifically building agents [17] - The create agent abstraction simplifies agent creation with a few lines of code, leveraging a battle-tested pattern [18][19] - Middleware allows developers to add custom logic at any point in the agent loop, enabling extensibility [23] Models & Content Blocks - Dynamic model middleware enables dynamic selection of models based on context, allowing builders to stay on the bleeding edge [27][29] - Content blocks are introduced as a standard representation for message content, addressing the issue of varying formats across model providers [31][32] Langchain vs Langraph - Langchain is recommended for getting started due to its ease of use, while Langraph is suitable for extremely custom workflows [36][37] - Langraph is ideal for workflows that require deterministic components and agentic components [37]

Heat. [Applause] [Music] Heat. Heat. [Applause] [Music] Heat. Heat.Heat. [Music]. ...