Responses API Overview - OpenAI introduced the Responses API to evolve beyond the Chat Completions API, addressing design limitations and enabling new functionalities for building agentic applications [1] - The Responses API combines the simplicity of chat completions with the ability to perform more agentic tasks, simplifying workflows like tool use, code execution, and state management [1] - The core of the Responses API is an agentic loop, allowing multiple actions within a single API request, unlike Chat Completions which only allows one model sample per request [2] - The Responses API uses "items" for everything, including messages, function calls, and MCP calls, making coding easier compared to Chat Completions where function calling was bolted onto messages [2] - The Responses API is purpose-built for reasoning models, preserving reasoning from request to request, boosting tool calling performance by 5% in primary tool calling eval tobench [2] - The Responses API facilitates multimodal workflows, making it easier to work with images and other multimodal content, including support for context stuffing with files like PDFs [2] - Streaming is rethought in the Responses API, emitting a finite number of strongly typed events, simplifying development compared to Chat Completions' object deltas [2] - Long multi-turn rollouts with the Responses API are 20% faster and less expensive due to the ability to rehydrate context from request to request, preserving the chain of thought [2] Agent Platform and Tools - OpenAI is changing deployment with its agent platform, centering on the Responses API and Agents SDK for building embeddable, customizable UIs [3] - Agent Builder and Chatkit, built on the Responses API, make it easy to build workflows into applications with minimal effort [3] - The Responses API is at the core of the improvement flywheel, enabling distillation and reinforcement fine-tuning using stateful data, along with tools like web search and file search [3]

Industry Trend - Agents are leading the next evolution in technology by thinking, paying, and settling onchain [1] - The industry is rebuilding the world's money rails [1] Partnerships and Standardization - The Agent Payment Protocol was developed with over 60 payment and tech partners [1] - Key partners include American Express, Coinbase, Intuit, Mastercard, PayPal, Salesforce & ServiceNow [1] - The protocol aims to standardize how agents handle financial transactions [1]

Thanks, Sam. Hi, everyone. I'm Christina and I work on the team building Agent Kit.Today, I want to show you how Agent Kit helps developers create agents faster than ever before. So, you may have already seen our Devday website. It's the site here that all of you have access to and has everything about today's schedule.But right now, it's just a static page. What if it could actually help you navigate the day and point you to the sessions that are most relevant to you. We're OpenAI.We need to have AI in our ...

Technology & Innovation - Airweave addresses the limitations of RAG (Retrieval-Augmented Generation) in handling real-time data [1] - Airweave constructs live, bi-temporal knowledge bases to ensure agents utilize the most up-to-date information [1] - The system supports comprehensive agentic retrieval, incorporating semantic and keyword search, query expansion, and integration across 30+ sources [1] - Airweave is 100% open-source [1]

Hi, today we'll be covering how to use Langmith to debug your AI applications and we'll be using a tool called Studio to do so. The studio is an IDE for building agents and you can use it with any langraph agent that you've built. Let's take a look at the repository for this course which contains our explain like 5 agent.We'll cover what you need to start using studio. You'll notice that in our repo, we have this file called langraph. json.langraph. json is a config file that tells studio where your agents ...

Building Agents is about engineering “behavior” at scale. So you cannot vibe-prompt an Agent and expect it to work.Parlant gives the structure to build Agents that behave exactly as instructed.GitHub repo: https://t.co/kjVj5Rp7Xm(don't forget to star it ⭐) ...

RT MetaMask Developer (@MetaMaskDev)We’re at the dawn of the era of agents.Adoption is early, curiosity is relentless, and the frontier is wide open.Across @Consensys, we’re committed to supporting and expanding x402 ​(proposed by @CoinbaseDev​) and we’re working with​ @Google to create an ope​n economy of agentic payments. 🧵 ...

RT s4mmy (@S4mmyEth)I've had several Prediction Market protocols ask why I don't post more about their industry.I have zero doubt that the market segment will do ok, but ultimately (imo) it doesn't provide the same value that I see being created with AI & Agents.Take BIO as a prime example: It aims to disrupt medicine by launching agentic systems for the top scientists.That's a lofty vision.Its not important to me whether they'll succeed, but the fact they are trying something innovative is what gets my att ...

RT s4mmy (@S4mmyEth)I've had several Prediction Market protocols ask why I don't post more about their industry.I have zero doubt that the market segment will do ok, but ultimately (imo) it doesn't provide the same value that I see being created with AI & Agents.Take BIO as a prime example: It aims to disrupt medicine by launching agentic systems for the top scientists.That's a lofty vision.Its not important to me whether they'll succeed, but the fact they are trying something innovative is what gets my att ...

Focus and Vision - The author prioritizes AI & Agents over Prediction Market protocols due to their potential for greater value creation [1] - The author is interested in disruptive technologies that drive real-world change and find breakout use cases for blockchain and DeAI [2] - The author is open to covering "Prediction AI" innovations but primarily focuses on disruptive tech [2] BIO and Aubrai Example - BIO aims to disrupt medicine with agentic systems for top scientists [1] - Onchain scientific agents may be the start of "vibe science" [3] - $AUBRAI has generated 980 novel hypotheses hashed on @base [3] - $AUBRAI generated $180 thousand in fees for experiments in 8 days on +$20 million volume [3] - @aubreydegrey's X account has 145 thousand listeners [3]