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We just raised an $11M seed to help developers build complex agents across any model and any tool in 5 lines of code. Our round was led by Kindred Ventures (steve jang) and Saga (Max Altman, Ben Braverman, Thomson Nguyen). At Dedalus Labs, we believe the future of AI...

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Cathy Di

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Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.
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Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

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New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

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Today we're launching Airbyte Agents. It’s a platform meant to solve what we call the discovery gap. Agents in demos are successful because they know the fields and sources they have to call to solve their task. Agents in production fail because they don’t have that knowledge assembled beforehand. Airbyte Agents, powered by the Context Store, fixes that with one unified layer of pre-materialized context across all of your business systems. Real results. 80% fewer tokens. 40% fewer tool calls. Get more out of your AI tool’s plan limit. Three ways to build: MCP, SDK, or no-code Automations 💡 We're early, and we want to build with our users. Try Airbyte Agents today.

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New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

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We are excited to announce a powerful step for the future of FOMO! Taking a page out of Virtuals book on BASE, FOMO will be releasing the ability for future projects to be paired in $FOMO in the coming weeks. This is the biggest release we have ever announced. Launch your AI Agent Token + $FOMO trading pair Every individual agent token is paired with the $FOMO token in its liquidity pool. When launching an agent on you will need $FOMO tokens, which are used to create the liquidity pool. This process creates deflationary pressure for FOMO and the entire agent ecosystem. When creating your agent and token, you will have the option to pair your launch with FOMO or SOL, as our goal is not to alienate any project, but rather invite the best communities, CTO’s and builders to launch with us. If you decide to pair your project with FOMO you in turn get full marketing and dev support, once your project graduates the bonding curve and reaches Raydium. Further, as an added incentive, as our revenue grows we will be using part of the funds to support projects that have paired in FOMO. And Devs who launch tokens paired in FOMO will earn fees from their AI Agent token launch. Building the most robust agents using our framework will catapult us as one of the most prominent standards of the Solana ecosystem. Not only have we developed our own core infrastructure, but we also pull from some of the best repo’s and developer talent in all of AI, not just blockchain. Our team is comprised of 9 world class artificial intelligence engineers, PHDs in mathematics and engineering from the top companies on the cutting edge of AI. The future of AI Agents will be on Solana and we will help lead the way.

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🔥 New LangChain Academy Course: LangChain Essentials (Python & TypeScript) 🔥 Learn the basics of LangChain – our open source framework that makes it easy to start building agents with any model provider. Last week, we released LangChain 1.0. We’ve completely rewritten LangChain to be opinionated, focused, and powered by LangGraph’s runtime. It includes a new `create_agent` abstraction to build agents quickly, middleware for flexibility, and standard content blocks that work across any model provider. In this quickstart course, you'll learn how to: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug and test your agent with LangSmith Observability & Evaluation Enroll for free ➡️

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We just open-sourced Simulang, a JavaScript library that gives you and your coding agents the ability to fully control computers. Simulang is our scripting framework for desktop automation. It allows you to write deterministic computer use code that can be replayed dozens if not hundreds of times. Much faster and cheaper, compared to LLM agents. It ships with a skill out of the box. That means you can turn natural language prompts into Simulang code directly, and your agent can now see the screen, click, type, and navigate. It gives your AI agents eyes and hands to control the computer like humans do. Try these things with Simulang: - upload your pile of invoices to the expense platform - find 50 people with specific criteria on LinkedIn - open your calculator app and do some math - find all buttons on your favorite app using accessibility tree - or draw a bunny with MS paint 🐰 In this demo, we show how Simulang took over when the coding agent couldn’t open and click on the target LinkedIn profile page. Give it a try:

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14,362 Aufrufe • vor 2 Monaten

Check Run Agents, now available in Macroscope. we made a video to show you how it works! these are super flexible agents you control that let you enforce your team's coding conventions, workflows, and compliance checks during code review. just define in markdown and include trigger rules, model choice/reasoning levels, and tool access (including any 3rd party integrations and MCP servers you've already connected to macroscope)-- then the agent spawns as a Check in GitHub. we've been delighted by the creativity and complexity of what our customers have been doing with check run agents: enforcing style conventions, coordinating multistep db migration workflows, ensuring public-facing docs are updated in sync with code changes, and more.
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Check Run Agents, now available in Macroscope. we made a video to show you how it works! these are super flexible agents you control that let you enforce your team's coding conventions, workflows, and compliance checks during code review. just define in markdown and include trigger rules, model choice/reasoning levels, and tool access (including any 3rd party integrations and MCP servers you've already connected to macroscope)-- then the agent spawns as a Check in GitHub. we've been delighted by the creativity and complexity of what our customers have been doing with check run agents: enforcing style conventions, coordinating multistep db migration workflows, ensuring public-facing docs are updated in sync with code changes, and more.

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66,617 Aufrufe • vor 2 Monaten

Molecular design for AI agents: announcing the Tamarind MCP Server. Today, scientists can use the Tamarind Bio library of 250+ molecular design tools(Boltz, AlphaFold, RFdiffusion...) in any AI chat interface. We serve not just open-source models, but the internal protocols your team has onboarded to Tamarind. Any tool added to Tamarind is then available across the MCP server, Tamarind web app, and API, so it can be used in chat-based agents, multi-step pipelines, ELNs, and LIMS-connected workflows. Our goal is simple: make Tamarind the place where scientists can access the BioAI tools they need, wherever they want to work, while we handle the infrastructure. Many users have already incorporated our MCP into their internal AI agents, along with community efforts like Blatant-Why building apps on top of Tamarind. Try out our tooling for antibody design, small molecule virtual screening, developability/ADMET scoring and more!
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Molecular design for AI agents: announcing the Tamarind MCP Server. Today, scientists can use the Tamarind Bio library of 250+ molecular design tools(Boltz, AlphaFold, RFdiffusion...) in any AI chat interface. We serve not just open-source models, but the internal protocols your team has onboarded to Tamarind. Any tool added to Tamarind is then available across the MCP server, Tamarind web app, and API, so it can be used in chat-based agents, multi-step pipelines, ELNs, and LIMS-connected workflows. Our goal is simple: make Tamarind the place where scientists can access the BioAI tools they need, wherever they want to work, while we handle the infrastructure. Many users have already incorporated our MCP into their internal AI agents, along with community efforts like Blatant-Why building apps on top of Tamarind. Try out our tooling for antibody design, small molecule virtual screening, developability/ADMET scoring and more!

Deniz Kavi

14,553 Aufrufe • vor 3 Monaten

An LLM with native access to find and run agents is a huge thing! This large language model (called ASI:One and developed by Fetch.ai) can do this natively when you show it a problem: • Reason about the problem • Identify live agents and tools that can help solve it • Dynamically invoke these agents and tools Honestly, this is pretty nuts! You don't need to set up anything beforehand. You don't need to write any code. Here is the best part: ASI:One has access to any agent published on Agentverse, an open directory of agents! This gives the model access to *millions* of tools it can execute dynamically when solving a task. Here is a simple example (the one in the attached video): • I went to Google Maps • I copied the latitude and longitude of a location • I asked ASI:One about relevant places around that location • ASI:One went to the marketplace • It identified and called the Google Places agent • It returned a list of museums, restaurants, and landmarks near my location Pretty awesome! You can try this now by going to You don't need to pay or register to test it out. If you want to get ideas of what's possible, check Agentverse, the agent marketplace: The model will have access to all of those tools. This is pretty awesome and opens up a whole new world of possibilities! Thanks to the team for partnering with me on this post!
4:59

An LLM with native access to find and run agents is a huge thing! This large language model (called ASI:One and developed by Fetch.ai) can do this natively when you show it a problem: • Reason about the problem • Identify live agents and tools that can help solve it • Dynamically invoke these agents and tools Honestly, this is pretty nuts! You don't need to set up anything beforehand. You don't need to write any code. Here is the best part: ASI:One has access to any agent published on Agentverse, an open directory of agents! This gives the model access to *millions* of tools it can execute dynamically when solving a task. Here is a simple example (the one in the attached video): • I went to Google Maps • I copied the latitude and longitude of a location • I asked ASI:One about relevant places around that location • ASI:One went to the marketplace • It identified and called the Google Places agent • It returned a list of museums, restaurants, and landmarks near my location Pretty awesome! You can try this now by going to You don't need to pay or register to test it out. If you want to get ideas of what's possible, check Agentverse, the agent marketplace: The model will have access to all of those tools. This is pretty awesome and opens up a whole new world of possibilities! Thanks to the team for partnering with me on this post!

Santiago

43,784 Aufrufe • vor 1 Jahr

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.
2:14

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.

Aaron Levie

24,618 Aufrufe • vor 3 Monaten

Over the past couple of years, you’ve heard us reference “agents” and “agentic capabilities,” nebulous concepts that are designed to help you with coding, booking trips, and other complex multi-step tasks. But, what actually IS an agent? We think of AI agents as systems that combine the intelligence of advanced AI models with access to tools so they can take actions on your behalf, under your control. Whether it’s a coding agent or our recently unveiled experimental tool Gemini Agent in the Google Gemini, these are systems that will meet you where you are, in the products that you love, to help take the tedious tasks you want off your plate.
0:57

Over the past couple of years, you’ve heard us reference “agents” and “agentic capabilities,” nebulous concepts that are designed to help you with coding, booking trips, and other complex multi-step tasks. But, what actually IS an agent? We think of AI agents as systems that combine the intelligence of advanced AI models with access to tools so they can take actions on your behalf, under your control. Whether it’s a coding agent or our recently unveiled experimental tool Gemini Agent in the Google Gemini, these are systems that will meet you where you are, in the products that you love, to help take the tedious tasks you want off your plate.

Google AI

120,790 Aufrufe • vor 7 Monaten

Multi-agent systems offer incredible potential and unprecedented risks. How do you solve for observability, failure mode analysis, and guardrailing in the era of agents? Today, we’re announcing our Agent Reliability platform to observe, evaluate, guardrail, and improve agents at scale. You can get started with the complete platform for trustworthy agentic AI today for free, and here’s how we’re solving some of the biggest challenges in agent reliability: - Observability redesigned for agents Trace views collapse under complex workflows, so we created the Graph View, Timeline View, and Conversation View to offer rich, intuitive visualizations of agent decisions, tool calls, and conversation flows. This multi-dimensional approach enables teams to pinpoint exactly where and why agents deviate or fail. - Automated Failure Mode Analysis with our new Insights Engine Our Insights Engine ingests your logs, metrics, and agent code to automatically surface nuanced failure modes and their root causes. But knowing the problem is not enough; you need to know how to fix it. Insights Engine delivers actionable fixes and can even apply them automatically. With adaptive learning, your insights become smarter and more relevant as your agents evolve. - Evaluating Agents Across Multiple Dimensions Agentic systems interact across complex pathways, and evaluating their performance requires new metrics that reflect this increasing complexity. To deliver comprehensive agentic measurements, we’ve added more out-of-the-box agent metrics like flow adherence, agent flow, agent efficiency, and more. For specialized domains and unique workflows, custom metrics powered by our new Luna-2 small language models can be rapidly designed and fine-tuned for your specific use case. - Real-Time Guardrails Powered by Luna-2 As AI agents become more autonomous and complex, failures like hallucinations or unsafe actions increase dramatically. Without real-time guardrails, these errors will hurt your user experience and brand reputation. Our Luna-2 family of small language models is purpose-built to provide low-latency, cost-effective guardrails that actively stop agent errors before they happen. With support for out-of-the-box and custom metrics, Luna-2 enables enterprises to enforce safety, compliance, and reliability at scale. Enterprises running hundreds of agents and processing hundreds of millions of queries daily already rely on Galileo’s Agent Reliability platform to protect their users, safeguard brand trust, and accelerate innovation. Agent Reliability is available starting today. Try it for free and experience the new standard in AI reliability. Learn more below 👇
2:43

Multi-agent systems offer incredible potential and unprecedented risks. How do you solve for observability, failure mode analysis, and guardrailing in the era of agents? Today, we’re announcing our Agent Reliability platform to observe, evaluate, guardrail, and improve agents at scale. You can get started with the complete platform for trustworthy agentic AI today for free, and here’s how we’re solving some of the biggest challenges in agent reliability: - Observability redesigned for agents Trace views collapse under complex workflows, so we created the Graph View, Timeline View, and Conversation View to offer rich, intuitive visualizations of agent decisions, tool calls, and conversation flows. This multi-dimensional approach enables teams to pinpoint exactly where and why agents deviate or fail. - Automated Failure Mode Analysis with our new Insights Engine Our Insights Engine ingests your logs, metrics, and agent code to automatically surface nuanced failure modes and their root causes. But knowing the problem is not enough; you need to know how to fix it. Insights Engine delivers actionable fixes and can even apply them automatically. With adaptive learning, your insights become smarter and more relevant as your agents evolve. - Evaluating Agents Across Multiple Dimensions Agentic systems interact across complex pathways, and evaluating their performance requires new metrics that reflect this increasing complexity. To deliver comprehensive agentic measurements, we’ve added more out-of-the-box agent metrics like flow adherence, agent flow, agent efficiency, and more. For specialized domains and unique workflows, custom metrics powered by our new Luna-2 small language models can be rapidly designed and fine-tuned for your specific use case. - Real-Time Guardrails Powered by Luna-2 As AI agents become more autonomous and complex, failures like hallucinations or unsafe actions increase dramatically. Without real-time guardrails, these errors will hurt your user experience and brand reputation. Our Luna-2 family of small language models is purpose-built to provide low-latency, cost-effective guardrails that actively stop agent errors before they happen. With support for out-of-the-box and custom metrics, Luna-2 enables enterprises to enforce safety, compliance, and reliability at scale. Enterprises running hundreds of agents and processing hundreds of millions of queries daily already rely on Galileo’s Agent Reliability platform to protect their users, safeguard brand trust, and accelerate innovation. Agent Reliability is available starting today. Try it for free and experience the new standard in AI reliability. Learn more below 👇

Galileo

1,276,298 Aufrufe • vor 1 Jahr

We're winning the AI browser wars, and we're not even trying. Our mission isn't to build a browser. We're an agent company, not a browser company. We've seen firsthand how teams struggle to get started with AI. The tools they tried are too complex and lack the context you need. We decided to build a browser since it's already the tool you work in. We all know how to use a browser. You can pick up Strawberry and start using AI agents from day one. By building a browser we are also positioning ourselves to build towards the internet of the future. An internet where trillions of agents will be communicating with each other. This is the biggest opportunity of our lifetime. But someone has to make sure it is safe, effective and accessible to teams across the world. That is our mission.
0:52

We're winning the AI browser wars, and we're not even trying. Our mission isn't to build a browser. We're an agent company, not a browser company. We've seen firsthand how teams struggle to get started with AI. The tools they tried are too complex and lack the context you need. We decided to build a browser since it's already the tool you work in. We all know how to use a browser. You can pick up Strawberry and start using AI agents from day one. By building a browser we are also positioning ourselves to build towards the internet of the future. An internet where trillions of agents will be communicating with each other. This is the biggest opportunity of our lifetime. But someone has to make sure it is safe, effective and accessible to teams across the world. That is our mission.

Charles Maddock

38,357 Aufrufe • vor 1 Monat

We just raised a $75M Series B to *finally* automate AML/KYC with AI agents. KYC/AML software has been around for a long time. Bretton AI is different: - Autonomous browsing: agents hop between multiple tools to complete reviews without the need for a massive engineering lift for the bank - Long Horizon Agents: our agents complete iterative, complex reviews that take humans hours to complete - Bank-grade model risk management: built for regulators, internal audit and third-party reviews It's why companies like Robinhood, Lead Bank and Gusto use us to scale efficiently and deliver great customer experiences. Our Series B is led by Rajeev Dham and Sapphire Ventures , with participation from Greylock, Y Combinator, Thomson Reuters Ventures, Canvas Ventures and TIAA Ventures. 3 years ago, this company was just a dream. Now I get to work with 40 of the smartest, hardest working poeple I’ve ever met to solve big problems at the heart of finance. I'm pumped for the future! Now back to building.
1:18

We just raised a $75M Series B to *finally* automate AML/KYC with AI agents. KYC/AML software has been around for a long time. Bretton AI is different: - Autonomous browsing: agents hop between multiple tools to complete reviews without the need for a massive engineering lift for the bank - Long Horizon Agents: our agents complete iterative, complex reviews that take humans hours to complete - Bank-grade model risk management: built for regulators, internal audit and third-party reviews It's why companies like Robinhood, Lead Bank and Gusto use us to scale efficiently and deliver great customer experiences. Our Series B is led by Rajeev Dham and Sapphire Ventures , with participation from Greylock, Y Combinator, Thomson Reuters Ventures, Canvas Ventures and TIAA Ventures. 3 years ago, this company was just a dream. Now I get to work with 40 of the smartest, hardest working poeple I’ve ever met to solve big problems at the heart of finance. I'm pumped for the future! Now back to building.

Will Lawrence

93,716 Aufrufe • vor 5 Monaten