Omer Tadjer

🤝Adding human-in-the-loop to deep agents Many tools that you may want to give to agents will take actions in the real world. For these tools, you will often want to add "human-in-the-loop" steps - require a human user to approve, edit, or respond to their request to execute that tool. In this video, we will see how to add and use human-in-the-loop functionality to Deep Agents Video: https://lnkd.in/gfbjdABH Deep Agents Github: https://lnkd.in/gWpaj88j

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CopilotKit 🤝 Tavily One of the most powerful patterns in agentic apps is combining live web retrieval with interactive UI. That’s why our partnership with Tavily makes so much sense. Tavily provides a real-time search API that lets agents fetch current, verifiable info from the web. Paired with CopilotKit’s AG-UI protocol, developers now have a drop-in way to build fullstack agentic apps where agents don’t just respond, but act in the UI. How it works- User asks a question → Agent calls Tavily → UI updates live → User interacts → Agent continues the loop In short, devs can now get: - LLMs that fetch & reason with fresh, verifiable info - Copilots that act in your UI and not 'just reply' - A tight loop between user, agent, and data Check out our latest blog and explore how CopilotKit + Tavily can supercharge your AI copilots. Read more & get started: https://lnkd.in/gDicmii4

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Today we announced an upcoming change to how Augment Code is priced. Starting October 20, 2025, we’ll move from plans based on user messages per month to a credit-based model, where every plan includes a pool of credits that can be spent across different activities. Our current model treats every interaction as equal — but they’re not. A quick CSS fix isn’t the same as refactoring a mobile payments flow and adding Apple Pay. These tasks differ dramatically in scope, reasoning, and compute cost, yet today they’re priced the same. That’s not transparent for developers, and it doesn’t reflect how modern reasoning models behave. The best models pause to ask clarifying questions to produce better results. Under a message-based plan, that quality actually costs users more. It’s also unsustainable on our side; going forward, we're aligning pricing with real usage to maintain long-term reliability. A credit model fixes both problems: + Developers pay for actual compute and complexity, not arbitrary message counts + Augment stays sustainable while supporting enterprise-grade workloads and investing in the best Context Engine in the world For some customers, this will mean higher costs. Over the coming weeks, we’ll share tools to help teams forecast usage and understand what this means for them. Our goal is a fair, transparent model that supports what Augment does best — helping developers build with AI at scale. Read more on our blog → https://lnkd.in/gZdPTGjG

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After an incredible trip to San Francisco, it's evident that fostering a closer relationship between the public sector, venture capital, and the startup ecosystem is crucial. This synergy will drive the adoption of cutting-edge technologies that are essential now, not just what we perceive as necessary. Collaborating on building and iterating processes alongside the private sector is key. Reflecting on the insightful discussions from last week, it's clear that there's untapped potential in bringing key stakeholders together. Venture capitalists can play a significant role in bridging this gap and facilitating meaningful collaborations.

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I watched this fractional CTO win our cohort demo day 🎉 Makar Stetsenko started as a software engineer obsessed about solving hard problems for medical labs, luxury brands, and banks. Along the way, his eng team ran into brutal bottlenecks. Product launches got delayed by months because of technical debt. Engineers worked overtime to fix preventable outages. That's when his obsession shifted to fixing broken systems. He built a platform team that: ⚡ Increased delivery speed by ~60% in months 📈 Achieved 99.95% uptime while cutting critical incidents in half 🔧 Rebuilt their KYC to scale from 1 to 100+ markets Here's what he learned from all of this - The technical skills were table stakes. It was ultimately about building scalable systems that prevented problems instead of just reacting to them. That's why now he's helping founders leverage his plug-and-play framework to avoid the same engineering debacles. Recently, he ran a Tech Health Check for a Singapore startup in two weeks, which revamped their next-quarter engineering strategy. With another client, he built executive-level metrics for production health to clarify C-level priorities. Want Makar to run a free Tech Health Check? Shoot him a DM to set one up for your eng team.

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What is the future of AI? At AI4, Ravit Jain spoke with Kevin McGrath, Co-Founder and CEO of Meibel, on The Ravit Show about the hard truths behind the numbers: nearly 80% of AI projects never make it to long-term success. Kevin explained that it often comes back to data. Customers run into incomplete or poor-quality datasets, or they cannot unify data across silos. Without fixing those fundamentals, even the most ambitious AI initiatives will falter. They also explored why AI demos that wow investors often stumble when they reach real customers. Scaling is the breaking point; infrastructure, workflows, and production realities expose weaknesses that prototypes cannot hide. With GenAI, Kevin sees teams hitting roadblocks around context, reliability, and safety. Building proofs of concept is straightforward, but building systems that are trustworthy and scalable is the real challenge. The takeaway: AI success depends less on the demo and more on how well you prepare data and infrastructure for real-world scale. Link to the complete interview in the comments 👇 #data # ai #agents #meibel #theravitshow

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Excited to launch Agent Eval on Lyzr AI 🔥 The recent study by MIT suggests that 95% of all AI projects never make it to production. The real blocker? Trust. Teams can’t trust the agent to do what it’s supposed to do. At Lyzr, this has been our mission from day one: to make agents reliable and trustworthy. We became the first (and even today the only) agent framework to natively incorporate responsible AI and Hallucination Manager modules into the agent architecture. This has greatly helped our customers build and deploy agents in production. Now, we are taking that one step further with #AgentEval launch. Lyzr Agent Eval can now, ✍ write test cases for your agents 🧪 generate synthetic data to test your agents 💁‍♀️ provide suggestions to improve your agents Today, almost 80% of all agents built on Lyzr go to production. Lyzr is not just an enterprise agent infrastructure platform. We are your risk partner in making your agents trustworthy. Head to https://studio.lyzr.ai/ and try Agent Eval today. Don't forget to share your feedback. We would love to improve this super important feature for our customers. Great work Shreyas Kapale and Lyzr team, in bringing this to life.

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What do Innovaccer, TrueFoundry, 𝗮𝗻𝗱 CoverSelf have in common? I found out over lunch! It's this simple truth: "𝙋𝙧𝙤𝙨𝙥𝙚𝙘𝙩𝙨 𝙤𝙣𝙡𝙮 𝙘𝙖𝙧𝙚 𝙞𝙛 𝙮𝙤𝙪𝙧 𝙨𝙤𝙡𝙪𝙩𝙞𝙤𝙣 𝙬𝙤𝙧𝙠𝙨 𝙛𝙤𝙧 𝙩𝙝𝙚𝙢 𝙩𝙤𝙙𝙖𝙮." Over a z21 Ventures organized get-together here in San Francisco, I learned from three founders who embrace this truth every day: Abhinav Shashank (CEO, Innovaccer)   Even with $275M raised and 130+ health systems, growth relies on solving one specific workflow issue at a time. For sales teams, that means addressing the exact pain your prospect feels, not presenting a long list of features. Anuraag Gutgutia (Co-founder, TrueFoundry)   AI/ML adoption doesn’t succeed just because it’s “the latest model.” It works when it’s clear, reliable, and easy to implement. At Dmand | The GTM Platform for MedTech Companies, we focus our AI capabilities on what SDRs and BDRs can actually use right away: precision search, network discovery, and sales playbooks that lead to closure. CoverSelf    In healthcare, compliance and trust aren’t just checkboxes; they can make or break a deal. Highlighting these aspects in outreach can shorten sales cycles and build early confidence. I’m grateful to z21 Ventures for gathering such a sharp group of founders. To SDRs, BDRs, and commercial leaders:  𝙒𝙝𝙖𝙩’𝙨 𝙩𝙝𝙚 𝙗𝙞𝙜𝙜𝙚𝙨𝙩 𝙘𝙝𝙖𝙡𝙡𝙚𝙣𝙜𝙚 𝙮𝙤𝙪 𝙛𝙖𝙘𝙚 𝙞𝙣 𝙮𝙤𝙪𝙧 𝙖𝙥𝙥𝙧𝙤𝙖𝙘𝙝 𝙩𝙤𝙙𝙖𝙮 - 𝙩𝙖𝙧𝙜𝙚𝙩𝙞𝙣𝙜 𝙩𝙝𝙚 𝙧𝙞𝙜𝙝𝙩 𝙥𝙚𝙤𝙥𝙡𝙚, 𝙘𝙤𝙣𝙫𝙚𝙮𝙞𝙣𝙜 𝙩𝙝𝙚 𝙧𝙞𝙜𝙝𝙩 𝙢𝙚𝙨𝙨𝙖𝙜𝙚, 𝙗𝙪𝙞𝙡𝙙𝙞𝙣𝙜 𝙧𝙚𝙡𝙖𝙩𝙞𝙤𝙣𝙨𝙝𝙞𝙥𝙨, 𝙤𝙧 𝙨𝙤𝙢𝙚𝙩𝙝𝙞𝙣𝙜 𝙚𝙡𝙨𝙚? #HealthTech #MedTech #Sales #CommercialLeaders #SDR #BDR #GTM #Z21Ventures

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Today, we’re thrilled to publicly launch Phoebe and announce $17M in seed funding, in rounds led by GV and Cherry Ventures. We are building the immune system for software: An AI agent platform that continuously investigates live data, diagnoses emerging issues and generates preemptive fixes. As AI accelerates how software is built, reliability must keep pace. Phoebe started with faster troubleshooting, and our aim is to prevent the overwhelming majority of emergent problems before becoming customer-impacting failures. Huge thanks to the hundreds of early users at customers such as Trainline and PPRO. Thanks to our investors, including 20 CTOs and tech veterans joining as angels. And of course, thanks to the small and mighty Phoebe team! This is just the beginning! 🚀 Shoutout to Mark Kleinman at Sky News for breaking the news this morning (linked in the comments) Some of the many backers we're grateful for 🤗 🙏 GV (Google Ventures), Cherry Ventures, Anthony Danon, Carmen Alfonso Rico 🍫, William Gaybrick, Shola Akinlade, Yaron Shaer, Christian Dorffer, Tom Stafford, Thomas Wolf, Andrew Tan, Victor Riparbelli, Harsh Sinha, Jeanne DeWitt Grosser and more

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Two long years ago, at Movable Ink's Think Summit 2023, I shared how software developers have gained sizable leverage writing code over the past few decades. While there are only 1.6 million developers in the US, there are 160 million workers who haven't witnessed the same advances. But that was about to change! Fast forward to today. We're seeing AI transform companies in three fundamental ways: ⏰ We're nearing the end of humans writing software. Engineers, product managers, and designers are prompting AI to generate code, translate customer interviews into PRDs, and rapidly prototype new designs. 🛠️ We're capturing multi-step, manual, business processes and turning them into agentic workflows. Sales outreach copywriting, data transformation and sharing, prospect research, slide creation, and much more will be things of the past. 💻  And finally, the software that we build will be AI-first. Traditional UIs and user flows can break out of the constraints of human interaction. Think digital agents completing tasks or predictive recommendations eliminating entire steps. An individual contributor role will look more like an agent manager and those who learn how to harness these new tools will 10x their productivity relative to peers who don’t. Every business worker is about to experience leverage first-hand in a way that only developers have and skillfully managing agents will eclipse the ability to manage people. It's both a scary and thrilling time to be alive! 

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AI agents have evolved. The culture needs to catch up. Agents are terrific at removing friction. But if your team doesn’t know what to build, momentum stalls. On this week’s Agents of Scale, Replit CEO Amjad Masad and I dug into how leaders turn curiosity into shipped value. 1. Host monthly show-and-tell so people learn by seeing 2. Seed 5 starter workflows so no one starts from zero 3. Set a weekly demo target and celebrate shipped automations 4. Give safe-to-try guardrails so experimentation feels rewarded 5. Track impact in hours saved and incidents avoided, not just demos If you want the playbook for moving from hype to hands-on, this episode is it. Listen here: https://lnkd.in/gAjFTcxi P.S. Up next? Christopher Pedregal from Granola. Who else should I chat with?

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