This is how we build an agentic workflow. We put each step into 4 buckets, and I was surprised how few were agentic. - Delete. After AI, this step is not necessary. - Plain code. The step follows rules and needs no judgment, for example a simple API call. - Agentic. The step needs judgment. - Humans in the loop. The step has the highest risk. Building a quote is an agentic task The agent must know who the customer is, what it is worth to us, and what resources to give it. Humans keep approval, negotiation, signing, and payment submission. These are the "things that you really cannot afford to get wrong." Before you give a step to an AI agent, put the step in one of the 4 buckets.

i am once again asking for you to checkout the latest SIP episode

How does an FDE audit a real workflow? We did one for a $5B public software company with 150+ products. Step 1: Read their process document. - Theirs had 7 steps: build the quote, submit, Deal Desk, approve, send, negotiate, sign. Step 2: Interview more than one person. - One person can tell you the wrong story. Step 3: Put process mining agents on the CRM. The agents found 20 steps and 7 loops. - 61% of requests go from step four or five back to step one. - Legal sends the request back 12% of the time. - 30% of the time, a new quote must get approval again. Step 4: Map the real workflow. - 4 of our deployed engineers did this. - The CRO and CFOs told us, "I feel like you understand our department better than we do." Before this map, no one in the department had seen the workflow this clearly. Do this audit before you use AI, so you know which process is broken.

HOW TO BECOME A FORWARD DEPLOYED ENGINEER (53 MIN MASTERCLASS) Some forward deployed engineers are getting PAID $1M+/year (kinda crazy, I know) And I REALIZED almost nobody talks about HOW they do it in any detail. It's basically nowhere online. So I asked someone who does it ALL DAY LONG for Fortune 500 companies to walk me through it, step by step. and he shares EXACTLY how he does it piped.video/watch?v=1a5HxU52… Here's what I learned: 1. They start by listening, not building. They interview the people doing the work and let agents quietly read the company's data for a few weeks. The real process is almost always 3x longer than the one on paper. 2. They sort every step into four buckets: delete it, automate it with a simple rule, give it to an AI agent, or keep a human on it. A surprising amount ends up in the first bucket. 3. They build inside the tools the company already uses. Nobody has to learn a new app. When a human needs to sign off, it's just a Slack message. 4. They use the cheapest model that gets the job done. Most of this work doesn't need the most expensive AI. 5. They measure everything before and after, then come back 6 months later and prove it worked. This + concrete client examples + way more on today's episode of @startupideaspod (thanks to @vasuman) A full course pretty much on the topic aivalable to you 100% for free. This episode is for ANYONE who wants to be a FDE or who wants to make their company AI-native by deploying agents. Watch

I'm headed to San Francisco next week. First trip away since my son was born two months ago! My life right now is 3am feedings, a toddler running around, and building in the gaps. Nap time is my new deep work block. So when I say I'm excited to sit at a dinner table and have a full conversation with adults about AI agents and startup ideas without anyone throwing food at me, I mean it. I'll be in town for a few days. If you're building something interesting, I'd love to buy you dinner and hear about it. Who wants to hang out in SF?

My first OBSERVATIONS from Sundar's Gemini 4 Argon announcement: 1. Quantum. Google's quantum researchers gave Argon a key piece of quantum software to shrink, and in minutes it found a version that needs 40% fewer qubits and operations than the best published human solution. Quantum computers are useful ONLY once programs fit on the hardware, so this pulls that date closer from the software side, and it also shortens the time banks and governments have to switch to encryption quantum can't break. 2. Data center memory. A team of Argon agents read Google's server data, found waste, and freed over 300 TiB of memory on their own, with up to 1 PiB expected. If agents can do this everywhere, companies will point them at their cloud bills before buying more servers, and some of the huge data center buildout may turn out to need less hardware than planned. 3. Rewriting old code. Argon agents are converting Google's old C and C++ code into Rust, a safer language, including an 800K- ine operating system kernel. There are PLENTY of companies stay with outdated software vendors only because switching is painful, and once rewriting is cheap, that lock in starts to break! 4. Faster than the engineers. On one video decoder, Argon ran 100s of experiments and ended up 2.7x faster than the version Google's own engineers wrote. Speeding up your slowest code used to take a specialist team, and now it's something you can run overnight and pay for based on the savings. 5. Million-token answers. The most a model could write in one answer went from 64K tokens to 1 million, enough for a whole codebase or a full due diligence report. Obviously, nobody can carefully review that much output, so the bottleneck moves from making things to checking them, and tools that verify AI work become a big market. 6. Two versions of the same model. Vetted security teams get Argon with the cyber safety limits removed, and everyone else gets the restricted one. The best defense goes to big trusted organizations first, which leaves smaller companies more exposed and creates demand for services that get them "trusted" status. Gotta think about this more and what it means. 7. Finding bugs faster than anyone can fix them. Wiz used Argon to find a serious security hole in hospital software used worldwide that every earlier model missed. When AI can find holes this fast, the pile of known but unpatched bugs becomes the real risk, and whoever makes patching fast wins. 8. Legal work. On Harvey's legal benchmark Argon scored 19.6%, and every other top model was under 7%, the biggest gap in the whole chart!! Harvey, a startup, built that test, and now Google launches on it, so owning the benchmark in your industry gives you leverage over the labs. 9. Promo pricing. It launches at $2 in and $10 out per million tokens, about a 20% ish of GPT6's price, and then doubles. Startups that set their prices on the promo rate will see margins shrink in a few months, even though everyone assumes AI only gets cheaper. 10. Cheap memory. Reused context is 95% off. Products that keep one big shared knowledge base and reuse it across users will run far cheaper than rivals that resend everything each time. 11. Watching the model. Google seals Argon's test environment before training, watches its reasoning live, can stop it mid task, and asked other labs to keep reasoning readable. Companies running their own agents will copy this, and monitoring and permissions for AI agents turns into its own product category. 12. Government first. The US government gets access before the public through a voluntary review. DC is pretty much the first customer now, and launch dates start depending on its review, not only on when the model is ready. That's the new normal, I guess since the Fable/Mythos debacle. I'll share more on Gemini 4 as it comes out on @startupideaspod Never a dull moment in AI, isn't it?

Live business coaching x.lingyaoai.com/i/broadcasts/1DxLdZrbE…

POV: first standup with the new team

OpenAI just shipped Dots, and it is a huge business opportunity. Dots is OpenAI's new personal agent platform. It gives plugins access to ChatGPT's 1.2 billion weekly active users. A plugin is a mini app that does work inside Dots. "You can be hired before the customer knows your name." Take a permit company. - A user types "I need this permit fix." - ChatGPT understands the job and selects a plugin. - The permit company's plugin files the permit. ChatGPT got mixed reviews on plugins before. Now it is making plugins a priority again. I expect billions of dollars in transactions here. In 10 to 20 years, I think it can be trillions. ChatGPT sends the work to the plugin that completes the task with a good result. Pick one customer and one request. Build the plugin that does it.

There were 20+ announcements at OpenAI DevDay today, but only 4 REALLY stood out to me. Here’s my take on Dots, agents, and the potential $100B opportunity for founders after watching Sam Altman and team for 60 minutes. piped.video/watch?v=Y_RevX5y…

How do you run a boring business with AI agents? Build your folders like an org chart. One shared folder holds the agents: intake, preparer, reviewer, client-comms, reporting. Each business gets its own folder. That folder holds the rules for that business only, such as a report date that one client asked for years ago. Three files let you trust the agents with a client: - global rules - business rules - the corrections log The log takes effort. Each time a person fixes agent work, you record the fix. Start with those 3