My top secrets to running an AI Agent Workforce
I sit down with Allie K. Miller to talk about the shift from managing AI agents to enabling them. Allie runs a workforce of 34 AI agents led by an AI chief of staff named Simon, plus six directors named after Friends characters. She shares her three-word prompt, her daily AI diary, her AI watchdogs, and her rule to build the factory before the product. We then debate the future of software: why enterprises still want a vendor to call, and why consumer software now rewards taste and distribution. Listeners leave with one mindset shift and a first step they can finish in under three hours. Get Your Complete Financial OS at https://startup-ideas-pod.link/brex_SIP Timestamps 00:00 – Intro 02:29 – Become a Great Agent Manager 04:49 – The Three-Word Prompt 08:12 – The Pyramid of Proactivity 12:23 – Making the Company Queryable 19:14 – How to Design an AI Workforce 22:25 – AI as a Watchdog 24:56 – Startup Opportunities 26:29 – Build the Factory, Then the Product 30:09 – The SaaS Question 34:53 – Consumer Software as Art 37:12 – High-Value Bottlenecks 44:56 – Closing Thoughts Key Points • Allie sits three rungs above her 34 agents. She sets the infrastructure and waits for escalations. • Her strongest prompt runs three words on top of full business context: do smart things. • She holds the risk tier steady and expands only the breadth and scope of agent work. • A dictated daily diary captures the context that lives outside meetings, email, and Slack. • AI watchdogs remain wide open: duplicate work, calendar conflicts, and meeting disagreements. • The bigger play is a software factory. Build the primitives once, then ship each product faster. • I see opportunity on both sides of software: enterprises want a vendor to call, and consumers reward taste. Numbered Section Summaries 1. Beyond Agent Management Allie argues that the phrase "managing agents" describes an outdated relationship. She compares her role to an SVP three rungs up: she builds the infrastructure, and the agents choose how to execute inside it. I add that most people come to AI to escape management work, so the framing matters. 2. Empowerment and the Three-Word Prompt Allie ran an org of about 100 people at AWS. She keeps the part she loved, which is helping people break through their ceiling, and she drops the admin part. Her AI workforce reads every context doc she owns, including goals, meetings, email, calendar, Notion, Stripe, Supabase, and GitHub. Several times a day she prompts it with three words: do smart things. 3. The Pyramid of Proactivity I describe three types of employees, and the best one invents and completes new work. Allie maps this to Alex Lieberman's five levels of proactivity, where level five solves the problem and plans for failure. She runs a quarterly goals review with her agents so that new work stays goal-oriented. 4. Context and the Daily AI Diary Allie wants her whole company to be queryable. Meetings and email cover part of it, so a daily prompt asks her to dictate the rest, which is four times faster than typing. She has banked more than 80 entries into a personal wiki that her agents read. 5. An Org Chart Built for 2026 Simon acts as AI chief of staff over six directors. Allie warns that 2015 job titles produce a 2015 org, so she hires roles that human budgets rule out. Phoebe works as chief dreaming officer and asks how to 10x the output, and Toby watches the workforce and logs friction and access gaps. 6. How to Start, and How to Find Friction Allie suggests one agent first, then a proactive agent, then two agents working together, then a workforce. One prompt gets you started: describe the business and the goals, then ask the model to interview you. Sub-agents run on Haiku and Sonnet, and she reserves Opus for the heavy work. Her human team talks to her AI workforce in a Slack channel called Loop Alley. 7. Build the Factory, Then the Product For the AI First Index, Allie chose the abstraction layer over the single build. Her team assembled a beginner software factory with primitives for login, payments, social sharing, and newsletters. The first product already earns revenue, and each next product now ships faster and stronger. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND ALLIE ON SOCIAL X/Twitter: https://x.com/alliekmiller Instagram: https://www.instagram.com/alliekmiller/ LinkedIn: https://www.linkedin.com/in/alliekmiller/
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- 📄 Trascrizione completa con timestamp
- ✨ Riepilogo AI, parole chiave e mappa mentale
- 💡 Conclusioni e citazioni chiave
- Parlante 1
- Parlante 2
- Parlante 1
- Parlante 2
Cronologia dell'episodio
Introduction to the episode and guest Alli K. Miller
- Greg Isenberg introduces Alli K. Miller, a prominent AI voice who has worked with IBM and AWS and managed multi-billion dollar P&Ls in AI.
- The episode focuses on strategies for building and managing AI agent workforces that underpromise and overdeliver.
- The episode is sponsored by Brex, a financial platform used by companies like OpenAI and Anthropic.
Mindset shift: from managing agents to enabling them
- Alli argues that the term 'managing agents' is outdated; instead, she sees her role as setting up infrastructure and waiting for escalations, not micromanaging.
- She compares managing agents to managing people, noting that the admin side is undesirable, but the empowering side is valuable.
- She emphasizes giving agents breadth and scope, not just tasks, and allowing them to operate with flexibility while maintaining risk tiers.
The 'do smart things' prompt and proactive agents
- Alli's best prompt is three words: 'do smart things,' which she uses with her AI workforce that has access to all her contacts, docs, and tools.
- She explains that this prompt works because models like Fable 5 and GPT-5.6 can handle vague instructions and act proactively.
- She distinguishes between proactive agents with defined workflows (e.g., trigger-based automations) and undefined workflows where AI decides what to do based on goals.
Concetti chiave
- AI agent workforce— Central concept of the episode, discussing how to build and manage a team of AI agents.
- proactive agents— Key shift from reactive to proactive AI agents that take initiative without being prompted.
- do smart things— The three-word prompt that empowers AI agents to act autonomously and ambitiously.
Citazioni rilevanti
So the best prompt, three words, and it's just do smart things.
💡— Reveals that a simple, vague prompt can unleash AI agents to act autonomously, overturning the belief that detailed instructions are necessary.
I have 34 AI agents that work in this workforce.
🤯— The scale of AI agents managed by one person is surprising, showing how far AI workforce management has come.
Conclusioni applicabili
🧠Mindset
Shift from managing agents to enabling them and waiting for escalations.
This week, identify one task you currently delegate to an AI agent and give it a vague, high-level prompt like 'do smart things' to see how it responds.
Embrace the idea of AI as a proactive partner, not just a tool.
Set up a simple automation that triggers an AI action based on a specific event (e.g., new file in a folder) to experience proactivity.
📈Strategy
Build the factory behind the product, not just the product itself.
For your next project, instead of building a single solution, create a repeatable process or template that can generate multiple variations.
Use AI as a watchdog to catch issues and provide insights.
Set up an AI monitor on your calendar or email to flag conflicts or important patterns, and review its findings at the end of the week.
Trascrizione e analisi sono generate dall'AI e possono contenere errori. L'accuratezza dipende dalla qualità dell'audio e dalla chiarezza dei parlanti: in caso di dubbi, l'audio originale resta la fonte attendibile.
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