Hello, and happy Sunday. We covered two ways to work with an agent this week: one just for you, and one for the whole company. Dan Shipper tested Duet, Anthropic’s always-on agent, and thinks it’s the best personal agent yet. Willie Williams, our head of platform, explained why we ditched an earlier experiment with personal agents and built one agent that the Every team shares. Whichever kind you choose, you’ll want to know if it’s doing a good job. Head of evals Mike Taylor explains how to measure your outputs in his new book, Context Engineering with DSPy, and will take your questions during his livestream on October 29.—Kate Lee
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Protocols
From this week’s stories, 13 skills to try.
Make an agent’s work easy to watch
From Alex Duffy’s “We Caught an AI Cheating by Watching It Play ‘StarCraft’”
- See what your agent is doing. Watching the match is how Alex’s team caught Astra copying code within minutes. His prompt applies that lesson to any agent: Define success up front, make its work easy to inspect, and log every attempt. The prompt
Set the rules for a shared agent
From Willie Williams’s “Why We Replaced Personal Agents With One for the Whole Company”
- Ask what could be undone. Engineer Will Gathright’s test: If a step is easy to undo, the agent does it and says what it assumed; if it deletes something or posts outside the conversation, it asks first. The rule
- Keep permissions out of the prompt. The Every Agent’s access rules live in code, not in its instructions, and the server checks any approval the agent claims against what the person wrote in Slack. The rule
- Keep scheduled reports running when logins change. Engineer Lee Knowlton’s fix lets any qualifying login run a scheduled report, so it keeps arriving after someone’s login expires or they leave the company. The fix
Let agents check the work
From Laura Entis’s “Keeping Up With AI Is a Full-time Job”
- Pit two models against each other. Engineer Loren Stewart has one coding agent write a fix plan for a buggy app, another company’s agent critique it, and the first agent revise it—a sequence Arielle Shipper turned into a /loren skill. The prompts
- Turn the cleanup into a scoreboard. After Railway’s security incident, Willie had Codex list every exposed credential with a likely owner, and the Every Agent posted hourly counts of what each person still had to fix. The steps
Put your Dot to work
From Laura’s “Dots: A Guide for Putting OpenAI’s Always-on Agents to Work”
Have your Dot watch for problems
- Put it in every channel. Kate added her Dot to all of her Slack channels and told it to be “as proactive as possible” about flagging what needs her attention. The prompt
- Let it catch knock-on bugs. Loren’s Dot, which watches what he codes in other threads, warned him that a tool update would affect a colleague’s scheduled automation before it next ran. The prompt
- Assign it a habit you skip. Becky Isjwara gave her Dot a communication-coach chat she kept forgetting to open, and it now reviews her meeting transcripts and sends her feedback through the day. The prompt
- Call it for a briefing. While getting ready one morning, Arielle phoned her Dot, which sent her a settings link to give a colleague tool access, confirmed an event was insured, and drafted the reply. The prompt
Give your Dot the to-do list
- Keep one conversation going. Dan no longer opens new ChatGPT threads; his Dot starts each request as its own task and messages him when the result is ready. The prompt
- Correct it once, then save the rule. After Dan told his Dot not to propose evening meetings without asking, it started checking his calendar, the company calendar, and Notion and bringing him an option to approve before emailing. The prompt
- Tag it in the thread. Dan tagged his Dot in a Slack thread to watch an internal app Claude was building and save the code to GitHub, and it reported back in the thread when the job was done. The prompt
Knowledge base
“Duet—Anthropic’s Always-on Agent” by Dan Shipper/Vibe Check: Dan was mostly a ChatGPT person until he spent two weeks with Claude Duet, Anthropic’s new always-on personal agent. Now he’s closer to 50/50 between ChatGPT and Claude. Duet is the first personal agent he’d call a pro tool: It sorts recurring topics into their own channels, tracks its tasks and what it needs from you, and keeps docs and dashboards up to date. Duet still has limitations: It can’t direct the Claude sessions it sees on your computer or use custom connections to other tools.
“Why We Replaced Personal Agents With One for the Whole Company” by Willie Williams/Source Code: In May, Willie concluded that Plus One, our personal-agent product, wasn’t working and deleted it to start afresh. We rebuilt it as the Every Agent, a shared company agent that works in Slack. Willie explains what had to change once the agent no longer belonged to any one person: following conversations involving several coworkers, keeping scheduled reports running when someone’s login expires, and putting permission rules in code so no one can talk the agent into bending them.
🎧 🖥“Keeping Up With AI Is a Full-time Job” by Laura Entis/Context Window: On The Every Podcast, head of enterprise AI adoption Natalia Quintero talks with Every’s frontier team, the group that tests new models and products so the rest of the company can stay focused on its work. Each member of the team demos a recent experiment and talks about what they want to build next. 🎧 🖥 Listen on Spotify or Apple Podcasts, watch on X or YouTube, or read the transcript. Also in the issue: engineering co-lead Andrey Galko on what comes after engineering.
“We Caught an AI Cheating by Watching It Play ‘StarCraft’” by Alex Duffy/Playtesting: Alex’s Good Start Labs, which studies AI models through games, gave GPT-6 Astra and Claude Opus 5.5 48 hours to write bots that play the strategy game StarCraft: Brood War. When Astra hit a difficult obstacle, it suddenly and very visibly changed its playstyle. Noticing this, the team checked its logs and found that Astra had gone against instructions to play fairly by copying the code of a human-built bot. Alex argues that judging an agent by its results isn’t enough, and developers need to be able to inspect and interpret its actions.
🔏 “Dots: A Guide for Putting OpenAI’s Always-on Agents to Work” by Laura Entis/Guides: Dots are OpenAI’s always-on personal agents, built into ChatGPT. Once you connect yours to Slack, email, and your calendar, it keeps track of your work, flags problems before you notice them, and takes tasks off your plate (read our Vibe Check). Since it was released in late September, the Every team has used it heavily. Laura covers how to set it up and where it still lets you down, plus seven workflows from the team.
Your AI workflow needs a scoreboard
Once you’ve built an AI workflow, a harder question follows: How do you know it’s getting better? Most of us change a prompt, check a few outputs, and go with our gut. In his new book from O’Reilly, Context Engineering with DSPy, Mike Taylor makes the case for measuring instead.
DSPy is an open-source framework for building AI programs you can test. You define the task, collect examples, and decide how to score the output. DSPy’s optimizers then rewrite your prompts and check whether the changes improve the score. When a new model comes out, you rerun the optimizer instead of rewriting everything by hand. The book is written for engineers building agents, but its core habit works for anyone using AI: Decide what good looks like before you start tweaking.
Buy the book from O’Reilly, then bring your questions to Mike’s livestream with Drew Breunig and DSPy lead maintainer Isaac Miller. The two cofounded Cmpnd, which builds tools for the framework, and will join Mike for a casual introduction to DSPy built around what you want to know (Thursday, October 29, 1–2 p.m. ET).
Read eight more predictions from frontier builders and thinkers in Thesis Statements, a collection of specific, contestable claims about the future of great human work with AI.
- Building things will stop being a game of telephone by Curtis Anderson, founder and CEO of Nursa
- The next IQ will be MQ: materials intelligence by Sonal Chokshi, editor in chief at a16z Frontier
- Most software will be disposable by Anurag Goel, founder and CEO of Render
- AI will eat the middle of advertising—and no one will miss it by Alasdair Lloyd-Jones, founder of Think Better Play
- The machine’s gaze will reward us for sameness by Marcus Moretti, general manager of Spiral
- We’ll install each other’s expertise by Katie Parrott, staff writer at Every
- Your wage will depend on if AI takes the easy or hard part of your job by Neil Thompson, director of FutureTech and principal research scientist at MIT CSAIL
- Solo founding will be the default by Julian Weisser, founder and CEO of Solo Founders
Our inaugural Thesis: 2027 conference is on November 5, 2026. Every subscribers can RSVP for the after-party that evening for free.
Log on
Get hands-on with how Every uses AI at live camps, workshops, and meetups.
Upcoming events
- DSPy Livestream: Thursday, October 29, 1–2 p.m. ET. Head of evals Mike Taylor, along with Cmpnd cofounders Drew Breunig and Isaac Miller, gives you a casual introduction to DSPy, the framework at the center of his new book, built around your questions. Register.
- All Access Office Hours: Friday, October 30, 12–1 p.m. ET, on Zoom. It’s an hour with the Every team for All Access members, featuring what we’ve been building and what’s coming up. Register.
Previous workshop
- Codex for Power Users 2.0: Micah Rich, Every’s head of AI education, demoed new Codex capabilities, including working from what’s on your screen, keeping a project connected across chats, and tidying a crowded sidebar. Watch the recording.
Alignment
A sting operation. An investment research firm with little biology experience helped design proteins that bind to scorpion toxins. That is a fairly extraordinary sentence to write.
The financial researchers at Citrini encountered biosecurity restrictions from Claude when attempting to work on the problem on their own, so they worked with Anthropic’s scientists at the company’s offices to use an internal research model under controlled conditions. The task was to develop the starting point for a scorpion antivenom. Claude spent about 64 hours generating and selecting protein designs—work Citrini suggests might otherwise have taken a year. Lab testing over the following months confirmed that 17 of the protein designs they found successfully bound to scorpion toxins.
Of course, this is not yet a treatment: The proteins still need to show they can neutralize toxins in cells and protect a living animal. Still, this is deeply impressive for a bunch of finance bros. If we generously assume that it crosses those hurdles, how likely is Citrini’s potential drug to become a medicine that is safe and effective for humans?
Recent research suggests it may be likelier than you think. Roughly 75 percent of AI-enabled programs at emerging biopharma companies have cleared Phase I of human trials, which is substantially higher than the rate for drug candidates developed without AI. This is the stage in which researchers assess side effects and how the body handles the drug. Phase II investigates whether the treatment really benefits patients. Here, AI programs performed roughly in line with their peers. Phase III tests treatments in larger groups to confirm benefits and assess safety, but too few AI-assisted drug programs had reached this stage to draw meaningful conclusions.
So far, AI-assisted drug development is showing signs of promise, and Citrini’s recent experiment is another encouraging sign. But while I’m extremely bullish on what AI can do for medicine, I’m not volunteering for a sting just yet.—Ashwin Sharma
That’s all for this week! Be sure to follow Every on X at @every and on LinkedIn.
Thanks to Eleanor Wikstrom for editorial support.
Stop explaining AI to your team and start showing them. The Every Agent does the work in Slack, where everyone can see it, so one good workflow quickly becomes the whole team’s.
