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The agent pipeline architecture: a coordinator on top of three specialist agents

The speaker structures the pipeline with a project manager agent (the campaign coordinator) on top and three specialist agents underneath: a prospector, a vetter, and a copywriter. The coordinator interviews the speaker with an annoying amount of questions until the campaign brief is completely over-defined, so the output matches how he would do it. Each agent gets a markdown file of text instructions telling it how to do its job, which the speaker generated using Claude.

Ahrefs · I Used AI Agents to Replace Our Marketing Agency

Flared elbows on bench press aggravate the bicep tendon

Flaring the elbows out on a bench press causes more internal rotation at the bottom, drifts the bar forward, and aggravates the bicep tendon. Pulling the elbows in closer and dropping them at a 45-degree angle to the sides takes stress off the bicep tendon.

ATHLEAN-X™ · 7 Exercises That Are Making Your Shoulder WORSE

Narrow bench press grip preserves scapular retraction

A narrower bench press grip lets the shoulder blades pinch back more, allows the arms to drop back, and keeps the blades retracted during the press. Going extra wide prevents that retraction, which is needed to stabilize the shoulder blades for a proper bench press.

ATHLEAN-X™ · 7 Exercises That Are Making Your Shoulder WORSE

Focus on your body to signal safety, and conversation techniques follow naturally

The narrator's key integration advice: the best way to incorporate everything is to focus on your body. Do everything you can to signal to yourself that you are safe — sit back, turn palms up, lift your chin, take a deep breath, and hold the posture you'd have in a friendly conversation. Changing what your body is doing makes all the tips follow naturally without a checklist.

Charisma on Command · When Someone Intimidates You, Do This...

Responding to conflict with openness and gratitude instead of defensiveness wins people over

When Mike Tyson confronted Theo, Theo didn't get defensive — he played along, expressed gratitude ('Thank you for spending time with us, and with me'), and this was the moment that won Mike over. The narrator highlights that personalizing gratitude and responding to a vulnerable compliment with appreciation deepens connection.

Charisma on Command · When Someone Intimidates You, Do This...

Letting the intimidating person talk and reflectively listening makes them feel understood

The narrator's analysis of what worked in Theo's interview with Mike: Theo let Mike talk without interrupting, and repeated back or paraphrased what Mike said, which made Mike feel not only heard but understood. This also helped Theo stay present and focus on Mike rather than on himself.

Charisma on Command · When Someone Intimidates You, Do This...

Contact exchange is not a locked-in date — keep pushing to set one up

A woman showing some interest (giving her number and IG) is not the same as a locked-in date — you still need to actively set up the date in the interaction. Even when a woman is showing interest and gives her contact info, keep pushing to lock in a concrete date plan during the interaction rather than settling for the number alone. After exchanging numbers and agreeing to meet up, talk about what you're going to do on the date instead of continuing geography talk; continuing neutral geography/weather talk after number exchange means missing the opportunity to lock in a date.

Coach Kyle · How to Talk to Women in Public: Real Infield Breakdown

Diagnose blockers, then encode context into skills or plugins

When Codex gets blocked, Charlie Guo recommends asking what blocked it — missing context or permissions — and building that context into repeatable tools like skills or plugins it can use next time.

AI Engineer · Cooking with Codex — Charlie Guo & Gabriel Chua, OpenAI

Understand when to reach for each flavor of Codex app control

Charlie Guo argues it is important to understand when to reach for each flavor of Codex app control, and lays out the tradeoffs: the in-app browser has deeper hooks into the browser engine than the Chrome extension (so Codex can see further into page layout) and suits building local web apps and deep testing of interaction design and rendering; the Chrome extension is most useful when you need to hand off state or authorization to the agent, since with your permission it can read sites as you and borrow your credentials, removing the authentication step; and computer use works for anything on your laptop but really shines with applications that lack a programmatic interface — if a web app's API calls can be reverse engineered you don't need it, but lots of desktop and legacy software doesn

endorsed

AI Engineer · Cooking with Codex — Charlie Guo & Gabriel Chua, OpenAI

Agents compound error risk: judge intermediate steps

An agent makes a series of tool calls and decisions, so at every step the chances it has made a mistake get higher. You have to judge the intermediate steps, not just the final output — checking whether the agent uses the right tool, passes the right arguments, and knows when to stop (getting stuck in loops is a real failure case).

AI Engineer · From Vibes to Production: Evaluating and Shipping AI Agents That Work 101 — Laurie Voss, Arize AI

Diverse test data catches what your agent is bad at

Without a diverse body of test data you won't catch what your agent is not good at: users will ask unpredictable weird things, so include different tickers, question types, complexity levels, low-data companies (Rivian), and varied analysis types (dividend yield vs growth stock). Repeating the same ticker with different questions reveals non-deterministic output differences.

AI Engineer · From Vibes to Production: Evaluating and Shipping AI Agents That Work 101 — Laurie Voss, Arize AI

Investment hierarchy when iterating: data quality first, then prompting, then model selection; hyperparameters last

Laurie Voss describes a hierarchy of where to invest when iterating: data quality fixes have the highest impact — if your agent is searching the wrong sources or your knowledge base has stale information, no amount of prompt iteration will help, so fix the data first. Prompting improvements are next: few-shot examples, explicit instructions, and constraints on what the agent should and shouldn't do are often the highest-ROI change to your agent. Model selection comes third: sometimes a more capable model solves problems that prompting can't, but it will also cost more and be slower. Do not spend a lot of time on hyperparameter tuning like temperature and top P — they very seldom make a high-leverage change compared to prompting or improving data quality; you can try them, but they shouldbe

AI Engineer · From Vibes to Production: Evaluating and Shipping AI Agents That Work 101 — Laurie Voss, Arize AI

Fix data quality first, then prompting, then model, then hyperparameters

Iteration hierarchy: fix data quality first (wrong sources or stale knowledge defeat any prompt iteration), invest in prompting second (few-shot examples, explicit instructions, constraints — often highest ROI), model selection third (more capable models solve what prompting can't but cost more and are slower), and hyperparameter tuning last.

AI Engineer · From Vibes to Production: Evaluating and Shipping AI Agents That Work 101 — Laurie Voss, Arize AI

Traces reveal every decision the agent made

Without traces all you see is 'I gave it a prompt and I got a report'; with traces you see every step and where exactly it went wrong. SDKs like Claude Agent SDK and OpenAI's already emit LLM and tool call spans, and OpenTelemetry/OpenInference means you don't write custom instrumentation per SDK.

AI Engineer · From Vibes to Production: Evaluating and Shipping AI Agents That Work 101 — Laurie Voss, Arize AI

Rubric criteria must be specific and observable, derived from error analysis

Don't say 'a good response' in your rubric because that is an aspiration, not a criterion. Instead, list exactly what makes a report actionable and not actionable. Every criterion should be specific and observable — something a human looking at the report could say yes or no to. Each rubric criterion should match something you observed going wrong in your trace analysis; your error analysis is what tells you what criteria to write. Don't come up with rubric criteria from whole cloth; get them from reading your traces and noting the specific wrong things your agent did. Specific criteria produce consistent judgments, while vague criteria produce coin flips.

AI Engineer · From Vibes to Production: Evaluating and Shipping AI Agents That Work 101 — Laurie Voss, Arize AI

When transcript and audio disagree, trust the audio

The source advises that when a voice agent's transcript and audio disagree, trust the audio, and that when a transcript shows a successful confirmation, play the audio to verify the confirmation actually occurred.

AI Engineer · The Transcript Looked Fine. The Call Wasn't. — Debugging Voice Agents, Arize

Rubric guiding principles

Grade only what was asked and not any extraneous criterion; keep criteria isolated so errors don't cascade from one to another; always look at the ground truth screenshots because agents often overconfidently claim they did something they didn't; and separate what the agent could control from what it could not, since the browser is an environment the agent doesn't always control.

AI Engineer · Your LLM Judge Is a Confident Liar: Building Better Verifiers — Browserbase

Loops: repeat a task until told to stop or a timer expires

A loop repeats a task nonstop until you cancel it or a timer you set expires — more creative and adaptable than goals. Examples: run tests every minute, check the deploy and ping when healthy (or check every five minutes and act, e.g., run an additional script), tell me when my favorite video game is available and text me, go through Slack filing customer complaints as Linear tickets, babysit a PR and fix comments until there are none. Loops are incredibly reusable; you could split a complex workflow into two loops; regular reports fit schedule better.

AI Engineer · Lifestyles of the AI-Native — Nick Nisi & Zack Proser, WorkOS

Goals: tangible, measurable targets the agent works toward and stops at

A goal is a clear, tangible, measurable definition the agent works until met, then stops — no reprompting; the agent reprompts itself, checks whether it reached the goal, fixes what it can or its approach, and loops. Examples: make all tests green, get to 90% test coverage, every file in src under 200 lines, watch a website or flight price until it changes. Goals bring back red-green-refactor: reproduce a bug with a failing test, implement the smallest possible fix, lint/type check/tests green, then open a PR. You can add extra steps like adversarial checks or diffing based on another model's review.

AI Engineer · Lifestyles of the AI-Native — Nick Nisi & Zack Proser, WorkOS

Hooks: things the agent can't avoid or forget

Use hooks for things you want the agent to always remember and do and never be able to forget. Hooks run automatically in certain cases — as bash scripts they can always run but only proceed if a specific case exists. They force the agent through gates (e.g., run linting after writing updated files) and it won't stop or forget them. You can just tell Claude to add a hook and it will do it correctly; adding hooks has been bearing the most fruit in the last three to four weeks of experimentation.

AI Engineer · Lifestyles of the AI-Native — Nick Nisi & Zack Proser, WorkOS