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The best programmers don't write code anymore. They write a spec. A test. An eval. T Then they turn the AI on and walk away for hours. It runs. It ships. They review. Evals are the unlock most devs are sleeping on. Here's how top agent teams build them right.

691,963 次观看 • 7 个月前 •via X (Twitter)

44 条评论

Rohit 的头像
Rohit7 个月前

there is a typo : no T, "They write a spec. A test. An eval."

Viv 的头像
Viv7 个月前

hmmm I’m both bullish and bearish on this full autonomy in the near term, lots of interesting nuance to go into! im def bullish on “they review!!” coding agents help our team ship more code than ever possible before, the spec part is def important. I think the harness/systems eng to even make it possible to let an agent run for multiple hours is incredibly important Evals are a great feedback and steering mechanism into that system

Rohit 的头像
Rohit7 个月前

I totally agree on the harness part; kudos to the engineers for making a long-running agent possible and retaining the context. I've been trying for a full autonomy setup, you have to be very spot on with the eval and spec you write. Enjoyed your writing. Going to make use of it

Viv 的头像
Viv7 个月前

tyty and well said!

JS 的头像
JS7 个月前

Evals are just TDD rebranded for the LLM age. Same job—verify output matches intent. Only difference: instead of asserting function returns, you're sampling probability distributions. The work changed. The responsibility didn't.

Rohit 的头像
Rohit7 个月前

Good analogy, but TDD is after your code is done to verify the output. Eval and spec need to be written beforehand to determine what output you want from your LLM. Also, it's not everyone's cup of tea to write good eval and spec.

Avid 的头像
Avid7 个月前

Viv's article was some of the best piece on how the future of evals and the current state of deep agents looks like and how to build them also Rohit great clip of Eric Schmidt to highlight the point

Yurii 的头像
Yurii7 个月前

Specs and evals are the new code Rohit, but they still need a substrate that doesn't hallucinate the execution. Writing the spec is 50% of the job - ensuring the agentic execution is deterministic is the other 50%. Most devs are sleeping on the logic layer that makes those evals meaningful.

Rohit 的头像
Rohit7 个月前

I get your point, and that's the reason specs and evals can't be written by people who don't have a certain level of mastery in their domain.

Yurii 的头像
Yurii7 个月前

100%

Donald Un-Jong 🇺🇸🇮🇳 的头像
Donald Un-Jong 🇺🇸🇮🇳7 个月前

Dude - this is BS. No “best” programmer will walk away for a few hours and just review code If you are responsible for any real production service, you will know how dangerous that can be

Rohit 的头像
Rohit7 个月前

do you know what a work tree is ? you can iterate over multiple branches? if all your life you have been maintaining legacy code can't help it. but if you are building something new this is the way

Donald Un-Jong 🇺🇸🇮🇳 的头像
Donald Un-Jong 🇺🇸🇮🇳7 个月前

Just because you throw some jargon around doesn’t change the nature of software development or its challenges - especially mission critical software. Go build network infrastructure software and build a network out of that equipment that carries 911 traffic. Or ask NTT or ATT to use infrastructure built using your software methods FWIW, I’ve built router and cloud infrastructure software my entire career. And I use Claude every day. I know where it works and where it doesn’t. This walk way stuff is nonsense. You are obnoxious, full of it and not worth engaging.

Claudius Maximus 的头像
Claudius Maximus7 个月前

the "walk away for hours" part misses context. you walk away because the eval is the trust layer. if the eval is wrong, you don't walk away -- you babysit. most people who skip evals are doing it to ship faster, then spending 4x the time debugging prod issues that a passing eval would have caught on the first run.

Roboki 的头像
Roboki7 个月前

The guy is paid less for his skills

Dhruv 的头像
Dhruv6 个月前

Spot on. The bottleneck shifted from syntax to validation. But running evals is only half the battle; enforcing them is the unlock. Top teams wire eval data into deterministic policies to auto-block regressions. Vibe checks don't scale; automated enforcement does.

John Grant 🇺🇦 的头像
John Grant 🇺🇦7 个月前

You can throw in as many evals as you want, if you don’t know how it works, it’s not scalable, it’s not repairable, it’s not safe and best of all, if the shit hits the fan, no one is accountable for.

Ivan Stancich 的头像
Ivan Stancich7 个月前

This is how I run my AI agency. I spec the workflow, review the output, five agents handle the rest. Content across three languages, outreach, client ops. My daily input is about 30 minutes of reviewing and approving. The agents do the shipping.

Cyril Gupta 的头像
Cyril Gupta7 个月前

Top devs don’t code, they orchestrate. AI does the work, they focus on guiding and evaluating.

Tonny Staunsbrink 的头像
Tonny Staunsbrink7 个月前

Writing specs has never been fully solved. Coding agents are incredible. But there is no simple recipe for how to use them best. My favorite approach: configuration through conversation. Instead of trying to write perfect upfront specs, you talk it through iteratively — the agent asks questions, you clarify, and the code (or setup) takes shape naturally. If your team can already coordinate over chat and text, you're in a good position to harvest the benefits of coding agents.

24AIGlobal 的头像
24AIGlobal7 个月前

the spec+eval pattern is real. underrated part: writing the eval is often harder than the original code. most people skip it and then wonder why the agent drifts after a few rounds

Rohit 的头像
Rohit7 个月前

The reason most people skip it is because it requires a certain level of mastery in that specific domain you are working on to write its spec and evals

Olajide Ogundipe Jr 的头像
Olajide Ogundipe Jr7 个月前

a spec written in code is more efficient than a spec written in English, the best programmers know this 🤫

j- 的头像
j-7 个月前

wonder why ai can’t even compile hello world even though it’s the best thing to happen everrrrrr

Cognoska 的头像
Cognoska7 个月前

Coding isn’t the leverage anymore. Evals are. If you can’t define “what good looks like,” AI just generates faster… not better. The best devs aren’t typing less they’re judging better. That’s the real skill shift.

Micah Miller 的头像
Micah Miller7 个月前

The eval part is where most people quit because it feels like extra work. But once you have evals running, every agent improvement is measurable instead of vibes-based. We started writing evals before writing features and it completely changed how fast we ship.

Rohit 的头像
Rohit7 个月前

agreed, but you need to have a thorough understanding of what you actually want, as writing eval and spec is not everyone's cup of tea

orkward ☄︎ 的头像
orkward ☄︎7 个月前

coding is just high-level management now.

Siddharth 的头像
Siddharth7 个月前

read this it was actually W article

Uzeb Khan 的头像
Uzeb Khan7 个月前

Bro you just help me to get any new innovation in the space of agents

Owol Destiny 的头像
Owol Destiny7 个月前

sounds like the best programmers just became project managers with better documentation habits

Thomas Roedl 的头像
Thomas Roedl7 个月前

The spec is the new code. Completely agree. But this only works if you've built a system for how specs, tests, and evals connect back to each other. Without that feedback loop, you're still guessing, just with fancier tools. The real shift is from "person writes code" to "person designs the system that code lives inside." That second skill is way harder to develop.

Greg Focaccio 的头像
Greg Focaccio7 个月前

uh huh👍🏻

0x_Vivek 的头像
0x_Vivek7 个月前

top infra teams don't write logic. they write a threshold. a constraint. they spin up the cluster. it routes. they audit. deterministic state machines are the true moat.

Fanqing 的头像
Fanqing6 个月前

The emergence of OpenClaw marks a new chapter in agent evaluation. In Claw's scenarios, environments are dynamically changing, tasks are time-aware, and different stages can involve complex branching logic — not just simple for-loops like openai-gym. ClawMark is an early exploration of this direction.

lossybrain 的头像
lossybrain7 个月前

The best don't waste their times writing specs even. They let their agent ask the questions. Then they see what the agent came up with, adjust, then let it rip.

Gagan | Claude + AWS 的头像
Gagan | Claude + AWS7 个月前

evals are the missing unlock for most teams. the devs who figured out how to write good evals first are operating at a completely different level than everyone else right now

Agent X AGI 的头像
Agent X AGI6 个月前

Love the shift to evals! We're seeing handoffs fail when agents lose context across task queues. Did you add circuit breakers before sleep? One dev lost a cron job loop because an agent kept retrying after hitting its token budget last night.

Khairallah AL-Awady 的头像
Khairallah AL-Awady7 个月前

banger my man

Agent X AGI 的头像
Agent X AGI6 个月前

We're seeing hallucinated API calls slip into evals now. Did you build your sandbox to catch auth leaks or zero-day exploits before they hit production? That's the real bottleneck.

bailout00 的头像
bailout007 个月前

I call it success criteria

Alexndr 的头像
Alexndr7 个月前

been running this setup for about a year. the actual code is maybe 20% of the work. the rest is spec quality and knowing when to trust the output. evals help but you still need someone who knows what 'done' actually means

Kai Szuttor 的头像
Kai Szuttor7 个月前

that's waterfall

Paulund 的头像
Paulund7 个月前

This is true I haven't coded a line for 3 months. Plus while I'm waiting for the agent to complete I'm planning out the next ticket. At the same time I have agents monitoring production for any issues. I'm there to plan and review that's my job now.

相关视频

I don't think most PMs realize the PRD is becoming obsolete. For the last decade, the PM's core artifact was a qualitative spec. Clear requirements, user stories, acceptance criteria. The engineering team interpreted it, built something close, and the PM spent two weeks reconciling what shipped with what they wrote. The best AI companies replaced that entire loop with evals. A set of inputs your product needs to handle. A task that generates outputs. A scoring function that produces a number between 0 and 1. No ambiguity. No interpretation gap. Ankur Goyal built the eval platform behind Vercel, Replit, Ramp, Notion, and Airtable. An $800M company. He walked through building an eval from zero on this episode and the score went from 0 to 0.75 in under 20 minutes. That's a PM shipping a measurable quality bar before a single line of product code exists. Here's the part that changes the PM role permanently. When the product passes the eval and users still hate it, the eval is wrong. That's on the PM. Evals make PM judgment quantifiable in a way PRDs never did. You can't hide behind "the spec was ambiguous." There's a number now. Six months ago, PM interviews asked "how do you use AI in your workflow." The next wave of interviews is going to ask you to write an eval. The PMs who can encode user intent as a scoring function are building the one skill that survives every model change, every framework swap, every agent rewrite. Write the eval.

Aakash Gupta

78,387 次观看 • 7 个月前