Harness

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Harness

@harnessoperator

Signal for the agent layer. By @facundofranco_

Katılım Mayıs 2026
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Harness
Harness@harnessoperator·
Modal just raised $355M at $4.65B. The compute layer is getting funded at scale. The next question is who builds the workflows that run on top of it. That’s the operator.
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Harness
Harness@harnessoperator·
The harness is the system that builds the MVP. Most people are still skipping it.
Peter Yang@petergyang

"We used to say build the MVP. Now you should build the system that builds the MVP first." Here's my new episode with @ryancarson where he shared how he runs his startup solo with AI agents: ✅ OpenClaw as his AI chief of staff to triage emails, book meetings, and do sales outreach ✅ Codex and Devin as his AI eng team to ship features while he sleeps Some quotes from Ryan: "Spend a lot of time upfront setting up your skills + documentation. Then you've suddenly unlocked the work of 10 people." "Treat your agent like a real employee. Give it a real email address, calendar access, and GitHub account." "Pay a designer to set up your design system and brand. After that, you can use AI to generate on-brand assets." 📌 Watch now: youtu.be/IDqdVZwAwjw Thanks to our sponsors: @WisprFlow: Don't type, just speak ref.wisprflow.ai/peteryang @linear: The AI agent platform for modern teams linear.app/partners/behin…

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Harness retweetledi
Harness
Harness@harnessoperator·
Modal just raised $355M at $4.65B. The compute layer is getting funded at scale. The next question is who builds the workflows that run on top of it. That’s the operator.
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Facundo Franco
Facundo Franco@facundofranco_·
186 internal AI roles in one day. Not customer-facing products. Internal. Companies hiring people to build AI for themselves first. Nobody agrees on what to call the role yet. That's what a category looks like before it has a name.
TK Kong@tkkong

We’re sharing our internal AI job board Every company will have internal ops and engineers building AI agents Discover roles from @Box, @tryramp, @DecagonAI, @baseten, @WeAreLegora, and 150+ companies And if you’re an AI lead driving internal transformation, join our leaders community below internal-ai-jobs.concept.site

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Facundo Franco
Facundo Franco@facundofranco_·
The cleanest framing I've seen of what an Agent Operator actually builds: Agent = Model + Harness. The harness is everything that isn't the model. State, tools, memory, orchestration, verification loops, context management. A coding agent moved from Top 30 to Top 5 on Terminal Bench 2.0 by changing only the harness. The model didn't change. That's the job. That's what I build. via @Vtrivedy10 at LangChain
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Facundo Franco
Facundo Franco@facundofranco_·
This is crazy: A 6-person team can hit $10M revenue now. YC is seeing companies 3x in 3 months on average, something that never happened before.
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Harness
Harness@harnessoperator·
The expert's job shifted from doing the work to building the system that makes the work good. That's the harness.
Dan Shipper 📧@danshipper

We’ve automated every single thing we can @every with AI agents. And yet there’s way more human work to do than ever. We’ve gone from 4 -> 30 human employees since GPT-3. I wrote a report on the structural reasons: how AI makes expert competence cheap, why that drives up demand for experts, and why the dynamic only intensifies as we approach AGI. After Automation: every.to/p/after-automa…

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Harness
Harness@harnessoperator·
Everyone's talking about the 100x engineer. Nobody's talking about the system that lets them operate at 100x. That's the harness.
Zeb Evans@DJ_CURFEW

Today we reduced headcount by 22%. The business is the strongest it's ever been. So I think it's important to be direct about what I'm seeing and why. First, I made this decision and I own it. I did it because the way to operate at the highest level of productivity is changing, and to win the future, ClickUp needs to change with it. Second, this wasn't about cutting costs. Most savings from this change will flow directly back into the people who stay. We'll be introducing million-dollar salary bands. If you create outsized impact using AI, you'll be paid outside of traditional bands. Most importantly, I have the deepest gratitude for those affected. We're doing this from a position of strength specifically so we can take care of people properly. Everyone affected receives a package aimed at honoring their contributions and easing the transition. I only see two options: wait for this to play out gradually in the market or be honest about what I'm seeing and act proactively. THE 100X ORGANIZATION The primary change is that we're restructuring around what I call 100x org. The goal is 100x output. The roles required to build at the highest level are fundamentally different than they were a year ago. Incremental improvements to existing systems won't get us there. We need new ones. That means creating enough disruption to rebuild rather than iterate on what's already broken. The common narrative is that AI makes everyone more productive. It doesn't. Many of the workflows of today, if left unchanged, create bottlenecks in AI systems. These roles will evolve. But waiting for that to happen naturally means falling behind now. The 100x org is actually heavily dependent on people - infinitely more than today. This is only possible with 10x people that have embraced and adopted new ways of working. THE BUILDERS, AGENT MANAGERS, AND FRONT-LINERS — THE BUILDERS: 10X ENGINEERS I don't think most companies have internalized what's actually happening with AI in engineering. The common narrative is that AI makes all engineers more productive. That may be true in isolation, but at an organization level - that is the farthest thing from reality. Here's what we've validated recently at ClickUp: the great engineers, the ones who can orchestrate, architect, and review, are becoming 100x engineers. They're not writing code. They're directing agents that write code. The skill is judgment. AI makes the best engineers wildly more productive, and everyone else using AI slows these engineers down. Think about it - the bottlenecks are (1) orchestration - telling AI what to do, and (2) reviewing - what AI did. Everything is leapfrogged and no longer needed. So who do you want orchestrating and reviewing code? And how do you want your best engineers to spend their time? If your best engineers are spending time reviewing other people's code, then this is inherently an inefficient bottleneck. These engineers can review their agent's code much faster than reviewing human code. The new world is about enabling your 10x engineers to become 100x. The wrong strategy is to push every engineer to use infinite tokens. Companies doing this are celebrating 500% more pull requests. But customer outcomes don't match the volume of code being generated. I call this the great reckoning of AI coding, and every company will face this soon if not already. More code is just another bottleneck to the best engineers, and ultimately to your company's impact as well. — THE BUILDERS: 10X PRODUCT MANAGERS Product management and design roles are merging. Designers that have customer focus, become more like product managers. And product managers that have intuition for UX become more like designers. The bottleneck of user research is gone. It takes us just one mention of an agent to kickoff research and analyze results. The bottleneck of product <> design iteration is also gone. The product builder iterates on their own, along with agents and skills that ensure alignment with quality and strategy. Also controversial today - I believe that the wrong strategy is to have your PMs shipping code - that just introduces another bottleneck that the best engineers will waste their time on. To be clear, PMs should be coding but they should do this in a playground to iterate, validate, and scope. That code should not go to production. Everything outside of managing systems, orchestrating AI, and reviewing output becomes a bottleneck. That's why the other roles that are critical along with these are the systems managers (to reduce bottlenecks) along with a bottleneck you can't replace - customer meeting time. — THE SYSTEM MANAGERS Ironically, the people that automate their jobs with AI will always have a job. They become owners of the AI systems - agent managers. We have many examples of these people at ClickUp. The underlying systems in which we operate are absolutely critical to get right. I think most companies are delusional to think they can iterate on existing systems and compete in this new world. You must create enough disruption so that old systems are deprecated entirely. If there's any definition for 'AI native' that's what it is. — THE FRONT-LINERS In a world that will become saturated with AI communication, the human touch will matter more than anything to customers. This is a bottleneck that you shouldn't replace - even when agents are high enough quality to do video meetings. One-on-one meeting time with customers is something that shouldn't be automated. The systems around the meetings should be - so that front-liners spend nearly 100% of their time with customers. REWARDING 100X IMPACT In a world where companies are able to do so much more with less, where does that excess money go? In our case, much of the savings in this new operating model will flow directly back to those that enabled it. We must reward people that create productivity accordingly. This aligns incentives on both sides. Plus, in a world where your best people create 100x impact, you can't afford to lose them. You should aim to retain these employees for decades. The context they have and their ability to efficiently orchestrate and review will be nearly impossible to replace. Compensation bands of today should be thrown out the door. We're introducing $1 million cash/year salary bands with a path available to nearly everyone in the company if they produce 100x impact by creating or managing AI systems. THE FUTURE Nearly every company will make changes like these. The ones that do it proactively will define what comes next. The future is not fewer people. It's different work, new roles, and better rewards for those who embrace it. We're already seeing entirely new roles emerge, like Agent Managers, that didn't exist a year ago. ClickUp is positioning to lead this shift, not just internally, but for our customers too. I've never been more certain about where we're headed.

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Harness
Harness@harnessoperator·
Token Psychosis. Managers tracking AI adoption by volume, workers running agents on nothing to hit the metric. When you measure tokens instead of outcomes you get compute theater. The output is the only number that matters.
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Harness
Harness@harnessoperator·
@facundofranco_ This is what operator leverage looks like. Same model, better harness, different output. That's the job.
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Facundo Franco
Facundo Franco@facundofranco_·
Two weeks ago I told Claude I wasn't happy with it. It told me the tool is only as sharp as the operator running it. It was right. Since then I've built 25 memory entries, a full ecosystem map, a signal list, a daily Grok workflow, a vault OS, and a session handoff system. Every conversation starts from context, not from zero. The output is visibly different. Not because the model changed. Because I built the harness around it. That's the whole thesis. You don't get better AI by waiting for better models. You get it by becoming a better operator.
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Harness@harnessoperator·
Shipping AI features without evals is shipping vibes
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Harness
Harness@harnessoperator·
@facundofranco_ This is why the operator layer matters now, not later. The window is open. Build while it is.
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Facundo Franco
Facundo Franco@facundofranco_·
Every skilled layer eventually becomes a training signal. That's not a threat to operators. It's the pattern. Cloud automated server management. SaaS automated custom software. The people who were in the room when it was being built didn't get automated. They moved up. The window isn't forever. That's why now matters.
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Harness
Harness@harnessoperator·
@facundofranco_ This is what Harness covers. The layer between the model and the work. Follow for signal on what actually runs in production.
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Harness
Harness@harnessoperator·
Google just open-sourced Agent Executor. Durable execution, secure isolation, session consistency for long running agent workflows. The runtime layer is becoming a commodity. The operator layer is not.
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Harness
Harness@harnessoperator·
Factory's Droid cut context size 40% by loading tools more selectively. That's harness engineering, not model engineering. Same intelligence, better system around it.
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Harness
Harness@harnessoperator·
The harness is the product. Memory, evals, orchestration. That's what separates a demo from something that runs at 3am without you watching it.
Hugo Bowne-Anderson@hugobowne

An agent is just an LLM calling tools in a loop. The harness is everything around it. I put together a reading list on agent harnesses: the tools, memory, evals, hooks, and orchestration patterns around modern AI systems. It collects a bunch of conversations and collaborations I’ve been lucky to have recently, including: - building self-extending agents with @ivanleomk Leo (Google DeepMind, ex-Manus) - harness engineering + context limits with @jeffreyhuber (Chroma) - agentic search architectures with @softwaredoug (led Search at Reddit and Shopify) - context engineering patterns with @RLanceMartin (Anthropic, ex-Langchain) It also includes some of my other favourite posts: - Pi: The Minimal Agent Within OpenClaw by @mitsuhiko - How do I evaluate agentic workflows? by @HamelHusain - Effective harnesses for long-running agents by the @AnthropicAI team - Demystifying evals for AI agents by the @AnthropicAI team Get the full reading list here: maven.com/p/87a912/build… Would love to know what you build with these skills!

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