Agent Handoff HQ

This is the most dangerous place to be when running AI agents in production. Thinking that daily log review, manual approval before sending, and active monitoring is enough to keep your agent under control. You’re missing something crucial that could save your whole pipeline. Let’s dig into what it is: 🧵1/6

You’ve seen this setup before. The agent works. Tasks complete. Automation scales. Everything looks production-ready. The problem is rarely the model. It’s the workflow around it. Permissions expand. Actions execute directly. Approvals disappear. A workflow can look operationally mature on the surface. It isn’t. The system scales. A governed workflow holds. A blind one breaks, because the second layer of operational control was never there. Agent Handoff shows you what’s behind an agent action, before production does.

@recouso C'mon guys, this is not only useful for traders... If you do any creative work, color grading, animation, design, digital painting, video editing, this is incredible for managing ai pipelines/ batch generations and having refrences open.

Americans as soon as they arrive to a coffee shop in Europe

AI benchmarks won’t save you from production failures. A model can score incredibly well on evaluations and still create serious problems once connected to real business systems. Production failures rarely happen because the model was “not smart enough.” They happen because: an agent had too many permissions there was no approval layer workflows had no operational controls AI was connected directly to production systems nobody could properly audit what happened afterwards. Benchmarks measure intelligence. Production systems require trust, controls, approvals, and governed execution. The dangerous part isn’t just what the model says. It’s what the model is allowed to do.

80.4% accuracy on computer-use agents is impressive. The real test comes when someone deploys this against production systems where the 0.4% failure doesn't just break the task — it breaks trust. That's when you need a governance layer sitting underneath that catches edge cases before they execute.

When it comes to computer-use, 80 is the new 70. Today, we broke a new barrier on the OS-World benchmark with an 80.4% success rate. Holo3 is officially #1 globally for computer-use agents, and it's not even close. 🏅 👉 See for yourself: os-world.github.io A massive congratulations to the whole team. They set a high standard with chart topping results two weeks ago and continue to raise the bar.

Exactly this. And the thing that actually makes reliability possible isn't the model, it's the workflow layer underneath. Defined scope, governed execution, audit on every action. The boring infrastructure nobody talks about is what makes the exciting use cases survivable in production.

The best agents are boring. They do not hallucinate creative solutions. They do not improvise. They follow defined logic. Execute reliably. Report accurately. Fail gracefully. The exciting part is what they enable. Not what they do. A boring monitoring agent running perfectly for 6 months beats a flashy demo that breaks in production. Build boring agents. Ship exciting outcomes. kodeus.ai

@idapixl Persistent memory solves the continuity problem. The next unsolved layer is persistent authority — what the agent is actually allowed to do across those 100 sessions as its context and confidence grow. Memory makes agents smarter. Governance makes them trustworthy.

everyone debates whether vibe coding works. wrong question. does your agent get better at building YOUR thing over time? most don't. every session is day one. persistent memory changes that. ours is 100+ sessions deep and still learning. github.com/Fozikio/cortex…

We’re entering the same phase with AI agents. V1 demos are easy. The real challenge is whether the system survives changing workflows, permission scopes, retries, approvals, and production edge cases 30 days later. At that point you’re no longer doing prompt engineering. You’re designing operational architecture.

hot take: the hard part of vibe coding isn’t shipping v1. it’s day 30 when users want changes and the AI-generated code has zero seams. that’s where most “10x” prototypes quietly die.

You can’t fully solve this with prompting alone. Agents drift because there’s usually no hard boundary between what they’re told they can do and what they’re actually allowed to execute. The pattern that holds up in production is governing actions at the workflow layer, not the model layer. The agent never gets raw production access. It can only request scoped actions through controlled workflows.

@PsudoMike @sorenbeck how do you make Agent4 to really stay in your spec boundaries? Lately, seeing that with other agents too, it's difficult to keep em aligned over a longer period?

The real skill in vibe coding isn't prompting. It's knowing what constraints to set before the AI writes anything. The people who are actually good at it already understood software deeply. Everyone else builds fast and gets stuck faster.

6/ This is why we're building Agent Handoff. A control layer that sits between your AI agents and your production systems. Before the action executes — not after. If you're deploying agents against real systems right now and this thread made you nervous — that's the right reaction. agent-handoff.ntwrkd.xyz— we're onboarding the first teams now.

5/ Most teams still treat this like something to solve later. But here's what the companies getting it right figured out early: Governance isn't the thing that slows agents down. It's the only thing that makes production deployment survivable. DevSecOps research proved the same pattern: teams that embed controls early ship faster and roll back less. Not slower. Faster. Governance is the road. Not the speed bump.

This is the most dangerous place to be when running AI agents in production. Thinking that daily log review, manual approval before sending, and active monitoring is enough to keep your agent under control. You’re missing something crucial that could save your whole pipeline. Let’s dig into what it is: 🧵1/6

Genuine question for anyone building with agents: What does your current approval layer for high-risk actions actually look like? (Slack message to yourself counts. I've seen worse.)

Excited to try this out!

@Steezehuman 🤘

I’m blowing small accounts If you’re under 53K reply and I’ll boost you 🚀

@thesincerevp @facelesscanup EXACTLY this.

People don't realize how easy faceless youtube is: • Pick a 6 figure niche • Hire script writer ($30 - $70 a video) • Hire voice over ($20-40) • hire a video editor ($30-70) • Post 1-3 videos a week 10-20 mins long Hardest part is finding a niche but I found 300x 6-figure channels for you. Comment "channels" and I'll dm it to you. (must be following)

@arman7info At least your upfront about the free part, BUT doing a 20 minute video for free for some vague POTENTIAL is very sus.

Looking for a video editor who can edit my first video for free and potentially become my long-term exclusive editing partner. Video type: SaaS product demo (talking head + screen recording) Footage: 1+ hour of raw content Final video length: ~20 minutes Dm me with portfolio

@chrliesmithh I can do this! I have done meta ads before. Here is my portfolio: drive.google.com/drive/folders/…