# FORGET Hermes & OpenClaw! My NEW AI Agent does it All!

Channel: Parker Prompts
Video: https://www.youtube.com/watch?v=ZUTOm0GrdHU
Duration: 9 min
Language: English
Words: 2411
Transcript page: https://viewrankai.com/tools/youtube-transcript/ZUTOm0GrdHU

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[0:00] Most people think an AI agent is just a chatbot that runs a script. Mine has its own computer and tracks my competitors without me writing full breakdowns as it goes. And 5 minutes ago, none of that existed. But an agent I set up last week built the whole thing while I got a cup of coffee. So now, you're probably assuming I'm running one of the big open-source agents for this, Hermes or Openclaw. And I did try both, and they're impressive, but a few things

[0:20] just didn't fit the way I actually work. The one that did is newer, it's called Hyperagent, and it works in a completely different way. So I'm going to build one of these agents live, and you can decide which is right for you. The idea behind Hyperagent is that you build agents that go off and do the work, and every agent you make gets its own computer in the cloud, so it can go and do things out in the world instead of [music] just describing them. It's built by the team at Airtable, and it started as a tool they used to run their own company. So by the time it became a product, it had

[0:45] already been running a real company's work for months. So the best and the fastest way to show you what it can do is to build one agent. I want a market watcher, something that keeps track of my competitors and tells me the moment something worth knowing changes, [music] and I'm going to call it Scout. I don't even write a system prompt or wire up any tools myself. I open a new thread that's just a work session in Hyperagent and describe the job in a couple of sentences. I type, "You're my competitive intelligence analyst. Watch these five companies and my market, and when something meaningful changes, dig

[1:11] into it and tell me plainly what it means for us." It asked me a few quick questions to fill the gaps, which companies to watch and what level of change should trigger an alert. Then it builds the whole agent for me, and when I open Scout's page, everything it's built from is laid out in tabs. Under identity is the system prompt it wrote from what I described, and if it ever gets something wrong, this is the one place I fix it in English and it holds from then on. Under model, I pick which model it runs on. I've left Scout on GPT, and if I ever want a different one,

[1:36] it's a single switch right here. Then there's tools, where I switch on the ones Scout needs, web search and documents, so it can save what it finds, and every agent comes with a real browser built in, so it can open a competitor's pricing page and read it itself. So from three sentences, I've got a working agent with its own computer, ready to go and check things in the world rather than guess from training data. And let me just test it right now. I point it at one competitor and watch it go read their pricing page itself, then come back and tell me

[2:00] straight that nothing meaningful moved. Then I push it a step further. I pointed at two competitors at once and ask which of the two moves matters more to us. And instead of dumping both on me, it sizes them up against our own strengths and tells me which one to worry about and why. You can watch it open both pricing pages, pull the numbers up side by side, and flag the one that moved against what we charge. And a normal scraper would just hand me the raw changes and leave the thinking to me. This does the thinking, too. But that was me telling it what to do. And an agent that only

[2:26] works when you prompt it is barely a step up from a search box. So, the question that matters is whether it can run without me, and that comes down to two things: skills and memory. A skill is something you teach it once and it never forgets, and you add them under the skills tab. So, I give Scout the way I personally judge a competitor's move, the questions I always ask first, what counts as a threat versus something not worth my time, and my own positioning to weigh it against. I load into its knowledge so it can pull from them

[2:49] anytime. That becomes a reusable skill. So, every time Scout looks into something, it reasons the way I would. And I can hand that same skill to any other agent on the team, so they all judge things by one standard instead of each doing its own thing. And they stack. The more of these I teach it, how to read a pricing page, how to tell a launch from a quiet test, the sharper its judgment gets. So, a bit of setup up front turns into an agent that already thinks the way I do. The second piece is memory, and you turn it on under the knowledge tab, where I've set it to auto

[3:14] save. Now, Scout starts remembering the things that matter as it works, like which competitor always leaks a launch on a Thursday, or which pricing page is the one actually worth watching. So, it stops treating every little change as equally important. And I can add my own memories in there, too, the things I already know it should never forget. And for each one, I set what kind of memory it is, how important it is, and whether it's just for Scout or shared with every agent I've got. So, I'm the one shaping what it remembers. That's what stops it starting from scratch every time, which is the quiet thing that trips up a lot

[3:41] of agent setups, where every session forgets the last one and you re-explain yourself forever. A month in, Scout has built up a working read on my market, and it keeps getting sharper. The longer it runs, the better it gets, because it never loses what it figured out yesterday. But even with all that, it still waits for me to go and open it up and ask. And the thing that finally changed that, the part that made me stop testing it and start trusting it, is next. It's called live mode, and you set it up under invocations. It reports back to me in Slack, so that's the one

[4:06] connection I set up first, and it only takes a minute. I authorize my Slack workspace, give Scout its own identity, and drop it into the channels I want it in. So, anyone on the team can pull it in with an @mention, and it answers right there in the thread. I tell Scout, "Check those five companies and the market." And if something's really

[4:20] changed, dig in and post me a write-up. And if nothing has, say nothing. You pick the model it runs on, where the runs land, the Slack channel it should post to, and how often it runs. The setting I love is what I've started calling silent unless it matters mode, because a normal alert tool would ping you all day. Scout only speaks up when there's something you'd actually want pulled away from your desk for. And when it does speak up, I can open the thread and see its reasoning, the pages it

[4:42] checked, the steps it took to get there. So, when it tells me something changed, I can see exactly how it knows. Because it runs in Slack, this all happens where my team already is. I can mention it in a channel with a one-off question, and it answers in the thread like anyone else would. Then that same kind of report shows up right in the channel, Scout walking through what actually changed, and exactly what I should do about it. One morning, the biggest name in the space made a real move. Google folded Notebook LM into Gemini Notebook, 30 million users, and started wiring in search distribution. Scout caught it,

[5:09] worked out what it meant for us, and laid out the response. Lean into fast, verifiable document workflows, and sharpen our starter plan, rather than trying to be generic PDF chat. So, I gave it the scenario I actually worry about. quietly cutting their entry price by 20% and burying it in a pricing page tweak nobody announced. It worked out that it would undercut our own starter plan, and the webpage it posted laid out exactly what changed, who it threatened, and the two moves it thought we should make, with the competitor's page linked underneath so I could check it in one click. I read the whole thing in about a

[5:37] minute. I'd have gone with one of its two suggestions, and had our response moving before I'd even finished my coffee. And the work that used to take me half a morning is just done and waiting the second I open Slack, without me asking for any of it. And one agent doing that on its own is the part that got me, but it isn't where this actually points, because the moment you've got one running itself, the obvious move is a second and a third, and that's when it stops being a tool and starts being a team. So, under Scout, I've got an inbox agent that clears the overnight pile and

[6:02] hands me the things that actually need a human, and a content agent that turns anything I hand it into posts ready to go out, each with its own computer, skills, and memory. So, I gave the content one a topic and walked away and came back to a LinkedIn post, a short thread, and a couple of pull quotes to record, all sitting in a doc for me to look over. I never wrote a prompt for any of it. The one job description I gave it at the start was enough. That's two completely different jobs, watching the market and turning a topic into content, running side by side out of the

[6:28] same setup, and neither one needs me until the very end. And they pass work between themselves. When Scout finds something big, it can hand it straight to another agent, and that second agent picks it up [music] and builds the response, with no me in the middle carrying messages back and forth. It starts to run like an actual team. One spots a competitor's move, the reply gets drafted and scheduled without me touching it, and I just approve the final version at the end. I'm only relaxed about letting them run like that

[6:49] because I never hand over full control. The small stuff they just fix and carry on, but anything with judgment in it, a change in tone, a reply that could land wrong, gets posted to me first and waits [music] for a yes. So, the boring, repetitive 9/10 runs itself, and the 1/10 that needs a human still comes to me, which is the only way I'd trust something like this on live customer emails. You keep an eye on all of them from a command center, where you can see what each agent is doing and score its work against a rubric you write. So, the whole lineup gets sharper over time

[7:15] instead of sloppier. And this isn't just my setup, the companies already running it are the convincing part. Inside Airtable itself, an agent answers questions across a data catalog of over 70,000 entries in Slack, saving their team around 200 hours every week. So, this is a tool a working company already runs itself on. A co-working company called Deskimo built an eight-agent sales pipeline in about a week that runs its prospecting, outreach, and follow-ups on its own, and gets better

[7:38] every week off its own feedback loop. The way that loop works is the part worth slowing down on. Every night that Pipeline lines up the emails it drafted against the ones the founder actually sent, reads the gap between the two, and adjust the next batch with no prompt rewriting anywhere. In 3 weeks that took him from editing every single outreach email to approving 80% to 90% of them untouched, purely from the agents watching how he corrected them. Because all eight share one memory, a lesson one of them learns lands on the rest at once. When their checker found certain email domains were bouncing most of the

[8:06] time, the agent doing the sourcing just stopped pulling them without anyone stepping in to say so. It's loop I put on Scout, only running across a whole sales team instead of one market watcher. And it's the clearest sign I've seen that these things compound instead of going stale the week after you build them. So, back to the question in the title, is it better than Hermes? And the honest answer is they're built for different people. Hermes is open source and you run it yourself, which is the appeal if you want to own every part of it, keep it private, and pay nothing to run it. And it's a solid tool. But that

[8:33] also means you're the one hosting it, updating it, and keeping it running, and it's really one agent that you drive. Whereas HyperAgent has nothing for you to run. Every agent gets its own computer the moment you make it, and you're building a whole team of them that work in your Slack and hand you finished output. So, if owning and running it yourself is the goal, Hermes is a fair pick. But if you just want the work getting done without having to run and maintain all of it yourself, HyperAgent is the stronger one. And for

[8:55] what I actually wanted, it wasn't close. It's the one I stuck with. And the fastest way to feel that difference is to build one yourself instead of watching me. So, use the first link in the description to sign up for HyperAgent, and the first 500 people through that link get $500 in credits to build with. So, don't wait around on this one. Thanks for watching, and I'll

[9:11] see you in the next one.

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