Agentic Brand Operations: When the Back Office Runs Itself
Some of the most interesting thinking gets smoothed out in the editing process. The hedged answer becomes the clean takeaway. The half-formed idea that was actually going somewhere gets cut for length. We've noticed this for a while, and this interview is our attempt to do something about it. What follows is a conversation with Pavan Otthi, founder of Curator — on why commerce operations is one of the hardest automation problems in software, what's actually new about this generation of agents, and what it takes to build something founders can actually trust to run.
If there's one line to carry through it, it's this. The goal isn't a faster back office. It's freeing founders from the operational grind so they can put their time where it actually compounds: the product, the customer, and the story they tell. Here's what we heard.
Current Business Context
UpScaleX: What was the original pain point that led you to build Curator?
Pavan: Disconnection. We've spent the last couple of years building in agentic commerce on both the B2B and B2C sides. We started by focusing on how fragmented the product discovery journey was for shoppers, but working closely with brands revealed a deeper problem: the operators themselves were stitching together everything from supply chain to marketing across a sprawl of disconnected SaaS tools that didn't talk to each other. Curator is how we envision work being executed in the future, where lean operators stay lean and agents handle the scale.
UpScaleX: What does "agentic commerce operations" actually mean in practice, and what does it not mean?
Pavan: It doesn't mean AI replacing the people who build the brand. I'm bullish on AI taking over the manual, repetitive work of running a business, but taste and creativity aren't going anywhere. Agentic commerce operations is what happens when operators get to spend their time on the next chapter of the brand instead of digging through dashboards to figure out why a shipment got delayed.
UpScaleX: "Free people up" is a phrase everyone uses. Free them up to do what, exactly?
Pavan: To spend it on the things that actually move a brand, the things only they can do: the product itself, engaging their customers, the story they tell, and the experience they deliver. So much of an operator's day gets eaten by the layer between intent and execution, like chasing POs, reconciling dashboards, and nudging a manufacturer. That work matters, but it isn't the work that builds the brand. Agentic brand operations isn't about doing those chores twenty percent faster. It's about handing enough of them off that founders get that attention back. And here's the part people underrate. The bottleneck was never the founder's calendar. It's that real operating know how tends to live in a few experienced heads, and the more of that you can capture, the more every brand can run like it has a seasoned operator behind it.
UpScaleX: If we zoom out, the way we've come to think about it is a few layers stacked on each other. Does that match how you see it?
Pavan: Pretty much. At the base there's the technical stack, the plumbing that lets an agent actually reach into a brand's tools. Above that is the information layer, the context of how that brand really runs. And on top sit the skills, the proprietary know how that's specific to a brand and hard to find anywhere else. That top layer is where the real edge is, and it's why this works through close, hands on work with each brand rather than something generic off the shelf.
UpScaleX: Why is commerce operations a uniquely difficult automation problem?
Pavan: Sensitivity and variability. Each brand uses its own cocktail of SaaS tools and has its own spin on how to best operate as a business. On top of that, day-to-day operations are crucial, and one mistake in automation could be very costly. Having the proper approval modes, auditability, and observability is crucial in building an automation platform for commerce operations.
UpScaleX: How do you define operations automation?
Pavan: Operations automation is giving agents control of data entry, constant monitoring of the business, repetitive tasks, and insights. It's putting the operator in the driver's seat and not responsible for refilling the tank and oiling the engine.
UpScaleX: What's actually new about agentic systems today, versus just smarter automation or better dashboards?
Pavan: Reasoning, omnipresence, and actionability. Today, agents can use your browser, keyboard, and mouse to perform the same actions humans do on their computers. That lets us operate across platforms that don't offer clean API integrations. In addition, ambient AI, or, in other words, AI that's constantly watching how operators make decisions and every moving piece of the business, allows for dashboards to truly be living, breathing windows into every aspect of the business.
UpScaleX: Can you make it concrete, without the dashboard speak?
Pavan: Sure. We've had a case where a brand's seasonal collection was at risk. The orders behind it weren't coming together in time, so they were heading toward either selling out or sitting on inventory they couldn't move. The useful part isn't that a system can see that happening, because a dashboard can do that. It's that the system knows enough about how the brand runs to catch it early, draft the follow ups to get it back on track, and put those in front of someone to approve before anything goes out. The judgment of what to do about the problem is the part that matters, and that's what we're trying to capture.
The Personal Assistant Frontier
UpScaleX: When was the ah-ha moment for you?
Pavan: The realization that agents could actually do some of my daily work. It felt like we were getting 2x or 3x the productivity we could have in a day, which is pretty crazy.
UpScaleX: Tell us about your side project, Jarvis. How did it come about, and what surprised you about the current architecture?
Pavan: This was a fun one. Recently we've been super busy, so we haven't had much time to work on it, but we're still using it to run some X and LinkedIn engagement.
We started by forking OpenClaw and building out infrastructure for better computer use, so we could have it run for longer. Eventually we used its core open-source agent runtime called Pi, which is honestly the unsung hero behind OpenClaw. It's a really dynamic and customizable agent framework that comes with coding tools and branching out of the box, which allows it to adapt and overcome challenges that would stop long-running agent tasks otherwise.
We're using it for computer-use tasks like navigating our X feed, finding interesting posts on LinkedIn, and identifying potential customers. Right now we're focusing on releasing it as an outbound, go-to-market, personal branding agent, as well as a personal assistant.
Beyond scripted agents or back-and-forth chat, there's truly been a shift in the agent world toward focusing on agent harnesses, how we enable agents to perform dynamic, long-running tasks with complex reasoning to truly unlock value for their users.
How This Evolves
UpScaleX: What does this mean for business and operations teams going forward?
Pavan: LLMs will keep getting better, which means users will be able to trust their agents to take on more audacious tasks. Teams should focus on staying lean, and individuals should become generalists — so they can delegate specialized work to specialized agent environments.
UpScaleX: What will "good" look like, and how will we evaluate the complex outputs of agents?
Pavan: Agent harness eval is definitely a hot topic right now, and I'd say it's still pretty nascent. Some say the top labs are overfitting their models to perform well on tool-calling and agent-eval benchmarks. One of the things we're really focused on is building a bunch of golden query sets to perfectly mimic how our customers would use their agents in the wild, and focusing on accuracy, latency, and reliability in dynamic data environments.
UpScaleX: A lot of this came up in conversations we've been having across the ecosystem lately. For a brand operator who wants to lean into where this is going, rather than just brace for it, what would you tell them?
Pavan: A few things. First, make yourself discoverable to agents. There's a thirty second version of this. Open whatever AI assistant you use and ask it to recommend a product in your category. If you show up, understand why. If you don't, that's the work. Second, don't try to reinvent the playbook. Look at the best operators in your space, copy what works, and let AI take the repetitive parts off your plate so you can run that playbook at a fraction of the effort. Third, make your operation legible. The brands that do well over the next couple of years are the ones whose context isn't locked in one person's head, so an agent can actually pick it up and run with it. And mostly, stay nimble. Most of this didn't exist two years ago. Nobody knows exactly where it lands, so the move is to start small, learn fast, and keep your hands close to the work.