What if AI agents could be delivered like groceries?
India has become fascinated with the idea of 10-minute delivery. Groceries, food, stationery, medicines, house help and a growing list of other services are being pushed toward the same promise: I need something now, and I don’t want to wait.
Some of these use cases are convenience plays; others, such as rapid ambulance or emergency-response services, can have a much more meaningful impact.
That got me thinking about a slightly crazy startup idea:
Why not a 10-Minute Agent?
I don’t mean that we magically build a sophisticated enterprise-grade agent from scratch in ten minutes. That would be a rather optimistic sales pitch! 😄
What I mean is: what if we could make the journey from “I need an AI capability” to “I have an AI capability deployed” dramatically faster and more configurable than it is today?
Think of it as an AI delivery network
Today, when a company wants to implement an AI solution, the journey can still be surprisingly long — requirements, architecture, model selection, POCs, data integration, security reviews, development, testing, deployment and plenty of back-and-forth.
The technology has become incredibly fast. The deployment process often hasn’t.
So imagine a different model. A customer comes to the platform and describes what they want to achieve. Instead of being handed one fixed “AI agent”, they get to configure the solution — choosing the model, data sources, capabilities and the trade-offs that matter to them.
OpenAI, Anthropic, an open-source model or their own? Documents, databases, APIs or a combination? Simple RAG, RAG + analytics, tools and workflows, or a more sophisticated agentic system? And then the inevitable trade-offs: cost vs performance, latency vs intelligence, managed vs self-hosted, simplicity vs flexibility.
In essence, the customer gets to configure their AI stack rather than buy a one-size-fits-all solution.
And this is where I find the idea particularly interesting. Instead of expecting the customer to figure out how to assemble and deploy all of this themselves, Forward Deployed Engineers become the delivery partners.
The platform takes the requirements and configuration, assembles the required components, and the FDE takes it the final mile — integrating systems, connecting data, handling customisation and edge cases, and getting the AI solution deployed.
In other words:
Maybe Dell had part of the answer
This is where an old idea came to mind. Years ago, Dell made PC buying interesting by letting customers configure their machine rather than forcing everyone into one fixed specification. You could choose the processor, memory, storage and other components based on what you actually needed.
What if enterprise AI worked the same way?
Instead of “Here is our AI agent. This is how we built it. Take it or leave it,” the experience could be: “Configure your AI.”
Choose the model, data, capabilities, infrastructure and the trade-offs you are willing to make — and let the platform recommend the right configuration.
For one business, that might simply be Vanilla RAG for answering questions over documents. For another, RAG + Analytics to combine unstructured documents with structured business data. Another might need RAG + Tools + Workflow to retrieve information, call APIs and execute defined processes. And for more complex problems, it could evolve into a fully agentic system capable of handling multi-step tasks.
The important part is that the customer doesn’t need to understand how every piece fits together. They describe the problem and the outcome; the platform translates that into the technical configuration, and the FDE turns it into a working solution.
That, to me, is where the Dell analogy gets interesting: don’t just sell the AI — let the customer configure it for what they actually need.
And this is where “10-Minute Agent” comes back
This is where I think the analogy with 10-minute delivery becomes even more interesting. A 10-minute delivery company doesn’t manufacture the grocery you ordered in ten minutes. The product already exists. What the company has really built is an infrastructure around selection, fulfilment and delivery that makes the entire experience incredibly fast.
Could AI deployment eventually work in a similar way?
The models already exist. OpenAI, Anthropic and a growing ecosystem of open-source models are already available. The databases and vector stores exist. The agent frameworks exist. Cloud infrastructure, APIs, observability and all the other building blocks required to put an AI system together are increasingly becoming commodities or readily available services.
So perhaps the challenge isn’t that we don’t have the ingredients.
Perhaps the challenge is getting the right ingredients together, in the right configuration, for the right business problem — quickly.
That is where the idea of a highly standardised configuration + orchestration + deployment layer becomes interesting. Instead of building every AI solution from scratch, the platform could bring together these existing components based on what the customer actually needs, apply the appropriate trade-offs, and create a deployable solution.
In that sense, perhaps the real product isn’t simply “an agent.”
The real product could be the AI delivery infrastructure that makes getting the right agent into a business as easy and eventually as fast as ordering something online.
And maybe that’s what “10-Minute Agent” really means.
Not necessarily an enterprise agent magically built from scratch in ten minutes — but an AI delivery system designed from the ground up to make that journey dramatically faster, more configurable and more repeatable.
The FDE becomes the “delivery partner”
I also like the Forward Deployed Engineer part of this idea because it could fundamentally change both the economics and the operating model. If every customer requires a completely bespoke engineering team to build an AI solution from scratch, the business can very quickly start looking like a traditional consulting company — highly customised, highly people-dependent and difficult to scale.
But what if a large part of the architecture is already standardised, modular and configurable?
In that model, the FDE’s job becomes much more focused. They aren’t reinventing the entire solution for every customer. Instead, they take a pre-configured AI solution and make it work within the customer’s actual environment: connecting the systems, mapping the data, handling the inevitable edge cases, configuring authentication and permissions, adapting workflows, deploying the solution and helping the customer get it into production.
In some ways, they become the delivery partner for AI.
And there is another interesting possibility here. Every deployment can teach the platform something. The more customers you deploy for, the more integrations, patterns, reusable components, architectures and edge cases you accumulate. Over time, that knowledge can feed back into the platform, making the next deployment faster and requiring less bespoke engineering.
That is where the “10-minute” part starts becoming less of a marketing gimmick and more of a direction for the architecture.
If an AI deployment that takes months today can be standardised down to weeks, and then days, and then hours, perhaps some well-understood and highly repeatable use cases could eventually get down to minutes.
Maybe not every agent.
Maybe not every customer.
But perhaps enough of them to make “10-Minute Agent” a meaningful product philosophy.
Of course, there are some big problems
There is, of course, a rather large reality check here.
Enterprise AI isn’t quite the same as ordering a packet of biscuits in ten minutes. 😄 Data security, privacy, permissions, compliance, model quality, hallucinations, observability, cost management and integration complexity don’t disappear simply because we put a nice configurator in front of them.
Some deployments will always require substantial engineering. Some customers will need completely custom models, infrastructure or workflows. And some use cases will simply be too sensitive or too complex to automate in this way.
So I don’t think the objective should literally be “Every AI deployment takes 10 minutes.”
The more interesting objective might be:
“Make the default AI deployment experience feel as close to ordering something as possible.”
That is a very different proposition.
The ambition is not to pretend that enterprise AI is simple. It is to take everything that can be standardised, productised and automated — and make the remaining complexity as easy as possible for the customer and the FDE to handle.
So… a 10-Minute Agent company?
Maybe.
Maybe it is a terrible startup idea.
Maybe it is simply a fun thought experiment triggered by India’s obsession with 10-minute delivery. 😄
But the more I think about it, the more I wonder whether the next wave of AI businesses will be built not only around creating better models and smarter agents, but around making those models and agents dramatically easier to configure, deploy and customise for real businesses.
Because we already have most of the ingredients.
We have LLMs, RAG, databases, APIs, agent frameworks, cloud infrastructure and analytics. We also increasingly have Forward Deployed Engineers who can bridge the gap between the technology and the customer’s real-world environment.
What if we put a product layer on top of all of that?
A customer could come in with a business problem, choose the model and data sources they want, decide how sophisticated the solution needs to be, make the relevant cost-versus-performance trade-offs, and let the platform recommend and assemble the architecture.
Then the FDE takes over the last mile — integrating, customising and deploying it.
Choose what you need. Configure the trade-offs. Let the platform assemble the solution. Send the FDE to deliver it.
And eventually…
10-Minute Agent. 🚀
Just like 10-minute delivery didn’t really change what we buy — it changed how quickly we can get it — perhaps the opportunity in AI isn’t only to build more powerful agents.
Perhaps it is to build the delivery infrastructure for AI: the layer that lets businesses get the right agent, configured for their needs, integrated with their systems and running in production much faster than they can today.
From:
“We should build an AI agent.”
to:
“Your AI agent has been delivered.”
That, at least, sounds like a startup I would be curious to explore.
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