The problem with this is that you have a consistency problem if you want to take action. The only way of making your agents read-write rather than read-only in practice is to use the underlying systems rather than try and pool information in a data lake.
But that does make it more complex to build simple information retrieval use cases.
I would encourage anyone who hasn’t to try and see a glassblowing demonstration. It’s about as different from programming as any creative process could be - real time, intuitive, working with an unstable material - but something in it really spoke to me.
The Chrysler museum in Norfolk VA has amazing free demonstrations almost every day which are amazing if anyone happens to find themselves in Southern Virginia.
We’re (harriethq.com) trying to do this by reframing it as a “provisioning” challenge - how do you get your connectors installed on non-technical desktops, how do you give some easy pre-bake recipes that wake them from their dogmatic slumber
Honestly though we are finding that a little FDE to set up pre-bake stuff that’s sufficiently specific to the customer is needed. Otherwise people are like, “I don’t need to close the books, I need to do a per-working-day profitability analysis for 10 EU countries with different public holidays”, and they get stuck there.
I had the same experience with chatbots, but we shipped a chatbot module a year ago that helps with complex config questions by reading and answering based on a Salesforce Experience site.
I was skeptical but it gets a 68 NPS from users, even if we do get the occasional "why are you investing in AI I hate it" coming through the feedback channel.
As ever, the issue is "what problem are you solving". If it's that you want more people to put their hand up and talk to you/order something, chatbots seem like a bad solution. If it's that you have a ton of complex docs that people have to read in order to implement and use your product, it's not the solution but it's probably part of a solution.
From the comments looks like lots of people looking at this problem from different angles.
We (harriethq.com) also have a somewhat similar insight, which is that setting up connectors is a drag for non-technical users, and a lot of systems don't support per-user connectivity so need an API shim.
The thing I like about this (Agent Vault) approach is that it's more extensible than what we're offering, which is a full managed service. But we've found that some features (e.g. ephemeral sandboxes to execute arbitrary e.g. uvx/npx based mcps) are just a big pain to self-deploy so it's easier for us to provide a service that just works out of the box.
Kudos to the team, this looks great and I'm looking forward to playing with it
I think the corrective to this is that many of these incumbents will fail to re-conceive their product stack from a user-centric perspective, and as a result they will be reduce to just a dumb data layer which is easily swappable.
Sure, they could do that, but the cultural change required is an order of magnitude harder than just sticking an agent on top of their source-of-truth and believing that the problem is solved.
Maybe it works for areas where the application is a relatively self-contained island of productivity. Figma is somewhere that a designer spends a lot of their day, so it's going to be less vulnerable, but most pieces of softare fit into broader workflows. So for Figma the disruptor is less likely to be "AI-powered designer" and more "AI-powered web builder" - e.g. Lovable or even Claude Code itself that just generates great designs.
The flip side of this is that if model capabilities are extremely strong such that they are able to saturate the benchmarks, the differentiation and defensibility of a wrapper solution built on top are significantly reduced.
IANAL but e.g. Claude Cowork is already good enough that it's hard to see how the legal tech startups are going to differentiate except around access controls, visual presentation of workflows, etc. And that's in a heavily enterprise/compliance-aware/security-focused context.
Don't get me wrong, that's still a big "except" - big enough for massive companies to be built. Personally the anxiety of being so close to being squashed by the foundation models would make me unhappy as an entrepreneur but looking at the market it seems like many people have a higher risk tolerance.
Of all the challenges you face as a startup, the legal entity you choose is possibly the least consequential. Just choose a jurisdiction where investors understand how the legals work (Delaware C-corp, UK Ltd is OK too) and there's a finite administrative burden and/or commoditized tooling in place to help you handle it.
Now, that may not work in all jurisdictions for reasons of local taxation etc (and you'll have to work out payroll tax, benefits etc) but that's almost never anything to do with the legal entity type!
> I believe that the near-term de-dollarization isn't as much trust erosion as it is a tool to provide monetary penalty for behaving in unpredictable ways.
I have played with this but been underwhelmed. However I do think probably on the right track.
I know the ecosystem not-at-all (sum total knowledge of the CAD ecosystem is that my kids got a Bambu printer for Hanukkah) but it feels to me that current LLMs should be able to generate specs for something like https://partcad.readthedocs.io/en/latest/, which can then be sliced etc.
Curious to know what others think? I come at this from the position of zero interest in developing the fine design skills needed to master but wanting to be able to build and tweak basic functional designs.
> The unification and seamless workflow at that scale is painfully hard to achieve
It does make you wonder, why not just be a lot smaller? It's not like most of these teams actually generate any revenue. It seems like a weird structural decision which maybe made sense when hoovering up available talent was its own defensive moat but now that strategy is no longer plausible should be rethought?
One thing I find interesting about discussions of typography in Cyrillic is how poor the overall readability of text is in most fonts compared to Latin because of the relative scarcity of risers and descenders (e.g. pqlt etc)
One of my tutors at university claimed that she was able to read 9th century manuscript Cyrillic faster than modern printed books because the orthography was more varied and easier to scan/speed-read.
It’s hard to bet against the foundation models winning consumer use cases where you can reimagine the whole product as a single tool or small number that can be dynamically plugged in to the underlying model and doesn’t require access to proprietary data/custom context.
I think this is a relative succinct summary of the downside case for LLM code generation. I hear a lot of this and as someone who enjoys a well-structured codebase, I have a lot of instinctive sympathy.
However I think we should be thinking harder about how coding will change as LLMs change the economics of writing code:
- If the cost of delivering a feature is ~0, what's the point in spending weeks prioritizing it? Maybe Product becomes more like an iterative QA function?
- What are the risks that we currently manage through good software engineering practices and what's the actual impact of those risks materializing? For instance, if we expose customer data that's probably pretty existential, but most companies can tolerate a little unplanned downtime (even if they don't enjoy it!). As the economics change, how sustainable is the current cost/benefit equilibrium of high-quality code?
We might not like it but my guess is that in ≤ 5 years actual code is more akin to assembler where sure we might jump in and optimize but we are really just monitoring the test suites and coverage and risks rather than tuning whether or not the same library function is being evolved in a way which gives leverage across the code base.
Email: david at dbuxton dot com