A PM's Guide to AI Agent Architecture

My view is that you need to transition slowly and carefully to AI first customer support.

1. Know the scope of problems an AI can solve with high probability. Related prompt: "You can ONLY help with the following issues."

2. Escalate to a human immediately if its out of scope: "If you cannot help, escalate to a human immediately by CCing bob@smallbiz.co"

3. Have an "unlocked agent" that your customer service person can use to answer a question and evaluate how well the agent performs in helping. Use this to drive your development roadmap.

4. If the "unlocked agent" becomes good at solving a problem, add that to the in-scope solutions.

Finally, you should probably have some way to test existing conversations when you make changes. (It's on my TODO list)

I've implemented this for a few small businesses, and the process is so seamless that no one has suspected interaction with an AI. For one client, there's not even a visible escalation step: they get pinged on their phone and take over the chat!

'Routing through increasingly specialised agents' was my approach, and the only thing that would've done the job (in MVP form) at the time. There weren't many models that would fit our (v good) CS & Product teams' dataset of "probable queries from customers" into a single context window.

I never personally got my MVP beyond sitting with it beside the customer support inbox, talking to customers. And AFAIK it never moved beyond that after I left.

Nor should it have been, probably - there are (wild, & mostly ineffable) trade-offs that you make the moment you stop actually talking to users at the very moment they get in touch. I don't remember ever making a trade-off like that where it was worthwhile.

I _do_ remember it as perhaps the most worthwhile time I ever spent doing product-y work.

I say that because: To consider a customer support query type that might be 0.005% of all queries received by the CS team, even my trash MVP had to walk a path down a pretty intricate tree of agents and possible query types.

So - if you believe that 'solving the problems users have with your product' = 'making a better product'. then talking to an LLM that was an advocate for a tiny subset of users, and knew very intimately the details of their issue with your product, that felt really good. It felt like it was a very pure version of what _I_ should be to devs, as any kind of interface between them and our users.

It was very hard to stay a believer in the idea of a 'PM' after seeing that, at least. As a person who preferred to just let people get on with things.

I enjoyed the linked post; it's really interesting to see how far things have come. I'm surprised nobody has built 'talk to your customers at scale', yet - this feels like a far more interesting problem than 'avoid talking to your customers at scale'.

I'm also not surprised, I guess, since it's an incredibly bespoke job to do properly, I imagine, for most products.

In short: nice industry roadmap, but we are nowhere near robust, trustworthy multi-agent systems yet.

With current technology (LLM), how can an agent ever be sure about its confidence?

In my book they ideally focus on understanding scope, user needs and how to measure success, while implementation details such as orchestration strategies, evaluation and making sure your system delivers the capabilities you want in general, are engineering responsibilities.