I've been in a bunch of security reviews for AI agents lately, and the question that trips vendors up isn't about encryption or SOC reports.

It's simpler: "where exactly does our data go, and who can see it?"

Every demo I've sat through this year looked amazing. Clean inputs, happy path, a nice "it just works" moment. Then week one in production hits and it's a different animal.

Where I keep seeing the cracks:

- inputs nobody planned for (scanned PDFs, half-filled forms, emails in 3 languages)

I keep a rough tally of agent incidents I hear about, and the model is almost never the cause. It's the stuff sitting next to it.

The usual suspects on my list:

Compiling the production AI ops questions we get asked most often from prospects and customers. Sharing the honest answers.

Q: How do you handle a model deprecation by the LLM vendor?

A: Multi-LLM routing means no single deprecation breaks our platform. Customers running BYOM are responsible for their own model lifecycle.

For founders looking at the enterprise AI market, sharing what I've seen from the platform side.\

Topics I can speak to:

The 80% failure rate Gartner cites isn't because AI is bad. It's because production is hard in ways pilots don't reveal.

The real reasons I've seen projects die:

Opening this up for the community. I work on enterprise AI agent deployments, mostly mid-market through enterprise BFSI, healthcare, retail, and logistics.

Topics I'm happy to go deep on:

- Pilot-to-production gap and how to close it

Audit trail is one of those features that sounds binary but isn't.

Levels I've seen requested:

- Basic: timestamp + user + action

Stepping back to look at the quarter.

Aggregated learnings across 40+ deployments this quarter:

Marketplace milestone update for anyone tracking.

Top 5 templates by deployment volume in the past quarter: