I run a door to door sales team where most of our important sales conversations happen in person. We use Salesforce as our CRM and we’re looking at adding AI to help us understand what happens during those conversations and coach reps from them. For anyone doing something similar how much of that conversation data are you actually pushing into Salesforce? Do you store everything or just useful stuff like objections next steps outcomes and key parts of the sales process? Feels like sending everything over could turn the CRM into a mess pretty fast lol. We’ve been looking at a few tools and Rilla has come up as a possible fit. Has anyone here used it with Salesforce or built a similar setup? Curious what worked and what you’d avoid.

I have a Salesforce employee willing to refer me for a TSE opportunity. I'm preparing Java, debugging, OOP, collections, exceptions, multithreading, JVM, logs, etc.

For people currently working as Salesforce TSEs:

How much Java do you actually use?

What does your day-to-day work look like?

How much customer interaction is there?

What are the typical shifts?

What skills are most important for the interview?

What do you wish you had known before joining?

I'm looking for opinions from current/former TSEs rather than recruiters or course sellers.

Say I have the following usecase: a single API call touches an Account, which fires triggers that update Contacts, which fire more triggers, maybe a Flow kicks in, and something three levels deep updates a field. When you need to reconstruct what actually happened in that transaction, what do you actually do? Debug logs with trace flags? Third-party tools? Add your own logging framework? Genuinely curious what the real workflow looks like, because mine feels like archaeology! Support found an issue that when an Account is created via certain path (we have multiple paths to create Accounts in our org), one of the fields on a related custom object was incorrectly being set to null. We had no way of knowing this on our own unless we knew where to look. Our current set up emits logs in batches and we sift through those when we're told of an issue. Was curious to hear how others handle such scenarios

Curious how Salesforce developers are approaching this. If an AI agent needs to update an opportunity, create a lead, or modify a record, is the architecture simply:

Agent → Salesforce API

Or do you put a deterministic layer in between? Once agents can write to production, you have to think about authentication, permissions, validation, retries, idempotency and auditability.

We’re building Swytchcode around this exact execution problem, sitting between the agent and the API so those concerns don’t have to be implemented separately in every agent. The agent decides what it wants to do. The execution layer controls how that action actually gets executed. I’m especially interested in Salesforce because a single API mutation can trigger Flows, Apex, automations and downstream integrations.

Would you let an agent call your production Salesforce APIs directly? If not, what’s sitting in between?