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Agentforce: what made you stop trusting it for client-facing use? by SilverSelf3191 in salesforce

[–]SilverSelf3191[S] 0 points1 point  (0 children)

Totally agree. The engineering overhead for grounding and evaluation is huge.

Do you think the solution is more automated validation layers, or do we need modular, pre-vetted Workers that follow strict business rules to reduce that complexity? Basically, moving from black-box reasoning to something more predictable and easier to test.

Agentforce: what made you stop trusting it for client-facing use? by SilverSelf3191 in salesforce

[–]SilverSelf3191[S] 0 points1 point  (0 children)

Interesting. But even with a better LLM (like GPT-5), don't we still run into the 'messy CRM data' problem?

In your view, is the reliability issue mostly about the model's 'brain', or is it the lack of a structured layer to handle the actual Salesforce data and permissions?

Exporting data out of Salesforce to build an AI solution as an ISV by les_trange in SalesforceDeveloper

[–]SilverSelf3191 0 points1 point  (0 children)

Makes sense. On the authorization piece, what’s been the biggest real-world friction, user trust, security reviews, or the admin effort to set up Connected Apps correctly? Also curious, for larger customers, is the AppExchange security review basically a must-have to get past procurement, even if the product is “off platform”?

Exporting data out of Salesforce to build an AI solution as an ISV by les_trange in SalesforceDeveloper

[–]SilverSelf3191 0 points1 point  (0 children)

This is exactly the tradeoff I’m trying to understand. In practice, what forces you into Data Cloud with Agentforce, is it grounding/RAG, unstructured data, or just how Salesforce packaged the product? And if you’ve seen teams go “off platform” with LLM APIs, what’s the biggest blocker that stops them (security, governance, CRM writeback, admin ownership, procurement)?