Architects are the choke point on whether AI agents become production infrastructure or another POC graveyard. This page is the reference for the patterns that work, the failure modes to avoid, and the governance model that scales beyond your first agent.
04 steps · 04 FAQs
“AI agents fail in production for the same reason traditional integrations do: they were designed for the happy path and shipped without the recovery path.”
Practical steps
What objects can it read? What can it write? What can it delete? Document it before you build. Permission creep is the architecture failure mode.
Auto-approve for low-risk reads. Human-in-loop for state changes. Multi-approver for deletes or mass updates. Treat it like a deploy pipeline.
Every agent action logged with input, output, decision rationale, and elapsed time. Without this, debugging is impossible.
One config flag that disables the agent globally. Test the kill switch quarterly. The first time you need it is the wrong time to find out it doesn't work.
From the library
Score
Quick reference for the 30 most common SetupAuditTrail action types: what each entry means, how severe it is, and what to do when you see it.
Score
The 4 permission layers and how they resolve, 10 named anti-patterns, a SOQL query bank and a cleanup sequence that will not break access.
Avg readiness
A checklist to test whether your Salesforce org is ready for AI: data quality, automation maturity, user adoption and integration readiness.
Frequently asked
As a peer service to your existing integration tier, with the same security model, the same monitoring, and the same SLA framing. Treating them as a feature inside Salesforce produces brittle architectures; treating them as a service produces resilient ones.
Permission sets, IP allowlists, audit logging, and an explicit approval workflow for state-changing operations. The agent should run as a least-privilege integration user. The credentials should be rotated on the same cadence as your integration secrets.
Agnostic if you have a multi-cloud architecture (Salesforce + Snowflake + non-SF tools). Agentforce if your agentic surface is 80%+ Salesforce-internal and you want platform-managed lifecycle. The platform lock-in trade-off is real either way.
Cascading hallucinations across multi-agent workflows. One agent's wrong output becomes the next agent's input, and the error compounds. The mitigation is hard validation gates between agent steps, not better prompts.
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