𝗔ft𝗲𝗿 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝟮 𝘆𝗲𝗮𝗿𝘀 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜, 𝘄𝗲’𝘃𝗲 𝗰𝗼𝗺𝗲 𝘁𝗼 𝗼𝗻𝗲 𝗰𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻:
The AI industry is optimizing for the wrong metric.
Everyone is asking: “How smart is the model?”
Enterprise teams should be asking: “𝗖𝗮𝗻 𝗜 𝘁𝗿𝘂𝘀𝘁 𝘁𝗵𝗶𝘀 𝗔𝗜 𝘁𝗼 𝗲𝘅𝗲𝗰𝘂𝘁𝗲 𝗺𝘆 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗰𝗲𝘀𝘀?”
The hardest part isn’t getting an agent to answer a question.
It’s getting it to reliably complete an end-to-end workflow.
Take a customer refund. In production, the agent has to do more than understand intent:
✅ Validate the request against business policies
✅ Decide what can be auto-approved vs escalated
✅ Pause execution while waiting for human approval
✅ Resume from the exact same state later (not restart)
✅ Update the right CRM records
✅ Produce an audit trail for every decision
None of that is a prompting problem. It’s an 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝗯𝗹𝗲𝗺.
𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗯𝗹𝗼𝗰𝗸𝗲𝗿 𝘆𝗼𝘂’𝘃𝗲 𝗳𝗮𝗰𝗲𝗱 𝘄𝗵𝗲𝗻 𝘁𝗿𝘆𝗶𝗻𝗴 𝘁𝗼 𝗺𝗮𝗸𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗶𝗻 𝘁𝗵𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲?
#EnterpriseArchitect #Enterprise Architecture #AIAgents #AI #Salesforce #Salesforce Developer #CRM #CRM Configuration
This really resonates.
One thing I've noticed is that most benchmarks focus on whether an AI agent can produce the right answer.
In production, that's only one piece of the puzzle.
The bigger questions become:
• Can it follow business policies?
• Can it recover from failures?
• Can it handle long-running workflows?
• Can you explain every decision it made?
Curious to see how the industry evolves over the next few years.