๐ft๐ฒ๐ฟ ๐บ๐ผ๐ฟ๐ฒ ๐๐ต๐ฎ๐ป ๐ฎ ๐๐ฒ๐ฎ๐ฟ๐ ๐ฏ๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐ฒ๐ป๐๐ฒ๐ฟ๐ฝ๐ฟ๐ถ๐๐ฒ ๐๐, ๐๐ฒโ๐๐ฒ ๐ฐ๐ผ๐บ๐ฒ ๐๐ผ ๐ผ๐ป๐ฒ ๐ฐ๐ผ๐ป๐ฐ๐น๐๐๐ถ๐ผ๐ป:
The AI industry is optimizing for the wrong metric.
Everyone is asking: โHow smart is the model?โ
Enterprise teams should be asking: โ๐๐ฎ๐ป ๐ ๐๐ฟ๐๐๐ ๐๐ต๐ถ๐ ๐๐ ๐๐ผ ๐ฒ๐ ๐ฒ๐ฐ๐๐๐ฒ ๐บ๐ ๐ฏ๐๐๐ถ๐ป๐ฒ๐๐ ๐ฝ๐ฟ๐ผ๐ฐ๐ฒ๐๐?โ
The hardest part isnโt getting an agent to answer a question.
Itโs 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:
โ Validate the request against business policies
โ Decide what can be auto-approved vs escalated
โ Pause execution while waiting for human approval
โ Resume from the exact same state later (not restart)
โ Update the right CRM records
โ Produce an audit trail for every decision
None of that is a prompting problem. Itโs an ๐ฒ๐ ๐ฒ๐ฐ๐๐๐ถ๐ผ๐ป ๐ฝ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ.
๐ช๐ต๐ฎ๐โ๐ ๐๐ต๐ฒ ๐ฏ๐ถ๐ด๐ด๐ฒ๐๐ ๐ฏ๐น๐ผ๐ฐ๐ธ๐ฒ๐ฟ ๐๐ผ๐โ๐๐ฒ ๐ณ๐ฎ๐ฐ๐ฒ๐ฑ ๐๐ต๐ฒ๐ป ๐๐ฟ๐๐ถ๐ป๐ด ๐๐ผ ๐บ๐ฎ๐ธ๐ฒ ๐๐ ๐ฎ๐ด๐ฒ๐ป๐๐ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป-๐ฟ๐ฒ๐ฎ๐ฑ๐ ๐ถ๐ป ๐๐ต๐ฒ ๐ฒ๐ป๐๐ฒ๐ฟ๐ฝ๐ฟ๐ถ๐๐ฒ?
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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:
โข Can it follow business policies?
โข Can it recover from failures?
โข Can it handle long-running workflows?
โข Can you explain every decision it made?
Curious to see how the industry evolves over the next few years.