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Matthew Berman comments on a short excerpt predicting that business logic will move to AI agents. He illustrates the idea with CRM updates, customer revenue charts and follow-up emails. The clip presents a vision for future software, not a demonstration that agents can reliably replace all hardcoded business logic or software engineering.
- 1A shift in business logicA speaker predicts that business logic will move to agents in an AI layer.
- 2What business logic doesBerman illustrates it with actions such as updating CRM records and sending emails based on database information.
- 3Natural-language workflowHe imagines requesting a top-customer chart and follow-up emails, with an agent carrying out the steps or creating tools.
Conclusiones
- The quoted vision places business logic in an AI agent layer.
- Berman describes business logic as actions performed on underlying business data, such as updating a CRM.
- His example asks an agent to chart top customers and draft follow-up emails.
- The claim that agents will replace hardcoded logic is a prediction rather than a result established in the clip.
Lee el texto editado por capítulos
Agents and business logic
Berman introduces a prediction that business logic will move to an AI layer. He explains the term using business data and CRM actions.
A short video presents what I think could be a clear picture of the future of software engineering: a speaker predicts that business logic will move into an AI agent layer. That is a vision of how software might work, not a demonstration that all existing business logic has already been replaced.
By business logic, I mean taking the data in a database and doing something with it: updating a CRM record, for example, or sending emails when certain criteria are met. The idea is that instead of building each workflow as hardcoded logic, a user could describe the outcome to an agent.
A customer workflow example
He imagines asking for a revenue chart and follow-up emails in natural language. The scenario illustrates his view of a possible future rather than a tested workflow.
For example, I might ask for a graph of my five highest-revenue customers, then ask for emails telling those customers I want to do business with them in 2025. I envision the agent knowing how to perform the work or creating tools to do it on my behalf. The clip does not test whether such a workflow would be reliable in practice.
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