A Vision of AI Agents Taking On Business Logic

Matthew Berman explains a prediction that agents could turn natural-language requests into actions on business data.

MBMatthew BermanAnime · Forward Future
Chaîne
Matthew Berman
Publié
1 mai 2025
Vidéo
1:00
Lecture
1 min
Original
anglais
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RÉSUMÉ

En 30 secondes

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.

  1. 1A shift in business logicA speaker predicts that business logic will move to agents in an AI layer.
  2. 2What business logic doesBerman illustrates it with actions such as updating CRM records and sending emails based on database information.
  3. 3Natural-language workflowHe imagines requesting a top-customer chart and follow-up emails, with an agent carrying out the steps or creating tools.

À retenir

  1. The quoted vision places business logic in an AI agent layer.
  2. Berman describes business logic as actions performed on underlying business data, such as updating a CRM.
  3. His example asks an agent to chart top customers and draft follow-up emails.
  4. The claim that agents will replace hardcoded logic is a prediction rather than a result established in the clip.
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Transcription seule
01

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.

02

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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