AI Strategy for Business Leaders
Module 4 of 8

Multi-Agent Systems

As a Business Operations Tool

This module is narrated by an AI voice model trained on Dr Ravichandran's own recordings — a real, working example of the AI-native approach this course teaches, not a shortcut around it.

What this module covers

Decompose a repetitive business process into an agent pipeline, produce a realistic cost model grounded in real figures, and anticipate the operational failure modes that come with running agents at scale.

Lessons in this module

  • 4a — Why compose agents, not just chat with one
    The strategic shift from single-chatbot use to composed, multi-agent processes.
  • 4b — The pipeline case study
    Architecture and real cost figures — a multi-agent pipeline assembled from operational conversations, with a bulk-classification pass and a scoring pass whose cost dropped roughly 20x by switching models for non-customer-facing work.
  • 4c — What actually broke at scale
    Concrete operational failures: crash-restart loops, out-of-memory crashes, a silently failed dependency, bad upstream data, corrupted records — the ordinary cost of running any pipeline at real volume.
  • 4d — Where automation needs a human checkpoint
    The mandatory-gate principle, especially where personal or sensitive data is involved.

Deliverable

Try this: Sketch an agent pipeline for one repetitive process in your own org, with a cost estimate grounded in per-unit model pricing, and name one operational failure mode to plan for.

Check your understanding

1What is the primary strategic shift this module argues for?
2What was the primary lever for cutting the scoring pass's cost by roughly 20x?
3What kind of failures were most common when running the pipeline at real volume?
4What is one clear indicator that a step in an agent pipeline needs a mandatory human checkpoint?