Read-only code review appears to be a small, safe first step. Yet even the first pilot requires the team not merely to run a model, but to design, measure, and evolve an entire agentic pipeline.
As soon as the team decides to “add AI review,” it inevitably begins discussing not one integration but the structure of a production process:
This script demonstrates the idea quickly, but immediately hard-codes one model, one harness, one prompt, one amount of context, and one response path. Every experiment becomes a change to integration code; versions cannot be compared and the choice cannot be justified. It is legacy from the first minute of its existence.
In Multica, the agent, harness, skills, access, context policy, and return path become configuration and system entities. Every attempt retains a version, trace, and metrics: alternatives can be compared, and the conversation with the executor can continue within the task context.
Multica is not excessive for a single AI review use case. It supports this scenario without a separate custom integration product and simultaneously creates a foundation for subsequent agents, skills, and experiments. The team gets not an authoritarian “this is how we do it,” but a way to demonstrate why a particular pipeline works better than the alternatives.