Build the workflow/agent execution engine inside Cohere's North enterprise AI platform

This interview probes full-stack product engineering (Python/React) on Cohere's Agents & Automations team, building the workflow builder, execution engine, integrations, and observability tooling behind North. Expect deep dives on shipping RAG/agentic systems at scale, working under security/privacy constraints that block using popular libraries, and comfort operating across the stack in a fast-moving, ambiguous environment.

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What this interview tests

Full-stack shipping in Python and React in production
Building/deploying RAG or agentic applications at meaningful scale
Engineering under strict security/privacy constraints (no popular libraries, low-resource environments)
Workflow/execution-engine design: builder, integrations, observability, evaluation loops
Debugging and fixing issues outside your own codebase

Common question themes

Describe a RAG or agentic system you built and deployed to real users — what broke at scale and how did you fix it?

Tell me about a time you couldn't use a popular library/tool due to security or deployment constraints — what did you build instead?

How would you design observability and evaluation for an agent workflow so customers can trust what it does?

Walk through debugging an issue that lived outside your own part of the codebase

How do you operate when priorities and objectives shift mid-project?

How candidates describe it

Real Software Engineer interview stories — retold from candidates' public write-ups, with sources.

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