Build the secure-execution and identity infrastructure that runs Cohere's autonomous AI agents

Cohere's Agentic Platform team is building next-generation infrastructure for autonomous AI agents: secure code execution, agent state management, model routing, identity/authentication, and resource management for long-running workflows. This interview targets engineers with production ML infrastructure and distributed systems depth who can turn emerging, pattern-less research ideas into reliable platform capabilities.

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What this interview tests

Secure execution/sandboxing for untrusted (agent-generated) code
Identity, authentication, and trust boundaries for non-human callers
Model routing and orchestration across model providers/environments
Rate limiting, quotas, and resource management at scale
State/memory/filesystem abstractions for long-running agents
Judgment in productionizing research prototypes with few established patterns

Common question themes

Design a sandbox for executing untrusted, agent-generated code safely

How would you design identity and permission scoping for an autonomous agent

Design a rate-limiting/quota system for many concurrent long-running agent workflows

Tradeoffs in routing requests across different LLM providers/model types

Tell me about taking a research idea to production infrastructure with no existing playbook

How candidates describe it

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

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