American Express Interview Questions — Page 2

American Express·AI Engineer·Technical Phone Screen

Jun 2026

Technical screen for an AI Engineer role at Amex focused almost entirely on how agents extend beyond what the base LLM can do. Pretty deep dive, more systems-thinking than I expected for a phone screen.

How does tool use and function calling work in AI agents? Walk through schema design, how the agent routes to the right tool, and what happens when a tool call fails.
How do plug-in style or MCP-style integrations extend an agent's capabilities, and what are the security and reliability trade-offs involved?
When would you use code execution as a tool for an agent, and what sandboxing concerns come with it?
How does retrieval function as a capability for an AI agent, and how does it differ from just stuffing more context into the prompt?
Explain the planner/executor pattern in agent architectures. What are the benefits and failure modes?
How does an agent discover and select the right tools at runtime, especially when the tool set is large?
How do you decide whether a new capability should be added as an external tool versus fine-tuned or baked into the model itself?

“I started with the schema side and felt pretty solid there, JSON schemas, typed parameters, descriptions that actually help the model pick correctly.”

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American Express·AI Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at Amex for an AI Engineer role, focused entirely on long-running multi-agent systems. Pretty deep technically, more so than I expected for a financial services company. Left feeling like I'd covered maybe 60% of what they were probing for.

How would you design a multi-agent system that needs to run reliably for hours or days, handling crashes and restarts without losing progress?
How do you ensure tool calls made by agents are idempotent, and what retry and recovery semantics do you apply when something fails mid-execution?
Describe how you would implement supervision in a multi-agent system, where a parent agent monitors and can restart child agents that fail or misbehave.
What communication patterns would you use between cooperating agents, and what are the tradeoffs between message passing versus shared memory or a blackboard architecture?
How do you approach debugging and observability for agent systems that produce very long execution traces spanning many steps and possibly multiple agents?
How do you manage LLM API costs and rate limits for agents running over long time horizons, especially when multiple agents are running concurrently?
How would you design human-in-the-loop interrupt points in a long-running agent workflow, where a human needs to review or approve something before the agent continues?
What failure modes are specific to long-running agents, such as drift, looping, or runaway costs, and how do you mitigate them?

“This is where I spent most of my time and still felt like I left things on the table.”

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American Express·AI Engineer·Technical Phone Screen

Jun 2026

Had a deep-dive technical screen at Amex for an AI Engineer role, basically one long question about how memory works in AI agents. Pretty niche topic and the interviewer clearly knew their stuff, so there was nowhere to hide.

How does short-term or working memory function in an AI agent, and what are its limitations?
How would you design long-term memory for an agent that needs to persist information across sessions?
What is the difference between episodic and semantic memory in the context of an AI agent, and how would you implement each?
Walk me through different retrieval strategies for agent memory and when you'd pick one over another.
How would you handle forgetting or compaction in an agent's memory to keep it from growing unbounded?
How would you design the read and write APIs for an agent memory system?
How do you handle consistency and conflicts when multiple agents or sessions share the same memory store?
How would you evaluate whether an agent's memory is actually working well in production?

“I talked about the context window being the agent's active workspace and how everything outside it is effectively invisible unless you retrieve it.”

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American Express·AI Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at Amex for an AI Engineer position, focused almost entirely on fault tolerance and reliability for AI systems. Pretty deep dive, more infrastructure-heavy than I expected for an AI role.

How would you design a fault-tolerant AI system that can recover from failures, covering both stateless and stateful components?
How do you handle idempotency and exactly-once semantics when retrying LLM or tool calls?
What retry and circuit breaker strategies would you use around LLM and retrieval backend calls?
How would you design graceful degradation when a downstream model or retrieval backend becomes unhealthy?
How would you approach multi-region replication and failover for an AI system, including both model artifacts and runtime state?
How do you handle KV-cache invalidation, prompt versioning, and reproducibility when model versions change?

“This was the anchor question and it sprawled into everything else.”

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American Express·AI Engineer·Technical Phone Screen

Jun 2026

Technical interview for an AI Engineer role at American Express focused entirely on how you evaluate LLM and agent systems in production. One big meaty question that branched into a lot of sub-topics. Dense but interesting.

How would you design an end-to-end evaluation framework for an AI system that uses LLMs or agents? Walk through both offline and online approaches, how you handle agent-specific metrics, regression testing across model versions, and where human eval fits in. Also discuss what can go wrong with these setups.

“This was basically the whole interview packed into one question.”

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