Decagon Forward Deployed Engineer interview questions

A 6% word error rate is a good number that measures the wrong thing. Per-digit accuracy of 98% loses 18% of ten-digit account numbers, and no aggregate metric contains that. Talking over callers is a turn-taking problem, not a model problem, and it has its own metrics too.

Word error rate is the number every vendor quotes and the one that predicts the least. The offline suite is built from confirmed real calls with noise and codecs applied, scored on entity accuracy, tail latency and false interruptions. Production adds what a recording cannot simulate: real callers and the reopen rate.

Sierra's signature design exercise. The grading hinges on three things most candidates underweight: tool API shape, the read/write trust boundary, and eval cases written before the agent. Walkthrough inside.

Serving 50 enterprise customers off one platform means four leak surfaces most designs miss, including the semantic cache and the eval set. A tier-by-tier isolation model with the noisy-neighbor math.

Standard A/B machinery assumes a crisp conversion event; agents give you fuzzy, delayed, gameable outcomes. The metric hierarchy, the reopen-window trick, and the sample-size reality check that win this question.

The data-isolation answer everyone gives misses the harder surface: live OAuth tokens and API keys the agent uses to act in third-party systems. The model that keeps one tenant's credentials out of another's agent loop, and out of the LLM context entirely.

Nobody deployed anything is true of your repository and false of the system. The model provider, a tool behind an API, the knowledge base and the traffic mix all change without a commit. Segmentation comes before theory: a broken intent and a mix shift produce the same headline and need different fixes.

A nightly suite of fifty simulated conversations per intent catches a collapsed tool with certainty and a five-point drift almost never. Knowing which is which, and sizing each layer for what it can actually see, is the whole design. The grader checks outcomes, not transcripts, or the agent learns to sound resolved.

This is the shape of the TypeScript debugging round at the agent companies: a forty-line stream handler that mostly works. Each bug has a one-line demonstration, and none of them is in the model. One is a stale closure, one is a decoder splitting a multibyte character, and one is a render per token.

The abstain path is designed last and decides whether the deployment is adopted. Routing to the right person, an SLA the queue can actually meet, and what happens to the case the agent already half-processed before it gave up.

A definition question wearing an experience question's clothes. Most candidates answer the resume half and flunk the hidden half: proving they know the operational difference between generation and agency. Here's the two-part frame.

Work trials and utilization-driven staffing are the 2026 startup FDE reality, and 'no questions, sounds great' is a failing answer. The reverse-interview questions that signal seniority, and the trial terms worth negotiating.

Two hard numbers in the contract: 95% accuracy and $0.50 per resolution. The job is an eval and rollout system that proves both hold on real traffic before you scale, with rollback triggers wired to the same numbers.

The warm-up that quietly filters out candidates who only know LLMs as a magic API. Nail the two-phase inference picture and you set up every later answer about latency, cost, and hallucinations.

Everyone can define it; few candidates know why 'advertised context' and 'usable context' aren't the same thing. The cost, latency, and attention caveats that make this answer stand out.

The warm-up that filters out tutorial-level candidates. Interviewers aren't grading the diagram, they're grading whether you know the three places real pipelines fail and how you'd see it happening.

Modern LLMs share one architectural skeleton. Learn the five components and you can read almost any model card cold, plus the single distinction that proves to an interviewer you actually understand it rather than the buzzwords.

Everyone says '500 tokens with overlap.' The candidates who get hired explain why chunk boundaries are a recall problem, name three strategies, and describe the experiment that picks between them.

Tokens drive your customer's bill, their latency, and the weird failures like botched arithmetic. The rough numbers every FDE should rattle off, plus the trap answers interviewers listen for.

Every FDE will face an angry customer asking this. The mechanistic explanation interviewers want (why hallucination is the default behavior, not a bug) plus the framing that defuses customer escalations.

Every queue, webhook, and retry loop you'll ever deploy at a customer hides this distinction. Interviewers use it to separate people who've shipped from people who've read about shipping.