Decagon AI Engineer interview questions

Token count is latency and dollars, not merely style. 'Be concise' hardly works, and max_tokens just cuts off mid-sentence. Here is how to genuinely shape length without amputating answers.

Your assistant scores 4.6 out of 5 on every individual turn and still loses the user by turn eight. Per-turn averages hide the failures that actually cancel subscriptions, and here is the session-level eval that catches them.

Agents get reached for too often. A strong answer pushes past the hype: most tasks are better served by a single structured call, and agents pay off only under particular conditions. Here is when one is worth it and how to stop it from compounding errors.

Conversations and agent tasks grow past the context window, and the naive 'stuff the whole history' approach breaks on cost, latency, and lost-in-the-middle. The signal is a tiered memory design: recent buffer, summarized mid-term, retrieved long-term.

Agents need memory beyond the context window, and 'just store the history' is the wrong answer. The signal is telling the memory types apart and mapping each to a storage and retrieval strategy. Here is the answer.

An agent that retries the same failed action 40 times means a runaway bill and a stuck user. Termination is something you build into the harness, not something the model reliably decides. Here is the control layer.

Real tools time out, rate-limit, and hand back malformed JSON. An agent that assumes every call succeeds is a demo, not a product. Here is the error-handling layer that keeps it running in production.

Real users type 'what about the new policy?' with no context. Retrieving on those four words yields noise. Here is how strong systems disambiguate before they ever reach the retriever.

When a chain of LLM calls and tools yields a wrong final answer, 'the model was bad' is not a diagnosis. You need visibility into every step. Here is the tracing layer that turns a black box into something debuggable.

Frameworks promise speed and hand you abstraction you'll eventually fight. Rolling your own is more code but full control. Here is the honest tradeoff, and why many production teams end up thinner than they started.

The industry uses 'agent' for anything that calls an LLM. The distinction that matters is how much control you hand to the model, and more autonomy is not better. Here is the spectrum and the decision rule.

A live-coding round that tells apart engineers who have shipped agents from those who have only read about them. Interviewers want a genuine plan-act-observe loop, tools that query a real SQL database, guardrails that block the obvious failures, and a concrete evaluation plan, rather than one prompt masquerading as an agent.

A context window is not memory. Real agents need a storage architecture: working buffer, episodic recall, and consolidated facts. The signal is the read/write paths and how you keep memory from decaying.

Autonomous agents still need humans at the right moments. The signal is designing checkpoints: where to interrupt, what to surface, how to persist and resume state, without reducing the agent to a click-through rubber stamp.

The CRM says the balance is $0 and the ledger says $412. An agent left to its own devices will average them into something confident and wrong. Designing the precedence before the model has to guess is the whole answer.

Agent latency is a step-count problem disguised as a speed problem. The winning move is removing round trips, not shaving milliseconds off each one, and knowing which step you can delete is the whole interview.

Support is the flagship applied-AI use case, exercising agents, RAG, tools, and the critical human handoff. The signal is knowing when to answer, when to take action, and when to escalate, safely.

An AI support agent working through millions of chats will occasionally be wrong, rude, or unhelpful, and no one can read them all. Watching quality at that scale is a design problem in itself, with its own failure modes.

Auto-resolving support tickets means grounding answers in your knowledge base, safely calling real APIs (refunds, account changes), and knowing when to hand off to a human. Learn the agent loop, the guardrails, and how to measure resolution without eroding trust.

Hype primes customers to expect a system that reads minds and never fails. Resetting that without losing the deal is an applied-AI skill interviewers probe head-on. Here is the move.

AI features break in public, sometimes embarrassingly. How you respond to the customer, not only the bug, is what this question actually tests. Here is the recovery that rebuilds trust.

An angry customer tests composure and ownership, not empathy alone. Interviewers want to watch you de-escalate, fix the real problem, and rebuild trust. Here is the arc that scores.

Telling a customer no is a relationship skill: shield them from a bad outcome while keeping their trust. Interviewers look for the reason, the alternative, and the framing. Here is the move.