The Coding / Technical Round
Most companies hiring for this role say the same thing up front: this is not a LeetCode round. What replaces it is closer to the job itself — debugging an agent that half-works, wiring up a small retrieval or tool-use slice, or reasoning about cost and latency trade-offs out loud. The bar is real, but it is a builder’s bar, not a competitive-programming one.
What to expect
Format varies by company, but the shape is consistent: a shared editor with the interviewer watching (sometimes pairing), or a take-home with a short debrief afterward. Either way, the problem is deliberately close to the actual job — debugging, small builds, and reasoning about behavior that isn’t fully deterministic — not an abstract algorithm.
| Problem type | What it looks like |
|---|---|
| Debugging an existing agent | You’re handed a small agent codebase where a tool call loops forever, a prompt silently drops context, or a retry swallows an error — find it and fix it while narrating your reasoning. |
| A small retrieval or tool-use slice | Wire up a minimal RAG step over a handful of documents, or add a single new tool to an existing agent loop — close to what Intermediate Chapters 3–6 of this curriculum build. |
| Token-budget / cost-control logic | Given a per-request token or dollar budget, add logic that truncates context, picks a cheaper model, or degrades gracefully instead of just failing. |
| Messy real-world data | Parse a document export, API response, or log file with missing fields and inconsistent formatting — the kind of input a production pipeline actually sees. |
| Prompt or eval iteration | Given a prompt that fails on a few example inputs, revise it and explain how you’d know the revision actually helped, not just that it feels better. |
| Take-home build | A handful of companies replace the live round entirely with a 2–4 hour take-home: build a small agent or RAG feature end to end and write up the trade-offs. |
You will not be asked to invert a binary tree or optimize a dynamic-programming recurrence. If a round does turn out to be LeetCode-style, that’s a signal the posting borrowed the title without the actual job behind it, worth noting as you evaluate the offer, not just the interview.
What actually separates a pass from a fail
The code quality bar is real but forgiving. What interviewers consistently flag, in both directions, has less to do with the final answer than with how you got there:
Clarifying questions before code
Asking “what should happen if the tool call fails?” or “is this latency-sensitive?” before typing reads as already thinking like someone who’ll own this in production.
Continuous narration
Silence for ten minutes reads as a red flag even if the final code is correct. The interviewer is grading how you think about agent behavior, not just what you output.
Treating non-determinism as normal, not surprising
Acknowledging that an LLM call might return something slightly different next time, and designing around that, is read as real experience rather than a first encounter with the problem.
Catching your own bugs
Nobody expects a perfect agent on the first pass. Noticing a dropped edge case yourself and fixing it reads far better than the interviewer having to point it out.
Pragmatic trade-off calls, said out loud
“I’d add a retry with backoff here in a real system, but I’ll skip it for time” beats silently skipping it, and beats silently over-building it too.
The most common failure mode isn’t a bad solution, it’s silence. A candidate who narrates a mediocre approach clearly will often score better than one who quietly writes a correct one. The interviewer can’t grade thinking they can’t see, and with agentic systems there’s usually more than one reasonable design, so the reasoning matters as much as the code.
How to prepare
Two weeks is enough if you’re already comfortable writing code. The goal isn’t to learn to code, it’s to rebuild the specific muscle of narrating, scoping, and shipping a small agent or retrieval feature under time pressure.
- 1
Week 1 — rebuild fluency with the tools you already know
Not algorithmic puzzles — practical fluency with agent loops, tool calling, and basic retrieval. If any of that is rusty, redo a couple of labs from this curriculum’s Intermediate track from scratch, without copying, narrating out loud as you go.
- 2
Week 2 — five debugging exercises, five eval exercises
Take five small broken agents or RAG pipelines and fix each in under an hour, out loud. Pair it with five short exercises in evaluating a prompt or agent change: precision/recall against a small labeled set, or an LLM-as-judge check, since “how do you know it worked” comes up in nearly every round.
- 3
Rehearse the take-home format too
Practice writing a short README explaining your trade-offs after a timed build. That write-up is often graded as closely as the code itself.
Two things to have ready before you walk in
Beyond practicing problems, two concrete artifacts change how the whole interview reads, because they let the interviewer evaluate real work instead of a simulated forty-five minutes of it.
A deployed agent demo, not something running on localhost
A URL beats a screen-share of your terminal every time. Take one of the agent builds from Intermediate Chapters 6–9 or the Advanced capstone and actually deploy it — a small FastAPI or Streamlit app on something like Render, Railway, Fly.io, or Streamlit Community Cloud is enough. Being able to say “here, try it” mid-interview is a different conversation than describing what it would do.
A clear answer to “how did you know it worked?”
Have one real example ready of measuring an agent or RAG change, even something small: a before/after eval score, a handful of hand-labeled test cases, a cost or latency number that moved. This question shows up constantly and a vague “it seemed better” answer is a common reason strong coders still get passed on.
One story about debugging something in production
Not the fuller version, save that for behavioral, but have the two-sentence version ready: what broke, how you found it, what you shipped to fix it. It comes up more than expected as a follow-up to the coding exercise itself.
Neither of these needs to be impressive at scale. A small, honestly-documented, actually-running thing beats an ambitious, half-finished one every time this comes up as a talking point.