Agoda EM Behavioural Interview: STAR Format for LLM Teams

Crack Agoda's Engineering Manager behavioural interview for LLM back-end roles using STAR format — with real examples, insider tips, and exact phrasing.

What This Interview Is Really Testing#

Here's the thing most candidates miss when they prep for a behavioural interview at a company like Agoda: it's not a quiz about your past. It's a live assessment of how you think, lead, and adapt — especially in the fast-moving, ambiguous world of LLM-powered back-end systems.

When I've sat on the interviewer's side of the table for senior EM roles, I'm listening for three things simultaneously:

• Can this person lead engineers through genuine technical uncertainty?
• Do they make decisions with incomplete information, or freeze up?
• Can they balance engineering excellence with business delivery — specifically in AI/ML contexts where the ground shifts weekly?

Agoda operates at massive scale — billions of search queries, real-time pricing, and increasingly, LLM-powered features for travel recommendations and customer support. Their EMs in LLM back-end roles need to manage teams that are, frankly, figuring things out as they go. The interviewer is checking if you can do that without falling apart.

The STAR Format: What It Is and Where Everyone Goes Wrong#

You already know STAR — Situation, Task, Action, Result. But here's what I see constantly: candidates nail the Situation and totally butcher the Action and Result.

A weak STAR answer sounds like this:

"We had a problem with our LLM latency, so we optimised it and the team was happy."

A strong STAR answer sounds like this:

"We had a p99 latency of 4.2 seconds on our prompt-completion pipeline, which was killing the product's conversion rate. My task was to cut that to under 1.5 seconds in 6 weeks without sacrificing output quality. I restructured how we approached the problem — let me walk you through exactly what I did and why..."

See the difference? The second one is specific, personal, and sets up a compelling story. That's what gets you the job.

The STAR Upgrade: Add the "L" for Learning#

For senior roles, I always tell candidates to use STARL — add a Learning component at the end. Interviewers at Agoda-level companies want to see that you reflect, iterate, and grow. Finishing with "Here's what I'd do differently" is a signal of maturity that separates good managers from great ones.

Core Behavioural Themes for LLM Back-End EM Roles#

Before we get into sample answers, let's map the territory. Agoda's LLM back-end EM interviews will almost certainly probe these themes:

Pick 5-7 strong stories from your career and map them to multiple themes. A good story about an LLM incident can answer questions about technical leadership, stakeholder communication, and team development.

Sample STAR Answer #1: Managing Technical Ambiguity in an LLM Project#

The question: "Tell me about a time you had to lead your team through a project with significant technical uncertainty."

Here's how a strong candidate might structure this:

Situation: At my previous company, we were tasked with integrating an LLM-based itinerary summarisation feature into our travel booking back end. This was early-stage — we had no established patterns for prompt engineering at scale, no internal expertise in managing token costs, and our existing microservice architecture wasn't designed for the latency profile of LLM API calls.

Task: As the EM, I needed to take a team of 6 engineers — none of whom had production LLM experience — and ship a working back-end service in 10 weeks. Leadership expected a demo for a major investor meeting.

Action: Here's what I actually did, step by step:

1. I ran a two-week spike phase — deliberately protected time for the team to experiment, fail, and learn without delivery pressure. I shielded this from stakeholder pressure by reframing it as "risk mitigation" rather than "exploration."
2. I introduced a tiered confidence framework for our technical decisions. I categorised each architecture choice as High Confidence (we'd done this before), Medium (we had some signal), or Low (pure experiment). This helped the team make decisions without false certainty.
3. I partnered with a data scientist who had LLM expertise and embedded them part-time with my team — not as a consultant, but as a working collaborator.
4. I set up a lightweight prompt versioning system so engineers could track what prompts were in production and roll back safely — treating prompts like code.

Here's a simplified version of the prompt versioning schema we designed:

# Simplified prompt registry structure
prompt_registry = {
    "itinerary_summary_v1": {
        "template": "Summarise the following travel itinerary in 3 bullet points: {itinerary}",
        "model": "gpt-4-turbo",
        "max_tokens": 300,
        "deployed_at": "2024-03-01",
        "owner": "backend-llm-team",
        "status": "active"
    },
    "itinerary_summary_v2": {
        "template": "You are a travel expert. Given this itinerary: {itinerary}\nProvide a concise 3-point summary highlighting key highlights and logistics.",
        "model": "gpt-4-turbo",
        "max_tokens": 350,
        "deployed_at": "2024-03-15",

Result: We shipped on time. The service handled 50k requests/day at launch with p95 latency under 2 seconds. Token costs came in 30% under our initial budget estimate because the spike phase surfaced prompt efficiency patterns early. The investor demo went well, and the team — who started the project anxious about LLMs — left it confident and excited.

Learning: I'd embed the data scientist from day one next time, not week two. The first week of the spike was less productive than it could've been because we were missing that expertise. I now advocate for cross-functional embedding as a structural pattern, not an afterthought.

Sample STAR Answer #2: Handling an LLM Production Incident#

The question: "Describe a time you managed a significant production incident. How did you lead your team through it?"

Situation: Three months after launching our LLM-powered customer support routing system, we experienced a silent degradation — the model started consistently misclassifying high-priority booking complaints as low-priority queries. No alerts fired because our monitoring was based on error rates, not output quality. We only caught it when a customer escalated and a support manager noticed the pattern.

Task: I needed to lead an incident response that was fundamentally different from typical back-end incidents — there was no stack trace, no 500 error, no obvious failure. The system was working but wrong.

Action:

1. I immediately declared an incident even though the system appeared healthy — this was a judgment call that not everyone agreed with. I made the call because the business impact was real even if the technical symptoms were hidden.
2. I assigned two parallel tracks: one team investigating what changed (model version? prompt? input distribution?), one team building a temporary manual escalation bypass to stop the bleeding.
3. I set up a war room cadence — 30-minute syncs, not hourly, because LLM debugging moves fast and decisions compound.
4. Root cause: our upstream data team had changed how booking complaint text was pre-processed, stripping punctuation that the model relied on for sentiment signal. A small upstream change had cascading downstream effects with no alert.

Here's a simplified version of the monitoring check we added post-incident:

import json
from dataclasses import dataclass
from typing import Optional
 
@dataclass
class LLMOutputQualityCheck:
    feature_name: str
    expected_distribution: dict  # e.g., {"high": 0.3, "medium": 0.5, "low": 0.2}
    tolerance: float = 0.1
 
    def check(self, observed_distribution: dict) -> dict:
        """Flag drift in output label distribution."""
        alerts = []
        for label, expected_ratio in self.expected_distribution.items():
            observed_ratio = observed_distribution.get(label, 0)
            drift = abs

Result: Full remediation in 4 hours. We implemented output distribution monitoring across all LLM features within the next sprint. We also introduced a contract test between our preprocessing service and the LLM input schema so future upstream changes would fail fast.

Learning: LLM systems need a fundamentally different monitoring philosophy than traditional back-end services. Error rates are a lagging indicator. Output quality distribution is the real signal. I now push for this as a non-negotiable in any LLM feature's definition of done.

Where Candidates Get Stuck#

Let me be direct about the most common failure modes I see in EM behavioural interviews for LLM roles:

• Being too vague about your personal contribution. "We decided to..." and "The team did..." are red flags. Use "I" when talking about decisions you made. Interviewers are assessing you, not your team's collective wisdom.
• Skipping the "why" behind decisions. Don't just say what you did — say why you chose that over the alternative. "I chose approach A over B because..." shows engineering leadership judgment.
• Treating LLM challenges like normal back-end challenges. If your stories don't reflect that LLM systems are probabilistic, hard to monitor, and sensitive to prompt and data changes, the interviewer will doubt whether you actually worked in this space.
• Weak results. "The team was happy" is not a result. "We reduced p95 latency by 60%, cut token costs by 30%, and shipped on schedule" is a result. Quantify everything you can.
• No learning moment. Senior EMs who never mention what they'd do differently come across as overconfident or unaware. Always include a genuine reflection.

How to Talk Through This in an Interview#

Here's the exact phrasing framework I coach candidates to use:

Opening the story:

"Let me tell you about a situation that I think directly maps to this — at [Company], we were building [X], which had [specific challenge]..."

Transitioning to your actions:

"Here's what I specifically decided to do, and let me explain the reasoning behind each call..."

When you made a controversial call:

"This wasn't a universally popular decision — some of the team pushed back because [reason]. Here's how I thought through it and why I held the position..."

Delivering results:

"The measurable outcome was [specific metric]. But beyond the number, what I was most proud of was [qualitative outcome — team growth, process change, etc.]"

Closing with learning:

"Looking back, the one thing I'd change is [honest reflection]. It's something I've carried forward into how I approach [similar situations] now."

Follow-Up Questions to Expect — and How to Handle Them#

Agoda interviewers will probe your stories. Here's what they'll ask and how to stay sharp:

• "What would you have done if the timeline wasn't flexible?" — This tests adaptability. Have a clear answer about what you'd cut or change, not just "we would have found a way."
• "How did you keep the team motivated during the uncertainty?" — Talk about psychological safety, transparent communication, and how you protected space for learning.
• "What did you learn about managing LLM-specific risk?" — This is a chance to show depth. Talk about output quality monitoring, prompt versioning, model dependency management.
• "How did you communicate the incident to stakeholders?" — Show that you can translate technical complexity into business impact without dumbing it down or catastrophising.

Red Flags to Avoid#

These are the things that make interviewers quietly lose confidence in you:

• Using jargon without explaining it — if you say "RAG pipeline" or "context window management," be ready to explain the business implication, not just the technical definition
• Blaming the team, product, or external constraints for failures without owning your part
• Describing situations where you had no technical opinion — senior EMs at Agoda are expected to have informed technical views, even if they delegate the final call
• Giving canned, rehearsed-sounding answers — interviewers can tell, and it makes them wonder what you're hiding
• Forgetting to connect your story to why it matters at Agoda specifically — if you can, tailor your examples to travel tech, high-volume systems, or AI-driven product features

Key Takeaways#

Walk into your Agoda EM behavioural interview remembering these:

• Prepare 5-7 versatile STAR stories that map to multiple themes — technical leadership, incidents, team growth, cross-functional work, and prioritisation
• Use STARL — always end with a genuine learning that shows reflection and growth
• Quantify your results — latency numbers, cost reductions, team retention, delivery timelines
• Make your LLM experience specific — talk about prompt versioning, output quality monitoring, probabilistic debugging, and model dependency management
• Own your decisions — use "I decided" and explain the reasoning, don't hide behind "the team"
• Prepare for follow-up probes — every strong story will be challenged; treat it as a conversation, not a monologue
• Connect your stories to Agoda's context — scale, travel tech, real-time systems, and AI-powered features

The interviewer isn't looking for perfection. They're looking for clarity of thought, ownership, and evidence that you can lead a team through the ambiguity of building with LLMs at scale. If your stories show that, you're already ahead of 80% of the candidates they'll see.