decagon Software Engineer Interview: Questions & Prep (2026)

Decagon builds AI agents that handle customer support for enterprise companies. The product sits at the intersection of large language model infrastructure, retrieval pipelines, and real-time conversation management. With 117 open Software Engineer roles as of July 2026, Decagon is one of the more active AI-startup hirers in the market right now.

Candidates typically move through a multi-stage process: a recruiter screen, a live or async coding round, a system design session focused on AI or distributed systems, and one or more behavioral interviews. The exact structure varies by team, so it is worth confirming the format with your recruiter early.

For context on market salaries, knok jobradar data shows Software Engineer roles in India ranging from 6-12 LPA at entry level (0-2 years), 15-25 LPA at mid level (3-5 years), 28-45 LPA at senior level (6-9 years), and 40-65+ LPA at lead or staff level (10+ years). Decagon-specific figures can be found on Glassdoor or levels.fyi, though sample sizes for younger AI companies tend to be small.

Based on candidate reports and the nature of Decagon's product, here are the questions that come up most frequently in Software Engineer interviews.

1. Walk me through how you would design a scalable system that routes incoming customer messages to the right AI agent.
2. How would you handle a situation where a language model returns inconsistent or hallucinated responses in a production context?
3. Describe a time you improved the latency of a service under real traffic constraints. What did you measure and how did you prioritize the fix?
4. How do you approach writing tests for non-deterministic systems, such as those powered by a language model?
5. Tell me about the most complex debugging session you have tackled. What made it hard and how did you resolve it?
6. How would you design an evaluation framework to measure whether an AI agent is improving or regressing over time?
7. Describe a time you had to push back on a product requirement. What happened and what was the outcome?
8. How do you decide between building something in-house versus using a third-party library or API?
9. Walk me through a system design for a retrieval-augmented generation pipeline that must stay current with a client's knowledge base.
10. Tell me about a time you collaborated with a non-engineering stakeholder to ship a feature. How did you manage differing expectations?
11. How would you handle a production incident at an AI company where the root cause is not clear from logs alone?
12. If you were asked to improve the reliability of a conversational AI system that occasionally fails silently, what would your first steps be?

Q: Describe a time you improved the latency of a service under real traffic constraints.

*Situation:* At my previous company, our search API was adding noticeable delay to every customer-facing query during peak hours, and complaints were coming in from the product team.

*Task:* I was asked to reduce response time without a full rewrite, because a rewrite would have taken months we did not have.

*Action:* I started by adding distributed tracing to understand where time was actually being spent. I found that slow database queries were responsible for most of the delay. I added a composite index on the most frequently queried columns, introduced a short-lived cache for repeated lookups that changed infrequently, and removed a redundant serialization step I found during profiling.

*Result:* Median response time dropped significantly, complaints from the product team stopped, and the fix required no schema migration or downtime.

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Q: How did you handle a situation where a technical decision you made turned out to be wrong?

*Situation:* I chose to use a document store for a feature that turned out to need complex relational queries. By the time this became clear, the feature was already in beta with real users.

*Task:* I had to fix the data layer without disrupting beta users or delaying the full launch by more than one sprint.

*Action:* I ran a dual-write migration: new writes went to both the document store and a relational table simultaneously. I then migrated reads to the relational table incrementally, verified data consistency row by row, and retired the document store writes once confidence was high.

*Result:* The migration finished within two sprints with no user-facing disruption, and the feature launched on the revised schedule.

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Q: Tell me about a time you had to learn a new technology quickly to deliver something important.

*Situation:* Our team decided to add a vector similarity search feature to the product, and I had no prior experience with vector databases or embedding models.

*Task:* I owned the backend integration and had roughly one sprint to go from zero knowledge to a working prototype.

*Action:* I read the documentation for the two most widely used vector stores, ran experiments on our own data to compare retrieval quality, picked the one that fit our latency budget, and integrated it behind a feature flag. I then paired with a senior engineer for a code review before anything went to staging.

*Result:* The prototype was ready on time, passed code review on the second attempt, and later became the foundation for a larger retrieval pipeline used across the product.

STAR (for behavioral questions): Structure every behavioral answer as Situation, Task, Action, Result. Keep Situation and Task brief, one or two sentences each. Spend most of your time on Action, because that is where interviewers judge your technical depth. End with a concrete Result, even if it is qualitative.

PEDALS (for system design): Problem clarification, Estimation of scale, Data model, API design, Low-level component design, Scaling considerations. At Decagon, add an AI-specific layer: how does the system handle model failures, how do you evaluate output quality, and how do you keep the knowledge base current.

Situation-Complication-Resolution (for product trade-off questions): Describe the situation, name the complication that made it non-trivial, and explain the resolution you chose and why. This works well for questions like 'How do you decide build versus buy?' or 'How did you push back on a requirement?'

Think-aloud habit: For live coding and debugging rounds, Decagon interviewers typically want to hear your reasoning as you work. Narrate your assumptions before writing code. If you get stuck, say what you have ruled out rather than going silent.

Decagon is building AI agents that enterprise clients depend on for customer-facing interactions. That shapes what they look for in every engineering hire.

Comfort with ambiguity. AI systems behave differently from deterministic software. Interviewers want to see that you can reason about probabilistic outputs, define evaluation criteria, and make pragmatic decisions even when ground truth is not obvious.

Ownership mindset. Decagon is a smaller company with a fast-moving product. They want engineers who notice problems and fix them without waiting to be asked, not engineers who stay in their assigned lane.

Strong debugging instincts. Because the product involves language models, retrieval pipelines, and real-time messaging, bugs are often subtle and cross-layer. Candidates who can describe a structured debugging process stand out.

Clear communication. Many of Decagon's clients are non-technical enterprise buyers. Engineers who can translate technical complexity into plain language score well on behavioral rounds.

Genuine curiosity about AI. You do not need to be a machine learning researcher, but you should have authentic interest in how language models work, where they fail, and how to build reliable systems around them.

Week 1: Foundations
1. Solve five to seven medium-level coding problems focused on arrays, graphs, and dynamic programming. Use a timer to simulate interview pressure.
2. Read up on how retrieval-augmented generation (RAG) pipelines work at a conceptual level. Decagon's product relies on this pattern heavily.
3. Write down three to four strong STAR stories from your past work covering: a technical failure you learned from, a time you improved a system, a time you disagreed with a decision, and a cross-functional collaboration.

Week 2: System Design and AI Specifics
1. Practice designing one distributed system per day. Focus on message queues, caching strategies, and API gateways.
2. Add an AI-specific layer to each design: how would you evaluate model output, handle model downtime, or version a prompt in production?
3. Study common failure modes of language model applications: hallucination, context length limits, latency spikes, and silent failures.

Week 3: Mock Interviews and Polish
1. Do at least two timed mock interviews with a peer or through an online practice platform.
2. Re-read the job description and map each listed requirement to a story or project from your own experience.
3. Prepare two or three thoughtful questions to ask the interviewer about Decagon's engineering culture, on-call practices, or how they measure agent quality.

While you prepare, knok checks 150+ job sites nightly, applies to Software Engineer roles that match your resume, and messages HR for you, so you do not miss a Decagon opening while you are focused on prep.

Treating AI questions like generic software questions. If asked how you would improve a system powered by a language model, a generic caching or indexing answer is not enough. Show that you understand the unique failure modes of AI systems.

Vague STAR answers. Saying 'I improved performance' without a concrete result feels thin. Interviewers want to know what changed and how you knew it was better.

Skipping clarifications in system design. Jumping straight into drawing components before asking about scale, consistency requirements, or failure tolerance is a common red flag. Ask first, then design.

Showing no curiosity about the product. Candidates who ask zero questions about how Decagon's agents work, or who clearly have not researched AI support tools, tend not to advance past the behavioral rounds.

Overthinking the coding round. Spending too long finding the theoretically optimal solution while the interviewer waits for a working one is a common trap. Get something running first, then optimize with your remaining time.

Not naming your trade-offs. When you make a design choice, say what you are giving up. 'I chose eventual consistency here because it gives us better write throughput, but it means reads can be stale briefly' is much stronger than just picking eventual consistency without explanation.