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Early-stage Startup·Software Engineer·Onsite - System Design / Architecture·Senior

Senior
May 2026

Summary

System design round where the interviewer actually demoed the product first before asking me to design it. The question was essentially: build a ChatGPT backend, which sounds straightforward until you realize the real scope is a multi-agent orchestration layer.

Questions Asked (1)

Q1

Design the backend system for a ChatGPT-like application, including how you would architect a multi-agent orchestration layer.

System DesignTechnical Trade-offsAPI & Integrations
Author's notes

The demo at the start was a nice touch but also kind of disorienting.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then propose a high-level architecture covering API gateway, stateless services, data stores, and LLM integration. Dedicate significant time to the multi-agent orchestration layer, explaining how agents are defined, how they communicate, and how the system handles state and failures.

Pro tip: Emphasize trade-offs and incremental delivery: for an early-stage startup, propose a phased approach starting with a simple orchestrator and evolving to a more complex multi-agent system as needs grow.

1. Clarify Requirements and Scale

Ask about expected user load, latency requirements, model choices, and whether multi-agent orchestration is needed from day one. This ensures your design is appropriately scoped.

2. High-Level Architecture

Outline core components: API gateway, authentication, session management, conversation service, LLM service, and data stores (e.g., Redis for sessions, Postgres for history, vector DB for embeddings).

3. Multi-Agent Orchestration Layer

Describe how agents are defined (e.g., via configuration or code), how they communicate (e.g., message queues, gRPC), and how the orchestrator routes tasks, manages state, and handles failures.

4. Data Flow and Integration

Explain the end-to-end flow: user input -> API -> orchestrator -> agent(s) -> LLM -> response. Highlight how streaming, context management, and tool integrations (e.g., APIs) are handled.

5. Trade-offs and Scalability

Discuss trade-offs (e.g., latency vs. cost, consistency vs. availability) and how to scale horizontally, including caching, rate limiting, and monitoring.

Key Points to Mention

  • Use of message queues (e.g., RabbitMQ, Kafka) for asynchronous agent communication and decoupling.
  • State management for conversations: session storage, context window management, and persistence.
  • Agent registry and discovery: how agents are registered, versioned, and discovered by the orchestrator.
  • Error handling and retries: idempotency, dead-letter queues, and fallback strategies.
  • Observability: logging, tracing, and metrics for agent performance and LLM calls.
  • Security: authentication, authorization, and rate limiting to prevent abuse.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.