Xai·Software Engineer·Onsite - System Design / Architecture·Staff

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Apr 2026Remote

Summary

xAI system design round, one big open-ended question about building an agentic LLM pipeline that produces a full hour-long movie. No leetcode, no behavioral fluff, just this beast of a problem for the whole session.

Questions Asked (1)

Design an agentic LLM workflow that can generate a full one-hour movie, covering agent roles, pipeline orchestration, how you maintain narrative and visual consistency across scenes, pacing across the whole runtime, shared memory between agents, evaluation checkpoints with human review, and compute/cost trade-offs.

System DesignTechnical Trade-offsProduct Sense & Ideation

Author's notes

This took me a second to even scope mentally.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal: a one-hour movie is a long-form narrative, so you need a hierarchical multi-agent system with a director agent overseeing specialized agents for script, storyboard, visuals, audio, and editing. Emphasize iterative generation with human-in-the-loop checkpoints and a shared memory store to maintain consistency, while balancing compute costs through caching and selective regeneration.

Pro tip: Frame your design around a 'movie as a state machine' where each scene is a state transition, and use a vector database for semantic memory to retrieve relevant context for consistency. Also, propose a tiered evaluation system: automated metrics for cheap checks and human review for critical junctures.

1. Define agent roles and hierarchy

Outline a director agent that orchestrates sub-agents: screenwriter, storyboard artist, visual generator, voice/audio synthesizer, and editor. Each agent has specific responsibilities and interfaces with the shared memory.

2. Design pipeline orchestration

Describe a sequential yet parallelizable pipeline: script breakdown into scenes, parallel generation of visuals and audio per scene, then assembly. Use a message queue or workflow engine (e.g., Airflow) to manage dependencies and retries.

3. Ensure narrative and visual consistency

Implement a shared memory system (e.g., vector DB) storing character descriptions, plot points, and visual style embeddings. Agents query this memory to condition their outputs, and a consistency checker agent flags deviations.

4. Manage pacing and runtime

Use a pacing agent that analyzes scene durations and emotional arcs against a target runtime, adjusting script length or suggesting cuts. Incorporate feedback loops to rebalance acts.

5. Integrate evaluation checkpoints and cost trade-offs

Place human review at key milestones (e.g., after script, after rough cut). Use automated metrics (e.g., CLIP score for visual-text alignment) for quick checks. Discuss compute trade-offs: caching generated assets, using smaller models for drafts, and scaling up for final renders.

Key Points to Mention

Hierarchical multi-agent architecture with a director agent for orchestration
Shared memory using vector databases for semantic consistency across scenes
Pipeline orchestration with parallelization and dependency management
Human-in-the-loop evaluation at critical checkpoints (script, storyboard, final cut)
Cost optimization via caching, model tiering, and selective regeneration
Pacing control through a dedicated agent that monitors runtime and emotional arc

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