Meta·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

May 2026

Summary

Interviewed for a Research Scientist role at Meta and got hit with a deep system design question on building an agentic research pipeline end to end. It was a single big question with a lot of sub-parts, which sounds manageable until you realize how many rabbit holes there are and how easy it is to lose the thread.

Questions Asked (1)

Design an agentic deep research system that takes a user query and autonomously plans and executes multi-step research including web search, source reading, evidence aggregation, and synthesis into a long-form cited answer. Cover the agent loop architecture, tool use, memory and scratchpad management, long context handling, latency and cost trade-offs, hallucination mitigation, and evaluation.

System DesignTechnical Trade-offsAdaptability & Ambiguity

Author's notes

This is a beast of a question.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scope, then walk through the agent loop architecture and tool integration, emphasizing how memory and context are managed. Discuss trade-offs around latency, cost, and accuracy, and explain how you mitigate hallucinations and evaluate the system. Conclude with a summary of key decisions and potential improvements.

Pro tip: Show that you prioritize user trust by designing for verifiability and transparency—e.g., citing sources and allowing users to inspect the agent's reasoning steps. This demonstrates product sense and maturity beyond pure technical design.

1. Clarify Requirements and Scope

Ask questions to understand expected query types, latency/cost constraints, and accuracy needs. Define success metrics and constraints before diving into design.

2. Design the Agent Loop and Tool Use

Outline the iterative plan-act-observe loop, including how the agent selects and invokes tools (web search, page reader, etc.). Explain how it decides when to stop and synthesize.

3. Manage Memory and Long Context

Describe the scratchpad for intermediate results, summarization/compression techniques, and retrieval-augmented generation to handle long contexts efficiently.

4. Address Trade-offs and Mitigations

Discuss latency vs. accuracy, cost vs. quality, and strategies to mitigate hallucinations (e.g., source grounding, verification steps).

5. Define Evaluation and Iteration

Propose offline and online evaluation metrics, human-in-the-loop checks, and A/B testing to continuously improve the system.

Key Points to Mention

Agent loop architecture: plan, act, observe, and reflect cycles with dynamic tool selection.
Tool integration: web search APIs, web scrapers, and possibly specialized knowledge bases, with error handling and retries.
Memory management: scratchpad for intermediate steps, summarization to compress context, and vector databases for retrieval.
Long context handling: chunking, hierarchical summarization, and retrieval-augmented generation to stay within token limits.
Latency and cost trade-offs: parallel tool calls, caching, model size selection, and budget-aware planning.
Hallucination mitigation: source citation, cross-verification, confidence scoring, and fallback to human review.
Evaluation: metrics like answer correctness, citation precision/recall, latency, cost per query, and user satisfaction.

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