Meta·Software Engineer·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Meta SWE interview with a systems-thinking coding question. The problem was more about wiring together a loop correctly than any clever algorithm, which I didn't fully appreciate until I was mid-explanation.

Questions Asked (1)

Implement a ReAct-style agent execution loop given three APIs: one that calls a language model, one that parses its output, and one that executes a tool. The loop should run up to a max number of steps, accumulate context in a prompt, and terminate early if a final answer is found.

API & IntegrationsSystem DesignTechnical Trade-offs

Author's notes

I kept wanting to jump into edge cases before I even had the happy path written, which burned time.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the interfaces of the three APIs and the expected behavior of the loop. Then outline a stateful loop that maintains a prompt/context, calls the LLM, parses the output to decide between tool execution and final answer, and repeats until max steps or final answer. Emphasize termination conditions, context accumulation, and error handling.

Pro tip: Mention that you would log each step's input/output for debugging and observability, and discuss how to handle parsing failures or tool errors gracefully—this shows production maturity beyond just coding the happy path.

1. Clarify APIs and Requirements

Ask about the exact signatures of the LLM call, parser, and tool executor, and confirm the expected output format (e.g., JSON with action and action_input). Clarify max_steps and whether the final answer should be returned or just detected.

2. Design the Loop Structure

Outline a for-loop up to max_steps. In each iteration, call the LLM with the current prompt, parse the output, and branch: if final answer, return it; else execute the tool and append the result to the prompt.

3. Manage Context Accumulation

Explain how to build the prompt by concatenating the initial question, previous thoughts/actions/observations, and the latest tool result. Mention token limits and potential truncation strategies.

4. Handle Termination and Errors

Describe early termination when a final answer is parsed, and what to do if max steps are reached (e.g., return a failure message). Also cover handling parse errors or tool execution failures by adding error observations to the prompt.

5. Discuss Trade-offs and Extensions

Talk about trade-offs like step limit vs. cost, prompt length vs. context retention, and potential improvements such as caching, parallel tool calls, or adding a fallback mechanism.

Key Points to Mention

State management: maintaining a growing prompt/context across iterations
Parsing logic: distinguishing between tool calls and final answers, and handling malformed outputs
Termination conditions: max steps and final answer detection
Error handling: retries, fallbacks, and logging for tool failures or LLM errors
Efficiency considerations: token limits, prompt truncation, and cost of repeated LLM calls
Observability: logging each step for debugging and monitoring

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