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IndiaQ1. What is an AI agent and how does it differ from a single LLM call?
Short answer: An agent plans and uses tools over multiple steps toward a goal, not one response.
Strong answer: An agent uses an LLM to reason about a goal, choose and call tools (search, code, APIs), observe results, and iterate over multiple steps — versus a single prompt→response call. This enables multi-step tasks but adds reliability, cost and control challenges. The value is autonomy; the risk is unpredictability.
Likely follow-up: What makes agents harder to make reliable than single calls?
What the interviewer is checking: Understanding of the agentic paradigm.
Common mistake: Describing an agent as just a longer prompt.
Q2. How do you design tools for an agent?
Short answer: Few, well-described, bounded tools with clear inputs/outputs and safe side effects.
Strong answer: Give the agent a small set of well-named tools with precise descriptions and schemas, validate inputs, make side effects explicit and reversible where possible, and return concise, structured results. Too many or vague tools confuse the model; risky tools need guardrails or human approval.
Likely follow-up: Why can too many tools hurt agent performance?
What the interviewer is checking: Practical agent design.
Common mistake: Exposing many overlapping or dangerous tools with vague docs.
Q3. How do you keep an agent reliable and prevent runaway loops?
Short answer: Bound steps and cost, validate between steps, add guardrails and human-in-the-loop.
Strong answer: Set max-step and budget limits, validate each step’s output, detect and break loops, add guardrails for risky actions (or require human approval), retry idempotently, and trace everything. Reliability engineering matters more than cleverness — an unbounded agent can loop or overspend fast.
Likely follow-up: How do you stop an agent from repeating the same failing action?
What the interviewer is checking: Agentic reliability, a 2026 focus.
Common mistake: Running an agent with no step/cost limits or tracing.
Q4. What are single-agent vs multi-agent architectures, and when use each?
Short answer: One capable agent is simpler; multi-agent splits roles but adds coordination cost.
Strong answer: A single agent with good tools is simpler and easier to debug. Multi-agent systems assign specialized roles (planner, researcher, critic) that collaborate — useful for complex, decomposable tasks — but add coordination, latency and failure modes. Start single-agent and only add agents when a task clearly benefits.
Likely follow-up: What new failure modes does multi-agent introduce?
What the interviewer is checking: Architecture judgment.
Common mistake: Reaching for multi-agent complexity by default.
Q5. How does an agent use memory?
Short answer: Short-term context plus external stores (vector/DB) for long-term recall.
Strong answer: Agents keep short-term state in the context window and use external memory (vector stores, databases) to recall facts across steps or sessions, retrieving relevant memories as needed. Good memory design avoids context bloat and keeps the agent grounded; poor memory causes forgetting or contradiction.
Likely follow-up: Why not just keep everything in the context window?
What the interviewer is checking: Memory-design understanding.
Common mistake: Stuffing all history into context, hitting limits and cost.
Q6. How do you evaluate and observe agentic systems?
Short answer: Trace every step, measure task success, and evaluate tool-use correctness.
Strong answer: Trace the full reasoning/tool-call sequence, measure end-to-end task success and cost, and evaluate intermediate steps (did it pick the right tool, with valid inputs?). Use labeled task suites and replay traces to debug. Observability is essential because failures hide in the multi-step process.
Likely follow-up: Why is step-level tracing critical for agents?
What the interviewer is checking: Agent observability.
Common mistake: Only checking the final answer, not the process.
Q7. What are the safety and security risks of agents with tool access?
Short answer: Prompt injection, unsafe actions and data exfiltration; mitigate with guardrails and least privilege.
Strong answer: Agents that browse or call tools are exposed to prompt injection (malicious content hijacking behavior), unintended destructive actions, and data leakage. Mitigate with least-privilege tool access, input/output validation, allow-lists, human approval for high-impact actions, sandboxing, and treating retrieved content as untrusted.
Likely follow-up: How does prompt injection threaten a tool-using agent?
What the interviewer is checking: Agent security awareness.
Common mistake: Giving an agent powerful tools with no guardrails.