AI Agents- Interview Preparation Part 2

Q1: Design a distributed system for training a massive foundational model (e.g., a 7B parameter LLM).

Goal: fit model & optimizer state in GPU memory, maximize throughput, minimize communication overhead. Key concepts & components: • Model Parallelism (Sharding): split model parameters across GPUs so each GPU stores only part of weights (layer-wise or tensor slicing). Reduces per-GPU memory need. Implemented via PyTorch FSDP or DeepSpeed ZeRO where optimizer state and gradients are sharded. • Data Parallelism: replicate the (sharded) model across multiple nodes; each replica processes different mini-batches and performs local backward; gradients are synchronized (all-reduce). • Hybrid Approach: combine model + data parallelism to scale both memory and throughput for very large models. • Communication fabric: high bandwidth, low latency interconnects (NVLink inside node, InfiniBand across nodes) and NCCL for efficient all-reduce. • ZeRO optimizer stages: ZeRO-1/2/3 progressively move optimizer states, gradients, and parameters off GPUs/replicas to reduce memory. • Checkpointing / Activation Checkpointing: trade compute for memory by recomputing activations during backward pass. • Storage & IO: parallel filesystems or object stores for checkpoints; staged prefetching to avoid IO stalls. • Autoscaling & orchestration: schedule jobs with K8s or cluster schedulers; preemptible spot instances can lower cost but require checkpointing. Why this matters: large models require carefully balancing memory, compute, and communication; design choices directly affect cost, convergence speed, and fault tolerance.

Q2: What are the trade-offs between monolithic and microservice architectures for an AI Agent platform?

Definitions: • Monolithic: single deployable process contains all functionality. • Microservices: system split into small, independently deployable services (retriever, ranker, policy, tool executor, etc.). Trade-offs explained: • Deployment & Complexity: Monolith is simpler to deploy and debug (single process, single logs). Microservices add operational complexity (service discovery, network management). • Scaling: Monolith must scale whole app; microservices let you scale only the heavy components (e.g., LLM inference) saving cost. • Fault isolation: Microservices can fail independently (circuit breakers), reducing blast radius; monolith failures often impact all functionality. • Team autonomy & tech heterogeneity: Microservices allow teams to choose appropriate languages/infra per service. • Latency & overhead: Microservices introduce RPC/network latency, serialization overhead, and increased testing effort. • Observability & debugging: More moving parts requires distributed tracing (OpenTelemetry) and structured logs. When to pick: small/simple systems → monolith; large, multi-model, multi-team systems with independent scaling needs → microservices.

Q3: How would you architect a RAG (Retrieval-Augmented Generation) pipeline for low-latency queries?

Goal: answer user queries with grounded LLM output fast and accurately. Pipeline components & optimizations: 1. Document preprocessing & chunking: split documents into semantically coherent chunks (passages) with overlap to avoid boundary loss. 2. Embedding precomputation: compute vector embeddings offline for all chunks, store in vector DB. 3. Vector Database (ANN): use a fast ANN engine (Milvus, Qdrant, Faiss backend) tuned with HNSW/IVF + quantization for memory/latency tradeoffs. 4. Index tuning: adjust index parameters (M/ef) to trade recall vs latency. 5. Caching: cache hot query embeddings, retrieval results, and common prompt→response pairs. 6. Parallelization & pipelining: run retrieval and LLM pre-processing in parallel; start LLM token generation as soon as partial context is ready (token streaming). 7. Low-latency LLM serving: use vLLM/Triton/TensorRT-LLM with dynamic batching and KV-cache reuse for short-latency inference. 8. Small verifier/evaluator: a lightweight faithfulness checker can filter hallucinations before returning result. 9. Monitoring & fallbacks: latency SLOs, degrade to cached answers or simpler models when load is high. Terminology: ANN = Approximate Nearest Neighbors; KV-cache = reuse transformer key/value caches to reuse context across calls.

Q4: Design a component for "Tool Use" or "Function Calling" within an Agent's architecture.

Component: Execution Handler (Tool Executor) — responsibilities and design: • Tool Registry: catalog of allowed tools: name, input schema (Pydantic), output schema, permission scope, timeout, and cost. • LLM Parser / Schema Enforcement: the LLM must return structured intent (e.g., JSON function call). Parser validates against Pydantic schema to avoid injection. • Sandboxed Execution: run tool calls in isolated environment (restricted filesystem, network ACLs, resource limits). Use ephemeral containers or function sandboxes (Firecracker). • Security & Access Controls: authentication, rate limits, and capability scoping (which agents/users can call which tools). • Result Normalization: convert raw tool output into a format the LLM expects; include provenance (timestamps, source). • Retry & Compensation: exponential backoff and idempotency keys for non-idempotent calls. • Observability: structured logs of tool calls, latency, errors for auditing and debugging. Why: tool execution must be trustworthy, auditable, and robust because agents interact with external systems and could otherwise cause harm.

Q5: What storage technologies would you select for a system that needs to store and serve billions of real-time user-feature vector data points?

Requirements: low latency reads/writes, horizontal scalability, and efficient similarity search for embeddings. Best-fit architecture: • Real-time features: Redis or Aerospike as low-latency key-value stores for frequently updated counters and session features. • Embeddings & similarity search: Vector DB (Pinecone, Milvus, Qdrant) optimized for ANN queries. Choose based on scale, index types supported (HNSW, IVF+PQ), persistence, and durability. • Cold/historical storage: S3 (object store) + data warehouse (Snowflake, BigQuery) for large-scale analytics and model training. • Consistency & streaming: Use a stream (Kafka) as source of truth for feature updates and changefeeds to keep KV stores and vector DBs in sync. Considerations: cost of replication, read/write patterns, consistency SLAs, backup & restore for vector indices, and ability to reindex.

Q6: Describe the role of API Gateways in securing and managing access to AI microservices.

API Gateway responsibilities: • Authentication & Authorization: validate tokens (JWT, OAuth), enforce roles & scopes for secure access. • Rate Limiting/Quota Management: protect expensive model endpoints from abusive or accidental spikes. • Request/Response Transformation & Validation: normalize client requests, enforce schemas, and reject malformed inputs before reaching model. • Routing & Versioning: route to correct microservice or model version (blue/green/canary). • Observability & Logging: central place for request metrics and traces. • WAF capabilities: simple filtering (block suspicious payloads) before reaching model. Why important for AI: model endpoints are expensive; gateway prevents misuse, enforces policy, and centralizes cross-cutting concerns.

Q7: How do you design for resilience in a multi-model agent system?

Resilience patterns & practices: • Circuit Breakers: stop calling a failing downstream to avoid cascading failure; open/half-open/closed states. • Retries with exponential backoff & jitter: for transient network errors. • Graceful degradation / fallbacks: replace heavy LLM calls with cached answers, smaller models, or template responses on outage. • Redundancy & Multi-AZ deployment: deploy replicas across availability zones/regions. • Health checks & auto-healing: liveness/readiness probes → autoscaler or orchestrator restarts. • Bulkhead partitioning: isolate failure domains (e.g., separate queues for high-priority vs low-priority). • Observability & SLOs: define SLAs, monitor error budgets, and use alerting + runbooks. Terminology: Circuit breaker = pattern to stop calls to failing service; bulkhead = isolate resources per function.

Q8: When designing a feature pipeline, what is the trade-off between streaming and batch processing?

Definitions: • Streaming: continuous, event-driven computation (Kafka, Flink). • Batch: periodic bulk processing (Spark, Airflow). Trade-offs: • Freshness: streaming provides low latency (near real-time features) suitable for personalization; batch produces aggregated/stable features (daily/ hourly) but is stale. • Complexity & cost: streaming systems are operationally complex and compute-intensive; batch is simpler and can handle large aggregations efficiently. • Consistency & recomputation: batch pipelines are easier to backfill and reproduce; streaming requires careful watermarking and late event handling. Typical design: hybrid (Lambda) → stream for real-time, batch for heavy aggregates; unify with a Feature Store (Feast) to avoid skew.

Q9: Explain the concept of 'State Management' for a conversational AI Agent.

State Management: storage and retrieval of contextual data across turns for coherence and personalization. Key elements: • Session State: immediate conversation history (utterances, timestamps). • Memory types: short-term (last n turns), long-term (user profile, preferences), episodic (previous actions/results). • Storage: low-latency KV (Redis/DynamoDB) keyed by session ID or user ID. • Consistency: atomic append/overwrite operations to avoid lost updates when parallel requests occur. • Privacy & retention: policies for what memory persists and for how long (GDPR). Why: prevents agents from starting fresh each turn and enables continuity, personalization, and tool re-use.

Q10: What is the primary motivation for decoupling the Feature Store from the Model Registry?

Reasoning & benefits: • Separation of concerns: Feature Store manages data transformations and online/offline serving of features; Model Registry manages model artifacts and metadata (versioning, lineage). • Reusability & stability: many models share same features; decoupling allows teams to update models without touching feature pipelines. • Avoid tight coupling & deployment risk: changing a model should not force reworking the feature serving infra and vice versa. • Operational control: separate SLAs, access control, and scaling strategies. Result: faster iteration, safer rollouts, and clearer governance.

Q11: How do you handle non-determinism when testing and deploying LLM-powered agents?

Sources of non-determinism: sampling temperature, beam search randomness, retrieval nondeterminism, async tool execution. Mitigations & testing strategy: • Deterministic modes: set temperature ≈ 0 and deterministic decoding for critical flows. • Large test suites & statistical evaluation: use many examples and measure aggregate metrics (mean, variance, CI) instead of single example matching. • Seed control & reproducibility: log RNG seeds and deterministic components. • Shadow / canary evaluation: run new behavior in shadow and compare distributions (token usage, latency, correctness). • Accept probabilistic correctness: define tolerance thresholds and use human validation for edge cases.

Q12: Describe the architecture and role of a sandbox environment in an AI Agent system.

Purpose: secure, isolated environment to execute untrusted code or interact with external systems safely. Design elements: • Isolation: containerization (Docker) or microVMs (Firecracker) limit kernel/system access. • Resource limiting: CPU, memory, disk, and network constraints to prevent DoS. • Filesystem & network ACLs: only allow explicit endpoints. • Timeouts & monitoring: cap execution time and track system calls. • Audit & logging: capture all tool outputs and commands for post-hoc review. Use cases: user-provided code execution, LLM-generated API calls, or third-party plugin invocation.

Q13: What is Dynamic Batching in LLM serving, and why is it crucial for cost efficiency?

Definition: runtime technique to merge multiple concurrent inference requests into a single GPU batch to amortize kernel launch overhead and increase GPU utilization. Benefits: • Throughput increases: more tokens processed per GPU second. • Reduced per-request cost: lower effective cost per inference. • Smoothing: handles spikes by grouping short requests. Considerations: latency SLOs vs throughput—excessive batching can increase tail latency; use adaptive policies (max latency cap). Engines: vLLM, TensorRT-LLM, NVIDIA Triton support dynamic batching/KV reuse.

Q14: Detail the components of a Traceability and Observability Stack for a multi-step agent.

Core stack pieces: • Metrics (Prometheus / Grafana): latency, token counts, queue lengths, tool-call success rates. • Structured logs (ELK / Splunk): prompts, responses, tool outputs, user IDs (PII redaction). • Distributed Tracing (OpenTelemetry / Jaeger): traces and spans for each LLM call, retrieval, tool call — reconstruct end-to-end flows. • Auditing & lineage: persistent traces tied to model versions and dataset versions for compliance. • Alerting & SLOs: set error budgets, anomaly detection for drift/hallucination spikes. Why: multi-step agents require visibility into every intermediate step to debug incorrect reasoning or data leaks.

Q15: How would you use a Canary Release strategy when upgrading a core LLM model used by an agent?

Approach: 1. Deploy new model as Canary (small % of traffic). 2. Collect metrics: latency, error rate, hallucination rate, business KPIs (conversion, task success). 3. Compare statistically (A/B testing or canary analysis) — watch for regressions. 4. Gradually ramp if stable; rollback automatically on anomalies. Key mechanisms: traffic split via gateway, mirrored logging, automated rollback triggers, and human oversight for safety-critical tasks.

Q16: Explain the difference between Data Parallelism and Pipeline Parallelism in LLM training.

Data Parallelism: entire model replicated across workers; each worker processes different data batches and synchronizes gradients. Pros: simple and scales well for moderate model sizes. Pipeline Parallelism: split the model across devices (layers assigned to GPUs) and stream micro-batches through pipeline stages. Pros: reduces memory footprint per device for very deep models. Hybrid: combine both to scale huge models; need micro-batching, activation checkpointing, and careful scheduling to avoid bubbles.

Q17: Describe the purpose of a Feature Store and how it prevents Training-Serving Skew in production.

Feature Store: a canonical layer for computing, storing, and serving features consistently for training and inference. Prevents skew by: • Single code path: same transformation logic applied offline & online. • Timestamps/backfills: consistent historical features for training and correct event time attribution to avoid leakage. • Online serving: low-latency feature retrieval for inference that matches training data shape. Example skew causes: computing aggregates differently in training vs online, or using future data by mistake. Feature Stores enforce commonality and lineage.

Q18: What is the main design consideration when choosing between an open-source LLM (self-hosted) and a proprietary LLM (API-based)?

Trade-offs: • Control & privacy: self-hosted gives full data control (no external API exposure), critical for sensitive data. • Cost & ops overhead: self-hosted needs GPUs, ops team, infra cost; APIs trade higher per-token cost for no infra. • Latency & customization: self-hosted can be tuned, fine-tuned locally; APIs may offer best-in-class quality but limited customization/speed dependent on network. • Compliance & SLAs: API providers may provide compliance attestations; self-hosted requires you to build compliance. Decision drivers: data sensitivity, team MLOps maturity, expected scale, and cost model.

Q19: How do you implement Confidence-Aware Routing for an AI Agent?

Mechanics: 1. Compute confidence signals: token log-probabilities, ensemble consensus, self-consistency votes, retrieval-overlap scores, or tool success indicators. 2. Define thresholds: high confidence → single cheaper LLM; medium → larger LLM or RAG; low → human review or restricted actions. 3. Implement routing layer that uses these signals to select model/handler. 4. Continuously calibrate thresholds using A/B experiments. Benefit: reduces cost and risk by only escalating uncertain cases to expensive or human workflows.

Q20: What is the role of Tokenization in the end-to-end architecture, and how does it impact latency?

Tokenization: converts raw text into sequence of discrete tokens the LLM processes (subwords via BPE, WordPiece, SentencePiece). Impacts: • Token count influences compute: more tokens = more transformer ops = higher latency and cost. • Granularity tradeoff: aggressive subword splitting reduces OOV errors but can inflate token count; good tokenizers keep token length compact. • Preprocessing time: tokenization itself must be low overhead; if done server-side repeatedly, it adds latency. Optimization: batch tokenization, reuse embeddings for cached inputs, and use efficient C++ tokenizers in production.

Q21: How would you design a multi-agent workflow orchestrator?

Components & behavior: • Task Graph / DAG engine (Ray, Airflow, Prefect): represent agents/tasks as nodes with dependencies and data contracts. • Coordinator / Controller: schedules agents, enforces timeouts, manages retries, aggregates outputs. • Message bus / event streaming: Kafka/Redis Streams for async inter-agent comms. • Shared storage & memory: feature store or vector DB for shared state. • Observability & tracing: trace interagent messages and decisions. Key policies: prioritization, resource allocation, isolation per agent to avoid resource contention, and idempotent processing.

Q22: How do you choose between synchronous vs asynchronous agent execution?

Synchronous: caller blocks until response (simple flow), suitable for low-latency single-step tasks. Asynchronous: caller receives token/result later (callbacks, webhooks) — useful when operations are long (tool calls, external API). Decision factors: SLOs (latency vs throughput), complexity of coordination (fan-out/fan-in), and user experience (immediate UX vs waiting). Use async for heavy tasks and orchestrate with state machines.

Q23: How do you prevent hallucinations in production LLM agents?

Strategies: • Grounding via RAG: force model to cite retrieved docs and prefer retrieval evidence. • Strict function calls: require structured outputs (schemas) for critical operations; validate results. • Confidence thresholds & verifiers: use a small verifier model or LLM judge to cross-check claims. • Post-processing with checks: rule-based or fact-check APIs for named entities & dates. • Prompt engineering: chain-of-thought moderation and constraint prompts. Monitoring: track hallucination metrics (human labels, automated checks) and create alerts.

Q24: How would you scale a multimodal agent (vision + text)?

Decomposition: separate encoders/decoders as microservices: image encoder (CNN/ViT) → produces embeddings, text LLM consumes embeddings via cross-attention or adapter layers. Scaling: keep vision encoder in GPU pool for batched inference; cache common image embeddings; use model distillation to create cheaper student models for high-volume inference. Latency patterns: pipeline image preprocessing and embedding generation before text generation; use async flows and reuse cached multimodal context when possible.

Q25: How to design a high-throughput inference cluster?

Key components: • Model servers optimized for GPU inference (vLLM, Triton) with support for dynamic batching and KV cache. • Load balancing with request routing and model sharding. • Autoscaling policies based on queue length and GPU utilization. • Edge caching & CDN for static responses. • Telemetry for token usage, per-request cost, and tail latency. Optimization techniques: mixed precision (FP16/BF16), quantization, kernel fusion, and batching windows to maximize utilization without violating latency SLOs.

Q26: What is an Agent Memory Store and how is it designed?

Purpose: persist agent memories to enable continuity & personalization. Design: • Short-term memory: recent turns kept in session store (Redis) for immediate context. • Long-term memory: compacted summaries or embeddings stored in vector DB or DB (Weaviate/Chroma). • Memory management: eviction policies, summarization to compress long histories, and privacy controls. Access patterns: fast read on turn start, append writes after each turn, and background summarization.

Q27: How do you design an Evaluation Harness for AI agents?

Components: • Synthetic and real test suites with diverse scenarios (OOD, edge cases, adversarial). • Deterministic replay of user sessions for regression tests. • Automated metric collection: success rate, tool accuracy, latency, hallucination rate. • Human evaluation pipelines: for subjective metrics and alignment. • Model gating: fail fast if regressions cross thresholds before promotion to prod.

Q28: How do you design a scalable embedding pipeline?

Flow: ingestion → text chunking → batching → GPU embedder workers → persist to vector DB. Scalability patterns: queueing (Kafka), autoscale worker pool, deduplication (hashing), incremental reindexing, and sharded vector DB indices. Maintaining freshness: TTL-based refresh, update streams for changed docs, and consistent hashing for rebalancing.

Q29: How do you prevent prompt injections and security risks in agents?

Mitigations: • Input sanitization & instruction stripping: remove suspicious system tokens or user content that tries to alter agent behavior. • Schema validation & Pydantic: require structured inputs/outputs for all tool calls. • Capability policy enforcement: ensure LLM cannot call sensitive tools without explicit scope. • Rate limits & monitoring: detect anomalous prompt patterns. • Sandboxing external actions and require human approval for destructive operations.

Q30: How do you design a human-in-the-loop (HITL) system for agents?

Design elements: • Escalation criteria: confidence thresholds or flagged outputs route to human reviewer. • Reviewer UI: show context, retrieved docs, chain-of-thought, and suggested corrections. • Asynchronous review & feedback loop: human corrections get logged and used to retrain verifiers or fine-tune models. • Operational metrics: review latency, reviewer accuracy, and cost. Goal: combine automation with human oversight to manage high-risk decisions while improving models via labeled corrections.

Weights

Both GPUs store [p1,p2,p3,p4]

GPU1: [p1,p2], GPU2: [p3,p4]

Gradients

both store all

sharded

Optim states

both store all

sharded

Replica

A full copy of the model

Shard

A part (slice) of the model