Today's LLM & Agent Engineering drill covers four core concepts: the workflow-vs-agent line is really a spectrum of agency, where every notch toward autonomy buys flexibility at the cost of predictability, latency, and money; the agent loop has four components (model, tools, context, stop conditions), and the part most candidates skip is the forced-stop half plus verification before declaring done; context engineering means treating the prompt like an API — instructions before data, untrusted content delimited, and layout that accounts for lost-in-the-middle; agent evaluation needs span-level scoring (did it pick the right tool, was the retrieval relevant) rather than grading only the final output, because tracing and evaluation are not the same thing. The practice question synthesizes agent-design and evaluation scenarios from the AI-Engineer-Interview-Questions GitHub repo (original composite, not a leaked company question): design a knowledge-base support agent that can escalate to a human ticket, chaining the reasoning from 'should this even be an agent' through to 'evaluation can't just look at the final output.'
Today's LLM & Agent Engineering round takes a different angle than the RAG-vs-agent and context-engineering ground already covered in previous weeks, and focuses on the part candidates most often haven't prepped for — and interviewers most love to probe: the three failure modes agents run into once deployed, each needing a distinct fix (infinite loops, wrong tool selection, malformed arguments); why max_turns alone isn't enough for termination — you need step budgets, cost ceilings, and an independent goal-completion check layered together; why agent evaluation should split into three independent metrics — tool selection quality, action advancement, and context adherence — rather than a single pass/fail; and why guardrails should route by confidence or dollar-impact threshold, auto-executing low-risk actions and sending anything above the line to human review. The practice question is a real reported AI Engineer interview prompt, 'What are the main failure modes of agents and how do you handle them?', walked through as a full answer combining failure-mode defenses, termination design, and evaluation metrics.
Today's LLM Engineering rotation covers: when to reach for a complex agentic system instead of simple RAG (weigh business value, not just the benchmark number), why context engineering isn't the same thing as prompt engineering (lost-in-the-middle, tool scoping, memory management), the three categories of LLM evaluation — offline, guardrails, and online — and what each is actually for, how to layer guardrail defenses (input filtering, output validation, an eval harness, red teaming), and a full breakdown of a question adapted from real OpenAI interview-prep material on RLHF reward overoptimization: the reward model is only a proxy for human preference, PPO will exploit that proxy's blind spots, the real bottleneck is annotation quality rather than compute, and the actual fix is switching to a verifiable reward (RLVR).
This round of LLM & Agent Engineering focuses on the architecture decisions interviewers dig into most in 2026: RAG and agents aren't an either/or choice but a division of labor — RAG handles cited, grounded policy Q&A, while an agent only steps in when the task needs multi-step, cross-system actions. Guardrails split into offline evaluation (catching regressions before release) and online interception (blocking unsafe outputs in real time), governed by one core principle: the model proposes, and a deterministic system authorizes and executes. On evaluation, beyond standard LLM-as-a-Judge scoring, agents also need trajectory-based evaluation that checks each intermediate decision, not just the final answer. Today's practice question is a PracHub-compiled 'design an enterprise support agent' system design prompt, drilling how to separate the RAG path from the agent path and make the authorization layer the centerpiece of your answer.
LLM & Agent Engineering interviews don't test whether you can wire up LangChain — they test whether you can turn a vague 'something's broken' into a set of testable sub-hypotheses. Today covers four high-frequency concepts: why RAG's 'retrieval is right but the answer is wrong' gap needs retrieval and generation evaluated as two separate systems; why a bigger agent context window doesn't fix quality decay, because the real problem is context pollution (noise diluting attention) rather than too few tokens; the decision logic between RAG and fine-tuning — 'RAG owns knowledge, fine-tuning owns behavior'; and why LLM-as-judge carries consistency and self-preference bias and can't stand alone as the only metric. The practice problem is a common enterprise scenario question — retrieval accuracy is 90% but answer accuracy is only 60%, how do you diagnose it — walking through how to turn a vague symptom into verifiable hypotheses.
AI Engineer interviews in 2026 no longer just ask 'can you build a RAG pipeline' — they test whether you can make defensible tradeoffs under real failure modes. Today covers five topics: when RAG should become agentic RAG, how production context windows are assembled layer by layer and the lost-in-the-middle problem, how guardrails stop malicious input and output, the RLHF reward-model training loop, and how to tell retrieval failure, generation failure, and infinite agent loops apart from a trace.