An agent is a bounded call→observe→act loop, not a smarter chatbot. The loop, the agent-computer interface (tool design as a public API), structured tool errors, context engineering for the loop, and the interview questions that probe whether you understand why the harness — not the model — is the product.

• →Tool Interface Design

ReAct, plan-and-execute, reflexion, the workflow taxonomy, and the single-vs-multi-agent decision (it’s about who owns the write) — plus the recovery stack and the cost compounding that separate agents that ship from agents that loop, with the interview round that probes all of it.

• →Agent System Design

The Model Context Protocol turns the M×N tool/agent integration problem into M+N — clients, servers, transports, and the three primitives. When it helps, when in-process tools win, how teams run it at scale, and why it’s a trust boundary with real CVEs — plus the interview round that probes the tradeoff and the threat model.

• →MCP Integration
• →Agent Safety

Capability vs regression evals, grading the trajectory not just the answer, pass^k over pass@k, LLM-as-judge done right, and how teams build eval sets from real failures — the discipline that turns an agent demo into a shippable, non-regressing product, plus the interview round that probes all of it.

• →Agent Evaluation

“Works in staging, fails in prod” is an observability gap. Span-level traces of every action/tool/cost/failure, cost as the earliest regression signal, the online/offline eval loop, and shadow/canary rollout — how teams debug agents at scale, and the interview round that probes it.

• →Agent Observability

Indirect prompt injection (73.2% baseline ASR → 8.7% layered), the lethal trifecta, real zero-click incidents (GeminiJack, Slack AI, the 2026 agent hijacks), the design patterns that contain it (CaMeL, dual-LLM), and the defense-in-depth that actually works — break the trifecta, gate the writes — plus the security interview round.

• →Agent Safety
• →Agent System Design

Assemble a single agent that can inspect, act, and recover — bounded loop, state-on-disk, span traces, an eval gate, injection defenses, and per-task cost economics — then rehearse, end-to-end, the system-design and incident decisions an interviewer will push on. The whole track converges here.

• →Agent System Design
• →Agent Evaluation
• →Agent Observability