The Agent-Infrastructure Interview Ramp
A phased reading route for a tool-calling, webhook, and sandboxed-execution interview: the method, the mechanics every question touches, and the four core designs — read as explainers — then the papers that built the field, on research.rudrite.com.
Phase 1 — The method
The shape of the hour, before any content — the frame the rest of this ramp hangs on.
- Preparing for the Agent-Infrastructure Interview — The map of this exact interview shape — the two axes, the four question territories, the hour with its agent-infra deltas. Read this first; everything below assumes it.
- How to Design a System in 60 Minutes — The four-phase clock every HLD runs on, agent-infra round or not — learn the general spine before you specialize it.
- Back-of-the-Envelope: the Numbers That Design Systems — Turn a user count and a tool-call rate into QPS and storage before drawing a single box — the arithmetic habit every round rewards.
Phase 2 — Mechanics every question touches
The primitives an agent-infra round leans on no matter which of the four designs it asks for.
- Rate Limiting: Four Algorithms, Honestly Compared — Every tool-calling platform throttles somebody — token vs leaky vs window, the admission control bolted onto every tool endpoint.
- Idempotency & the Exactly-Once Illusion — A retried tool call must not double-charge or double-send — the property that makes an agent’s retries safe.
- Design a Distributed Message Queue — Tool invocations and webhook deliveries both move through a queue somewhere — the durable hand-off underneath both.
- Design a DAG Job Scheduler — An agent’s plan is a DAG of dependent steps — the scheduler that runs it once, retries the failed node, and never repeats a step.
- Design a Multi-Tenant Query Engine on Object Storage — Thousands of tenants sharing one fleet of sandboxes and executors — the fairness and isolation problem under every agent platform’s infra bill.
- Design a Metrics & Monitoring System — Tool latency, error rate, sandbox CPU — the telemetry an agent platform needs to know it’s healthy, at a volume ordinary dashboards choke on.
Phase 3 — The four designs
The core of this interview shape — the flagship first, then the pieces that back it.
- Design a Tool-Calling Platform for AI Agents — The flagship design of this whole interview shape — a catalog, a router, an auth vault, an executor, one system end to end.
- The Tool Router: the Right 5 Tools out of 10,000 — The piece every tool-calling platform lives or dies on — picking the right tool from a crowded catalog without hallucinating one that doesn’t exist.
- Design a Webhook & Trigger Delivery Platform — Agents don’t only call out, they get called back — the delivery system that retries safely instead of firing a callback twice.
- Design a Sandboxed Code-Execution Service — Letting a model run arbitrary code is the single riskiest thing an agent platform does — the isolation boundary that makes it survivable.
Phase 4 — The papers
External, on research.rudrite.com — the research arc behind the systems above, read in the order the field actually built them.
- ReAct ↗ — Where the loop starts — interleave a reasoning trace with an action and an observation, instead of committing to a plan before you’ve seen what happens.
- Toolformer ↗ — How a model teaches itself which API call helps, rather than being told — the self-supervised trick behind every "the model decided to call a tool" claim.
- Gorilla ↗ — The paper that put a number on hallucinated API calls — and the retrieval trick that cuts them down once the tool catalog gets large.
- ToolLLM ↗ — The same idea at real scale — thousands of live APIs, not sixteen — and what breaks once the catalog stops fitting in a prompt.
- Voyager ↗ — A skill stops being a one-off completion and becomes code in a library the agent can call again — tool use turning into accumulated capability.
- Agent Workflow Memory ↗ — Past trajectories become reusable workflows instead of being thrown away after one run — memory that compounds instead of resetting every session.
- ToolRL ↗ — Tool use graded and reinforced instead of imitated — the reward design that teaches a model when calling a tool actually helped.
- τ-bench ↗ — The benchmark that asks the right question — not "did it call a tool" but "did the whole conversation end where the user needed" — how reliability actually gets graded.