AI Engineering
Learn LLMs, RAG, agents, secure tools, and production AI through practical insurance examples.
Scalability, availability, reliability, and latency
Scalability handles growing load. Availability means the system is accessible when needed; reliability means tasks produce correct, durable outcomes despite failures. Latency measures waiting, including queue time and user-visible completion.
Queues, caching, databases, and vector indexes
Queues decouple arrival from processing. Caches reduce repeated eligible work. Business databases store authoritative records; vector indexes store derived retrieval representations. Each has a distinct consistency and recovery contract.
Providers, failover, security, multi-tenancy, and observability
Provider failover chooses an allowed alternative after eligible failure. Multi-tenancy scopes records and operations; security enforces trust boundaries. Observability connects execution paths to their outcomes and resource costs.
Design 1: RAG chatbot
A conversational interface over an eligible knowledge corpus. It combines a document pipeline with a request-time answer pipeline.
Design 2: Customer-support agent
A bounded assistant for knowledge questions, account lookups, clarification, and escalation. It may perform approved low-risk actions.
Design 3: Insurance claim agent
A claim-preparation assistant inside an authoritative claim workflow. It gathers and drafts information without independently adjudicating claims.
Design 4: AI CRM agent
A staff assistant that summarizes interactions and proposes CRM updates or follow-ups. Its tools obey staff role permissions.
Design 5: Document processing system
An asynchronous pipeline turns uploads into validated extracted data and search indexes. It is primarily a workflow with AI stages.
Design 6: Email agent
A mailbox-aware assistant for triage, search, summaries, and reviewed outgoing messages. Sending is a controlled tool.
Design 7: Research agent
A bounded system gathers external evidence and produces a cited research draft. Its output is decision support, not verified universal truth.
Design 8: Multi-agent system
Several specialized model-driven components cooperate under a controlled coordinator. Each has a measurable task and narrow context.
Design 9: Enterprise knowledge assistant
A permission-aware assistant over many organizational sources and users. It requires identity-linked document access, freshness, and trustworthy source references.
What questions come before architecture?
What task should succeed, which data is authoritative, who may access it, what errors are costly, how fast it must respond, expected load, and available budget. Then decide whether deterministic code or AI is needed.
How do you scale agent traffic?
Estimate provider/tool call amplification, use admission controls and tenant quotas, isolate background work, and scale workers to real dependency capacity. More API replicas do not remove provider token limits or serialize conflicting thread updates…
How do you design degraded behavior?
Define acceptable alternatives per task: verified templates, search results, paused durable work, or staff escalation. Preserve data/permission/quality contracts and distinguish missing evidence, service failure, and unknown commit outcomes explicitly.
Design an insurance assistant in two minutes.
Use Next.js and an authenticated Node API, a bounded intent router, policy RAG, and authorized claim tools. Put draft validation, exact approval, and idempotent submission in deterministic services/workflows. Add scoped memory, durable state where needed,…
Difficulty is an editorial estimate. Completion means you chose to mark a question as understood.