End-to-end LLM application design concepts¶
This page captures topic framing from a curated eleven-concept list associated with production LLM and agent system design discourse (including threads by @ConsciousRide). It is an outline for later chapters, not a finished curriculum.
Concept map¶
- End-to-end LLM application design: User journeys, agent boundaries, and how inference, tools, and UI connect into one product surface.
- Latency, cost, and quality: Explicit tradeoffs across model choice, routing, caching, and depth of reasoning.
- Scalable inference: Throughput, autoscaling, queueing, and serving patterns for spiky agent workloads.
- GenAI data pipeline: Ingestion, chunking, embedding, refresh, and grounding data that agents depend on.
- Monitoring: Health, SLOs, and production signals beyond single-request success.
- Evaluation and A/B testing: Offline suites, online judges, regression detection, and controlled experiments.
- Security and prompt injection: Untrusted inputs, tool results, and sandbox boundaries as part of the threat model.
- Hybrid human-AI workflows: When automation stops and people approve, correct, or co-edit outcomes.
- Cost optimization: Token budgets, tool call ceilings, model fallbacks, and batch versus interactive paths.
- Disaster recovery and versioning: Safe deploys, checkpoint resume, artifact versioning, and rollback stories for long runs.
- Ethics and compliance: Logging, redaction, retention, and product policies for high-impact agent actions.
Sydney Runkle post (blocked fetch)¶
The post at https://x.com/sydneyrunkle/status/2052795546855752178 could not be retrieved automatically in this environment (HTTP 403 from X; an alternate reader endpoint timed out). Treat the URL as a manual follow-up for additional framing, especially harness-versus-runtime emphasis that overlaps the LangChain article by the same author.
A related public summary appears on LinkedIn: Deploying Agents: Harness vs Runtime Requirements.