Engineering deep dives: reference architecture, RAG, agent design, evals, security, performance, reliability, integrations, data, and LLMOps.

Engineering

Overview

Engineering guidance focuses on concrete implementation patterns and operational discipline. The goal is to help teams move from prototypes to production by making design trade-offs explicit and measurable.

Key topics

  • Reference architecture and component boundaries
  • RAG and knowledge grounding patterns
  • Agent design and tool governance
  • Evaluation, regression testing, and red teaming
  • Security posture and prompt-injection defenses
  • Performance, reliability, and cost control
  • Integration patterns and operational tooling
  • Data engineering and LLMOps practices

Common pitfalls

  • Treating agent behavior as ‘prompt magic’ without contracts or tests.
  • Shipping write-capable automation without idempotency and audit trails.
  • Overloading context with irrelevant chunks (token bloat, lower accuracy).
  • Ignoring permission boundaries during retrieval and tool execution.
  • No regression testing: quality silently degrades over time.

Recommended practices

  • Use strict schemas and structured outputs for critical steps.
  • Implement stateful orchestration with checkpoints and replay.
  • Adopt a golden set and run evaluations continuously.
  • Instrument traces and cost dashboards from day one.
  • Default to read-only and add approvals before automation.

This page is intended to be actionable for engineering teams. For platform-specific details, cross-reference /platform/agents, /platform/orchestration, and /platform/knowledge.