For people who keep asking what to build in AI Engineering.

➣ Build your own Context Assembler
(token-budgeted memory + retrieval + tools)

➣ Build your own Retrieval Stack
(chunker + BM25 + dense search + reranker)

➣ Build your own Model Router
(cost/latency/quality routing + fallbacks)

➣ Build your own Semantic Cache
(embedding similarity + hit-rate tracking)

➣ Build your own Agent Orchestrator
(deterministic state machine, no LangChain)

➣ Build your own MCP Server and Client
(raw JSON-RPC, no SDK)

➣ Build your own Multi-Agent Consensus
(weighted voting + judge + escalation)

➣ Build your own Sandboxed Tool Executor
(isolated execution + resource limits)

➣ Build your own Guardrails Middleware
(injection detection + PII redaction)

➣ Build your own Durable Workflow Engine
(checkpoint/resume, mini-Temporal)

➣ Build your own Streaming Proxy
(SSE + TTFT and ITL metrics)

➣ Build your own LLM Tracer
(OpenTelemetry-style spans for every hop)

➣ Build your own Eval Harness
(trajectory grading + CI regression gates)

➣ Build your own Prompt Registry
(versioning + A/B routing + rollback)

➣ Build your own Data Flywheel
(feedback → synthetic data → LoRA loop)

Pick 3. Build them from scratch. Document every decision.
Most people import libraries.

Builders understand what happens underneath.

Bookmark this. You'll need it.