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Stop debugging monolithic agents by guessing. Build a FastAPI service where every request flows through transport, orchestrator, tools, memory, retrieval, guardrails, and observability, with a per-layer trace you can replay.
Message a mentor about fit, prerequisites, or where to start. Replies come on WhatsApp, usually within a day.
Engineers are learning here from
Build an LLM agent as seven composable layers with clear contracts, per-layer tracing, and graceful failure modes. Transport, orchestrator, tools, memory, retrieval, guardrails, observability.
Architect an agent as seven composable layers with per-request traces.
What you'll ship
What you'll learn
Curriculum
Transport layer
Validate every request at the door with Pydantic contracts, size caps, and versioned routes
Orchestrator layer
Build a state machine that picks tool use, retrieval, or reply, with explicit cycle-break conditions
Tools layer
Register tools behind a dispatch table with schema validation and a shared error envelope
Thread memory layer
Store per-thread conversation history with hard bounds and a summarization hook
Retrieval layer
Ground replies in ChromaDB with citations and a re-rank hook for later upgrades
I/O guardrails layer
Scrub PII on both sides of the model and apply a refusal policy for unsafe outputs
Observability layer
Emit structured per-layer traces, capture timings, and expose a replay endpoint for debugging
Who it's for
who keep finding new failure modes at 2am because their agent is one big function with no layer boundaries
who already know how to build clean services but have not mapped those patterns onto agent pipelines
who need a shared vocabulary for transport, orchestration, tools, memory, retrieval, guardrails, and observability
FAQ
No. The orchestrator is a plain Python state machine that mirrors the graph-of-nodes pattern. You will understand LangGraph better after, but the course does not depend on it.
Yes. ChromaDB runs embedded with local Sentence Transformers. The retrieval layer also degrades gracefully if embeddings are unavailable, so the rest of the pipeline keeps serving traffic.
No. The service uses an LLM provider pattern that works with OpenRouter, Fireworks, Gemini, or OpenAI. Any provider with async text generation works.
Design patterns teach individual building blocks. This course wires them into a single running service where every request flows through all seven layers and emits a trace. It is the production assembly of those patterns.
Pricing
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Layered production AI architecture
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