Engineering · May 2026 · 9 min read
Neural Architecture Patterns
Patterns we reuse when wiring LLMs into real products — retrieval, routing, eval gates, and failure modes that show up after the demo.
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Demos hide the architecture
A single chat box can look magical for twenty minutes. Production systems fail for boring reasons: stale retrieval, unbounded tool calls, silent prompt drift, and no one watching latency at p95. The patterns below are how we keep neural components boring on purpose.
We use “neural architecture” to mean the shape of the system around the model — not a novel research paper. Same discipline as any distributed design: clear boundaries, explicit contracts, and observability that outlives the original author.
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Pattern 1 — Retrieve, then reason
Never ask a model to recite your product truth from memory. Pull structured facts first: docs chunks with citations, SQL rows, CRM fields, pricing tables. Pass only what the turn needs.
Chunking strategy matters less than freshness and ownership. Every retrieved item should carry a source id and a last-updated stamp. If retrieval confidence is low, the agent should ask a clarifying question or refuse — not improvise.
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Pattern 2 — Router before generalist
A lightweight router (rules + small classifier + cheap model) decides which specialist path runs: FAQ, billing, scheduling, code lookup. The expensive reasoning model only runs when the route requires it.
Routing also encodes cost and risk. Billing mutations go through stricter paths. Read-only knowledge answers can be faster and cheaper. This is capacity planning expressed as product design.
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Pattern 3 — Tool calls as typed transactions
Treat every tool like an API with a schema, idempotency key, and timeout. The model proposes arguments; your runtime validates them. On failure, return structured errors the model can recover from — not stack traces.
- Allowlist tools per agent role
- Cap parallel tool fan-out
- Log input/output hashes for replay
- Require human approval for irreversible side effects
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Pattern 4 — Eval gates on the critical path
Ship a golden set before you ship the agent. Include happy paths, adversarial prompts, and the ten tickets your support team hates most. Run them in CI when prompts, tools, or retrieval corpora change.
Online, sample live traffic into a review queue. Score faithfulness to sources, tone, and action correctness — not just “sounds good.” Architecture without eval is decoration.
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Pattern 5 — Graceful degradation
Models time out. Vendors rate-limit. Embeddings go stale. Design the fallback ladder: cached FAQ → deterministic form → human queue. Users should experience a narrower product, not a broken one.
At Klyrox we instrument each rung: how often fallbacks fire, how long recovery takes, and whether customers retry. Those numbers tell you when to buy more capacity — or when to simplify the agent’s mandate.