Production AI Deployment Patterns
The gap between a demo and a production system is where every AI engagement lives. These patterns close it.
Caching and cost control
Every call to a model provider costs money and adds latency. A semantic cache — keyed on the embedding of the prompt, not the raw string — can reduce LLM calls by 30-60% for common query patterns. Combine with exact-match caching for identical prompts, TTL-based invalidation, and cost budgets per user or tenant. The cache architecture you choose determines whether your system is economically viable at scale.
Fallbacks and graceful degradation
Model APIs fail. Rate limits hit. Latency spikes. A production agent system needs a fallback chain: primary model → secondary model (different provider) → cached response → graceful error. Each fallback should be observable — you need to know which tier served every response. Without this, your users will tell you the system is broken before you know it yourself.
Observability beyond logs
Traditional logging doesn't work for non-deterministic systems. You need tracing that follows a single request through prompt construction, retrieval, LLM call, and response generation. You need to log the exact prompt, model version, parameters, and token counts for every call — because when something breaks, you need to replay it. Tools like LangSmith, Braintrust, and custom OpenTelemetry pipelines give you this. Standard APM tools don't.
Rate limiting and concurrency
LLM APIs have rate limits that vary by tier, model, and time of day. A burst of user requests can exhaust your quota in seconds. Implement token-bucket rate limiting at the application layer, with separate buckets per model and per user. Queue requests that exceed the limit. Monitor queue depth — it's your leading indicator of capacity problems.
Canary deploys and rollback
When you update a prompt, change a model version, or swap a tool — you're deploying a new system behavior. But unlike deterministic code, you can't write a unit test that guarantees the new behavior is correct. Canary deploys route a percentage of traffic to the new configuration while monitoring key metrics (latency, cost per request, user-reported errors). If metrics degrade, rollback isn't `git revert` — it's switching back to the previous model version or prompt template. Have both ready before you deploy.
Versioning prompts, models, and tools
A production agent system has three things that change independently: the prompts, the models, and the tools. Version all three. Store prompt templates in version control alongside your code. Pin model versions — don't use `latest`. When a tool's API changes, the agent's tool definitions need to change with it. A version matrix (prompt version × model version × tool version) is the minimum viable production artifact.
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