Definition
Agent architecture is the set of design choices that turn an LLM prompt into a production system: routing and classification, tool selection, memory and state, retry and failure handling, observability, and the data layer that feeds every tool call.
In Depth
The label gets dismissed as hype, but the architectural decisions are real and concrete. Classifier up front decides whether a query is deterministic (lookup, SQL) or ambiguous (RAG, web search). Tool selection picks the minimum set that covers the task. Memory is separated into short-term (conversation) and long-term (user/entity). Retries handle rate limits and flaky APIs. Observability tracks tool calls, data quality, and silent failures. The data layer — in Scavio's case, structured Google, YouTube, Amazon, and Reddit endpoints — is what keeps the reasoning stable when every tool call has to return clean, fresh context.
Example Usage
After three production incidents, the team rewrote the agent architecture to add a classifier, idempotent retries, and Scavio as the single structured data source for every tool.
Platforms
Agent Architecture is relevant across the following platforms, all accessible through Scavio's unified API:
- youtube
- amazon
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