ScavioScavio
ProductPricingDocs
Sign InGet Started
  1. Home
  2. Glossary
  3. Agent Architecture
Glossary

Agent Architecture

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.

Try Scavio FreeAPI Docs

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

Real-World Example

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:

  • google
  • reddit
  • youtube
  • amazon

Related Terms

Orchestrator-Subagent Architecture

Orchestrator-subagent architecture is the 2026-era agent design pattern where a single orchestrator model plans the task...

Grounding LLM Workflows

Grounding LLM workflows is the pattern of injecting verified, fresh, structured context — from search APIs, internal doc...

Multi-Agent Web Intelligence

Multi-agent web intelligence is the 2026 pattern of running multiple specialized agents in parallel across different web...

Frequently Asked Questions

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.

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.

Agent Architecture is relevant to google, reddit, youtube, amazon. Scavio provides a unified API to access data from all of these platforms.

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.

Agent Architecture

Start using Scavio to work with agent architecture across Google, Amazon, YouTube, Walmart, and Reddit.

Try Scavio FreeRead the Docs
ScavioScavio

Real-time search API for AI agents. Search every platform, not just Google.

Product

  • Features
  • Pricing
  • Dashboard
  • Affiliates

Developers

  • Documentation
  • API Reference
  • Quickstart
  • MCP Integration
  • Python SDK

Alternatives

  • Tavily Alternative
  • SerpAPI Alternative
  • Firecrawl Alternative
  • Exa Alternative

Tools

  • JSON Formatter
  • cURL to Code
  • Token Counter
  • All Tools

© 2026 Scavio. All rights reserved.

Featured on TAAFT
Terms of ServicePrivacy Policy