Definition
A custom agent framework built around a specific domain (legal research, people search, market analysis, competitive intelligence) that constrains the agent's tool set, prompt structure, and data sources to maximize accuracy and reliability within that domain.
In Depth
General-purpose agents (ChatGPT, Claude with web search) can perform many tasks but excel at none. A domain-specific agent harness constrains the agent to a particular problem space, providing specialized tools, domain-aware prompts, and curated data sources. The result is an agent that performs its narrow task with higher accuracy, consistency, and reliability than a general agent. Harness components: (1) Domain prompt -- system instructions that establish domain expertise, define output formats, and set quality constraints. A legal research agent's prompt specifies citation formats, jurisdiction awareness, and recency requirements. (2) Curated tool set -- only the tools relevant to the domain. A competitive intelligence agent might have Google search, Reddit search, and Amazon product search, but not TikTok or YouTube. Fewer tools means fewer errors in tool selection. (3) Validation layer -- domain-specific checks on agent output. A people search agent validates that extracted email formats are valid, phone numbers match country patterns, and company names resolve to real entities. (4) Data source configuration -- which platforms to query and how. A market analysis agent queries Google for market reports, Reddit for customer sentiment, and Amazon for product landscape. Scavio integration: the search API serves as the data layer for domain-specific agents. Configure which platforms the agent can access, set per-query budgets, and define result filtering rules per domain. A legal research agent might only search Google with specific query templates, while a product intelligence agent searches Amazon, Walmart, and TikTok.
Example Usage
The legal research harness constrains the agent to Google search (Scavio, $0.005/query) with query templates like '{topic} site:law.cornell.edu OR site:scholar.google.com'. The agent's prompt enforces Bluebook citation format. Validation checks that cited cases exist and dates are correct. Accuracy improved from 70% (general agent) to 94% (domain harness).
Platforms
Domain-Specific Agent Harness is relevant across the following platforms, all accessible through Scavio's unified API:
- Amazon
- TikTok
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