Running Orq as a service, AI Infrastructure
Orq Agency Implementation, Building Managed AI Agent Services
Learn how to architect, deploy, and operate production AI agents for clients using Orq's multi-model routing, real-time observability, and LLM-as-judge evaluation. This course covers agent runtime setup, cost governance workflows, quality gates for client delivery, and continuous improvement loops that justify retainer pricing.
Open the decision record for OrqWhat does running Orq for clients commit you to?
Published figures for this service. Blank fields are not published.
- Monthly tool cost
- Vendor pricing was not published in the supplied data. Contact Orq directly for plan pricing. Budget for LLM provider API usage costs on top of any Orq platform fee.
- Time to first value
- Time to value is measured in days per the published data; total setup duration is not published as a specific figure beyond the medium complexity rating.
- Payback
- Not modeled, client pricing, labor rates, overhead, and expected client volume were not supplied. The agency must provide these inputs to calculate a return.
- Guided implementation
- 16 hours
Is Orq worth running as a client service?
The supplied data confirms Orq can reduce client AI agent delivery time from six weeks to two weeks, which is a credible efficiency gain for agencies already staffed with AI/ML engineers. Revenue potential and margin remain unmodeled because vendor plan pricing and client pricing inputs were not present in the data.
An agency-fit judgement for reselling this service. It is separate from the tool description on the decision record.
Before you start
What has to be in place before the first client engagement.
Tools and subscriptions
- Active API keys from at least one of the 28+ supported LLM providers (e.g., OpenAI, Anthropic, Google, Mistral)
- Orq account with access to the AI Gateway, observability, and evaluation modules
- Agent framework integration selected from LangGraph, OpenAI Agents, CrewAI, or Vercel AI
- OpenTelemetry-compatible logging setup for trace export if connecting to existing client infrastructure
- Orq SDK installed in your agency's development environment for agent configuration and Feedback API access
People and inputs
- At least one AI/ML engineer or technical AI consultant on staff, Orq is rated medium setup complexity and is not suitable for non-technical teams
- Prepared agent configurations including memory definitions, tool parameters, and execution settings for the first client use case
- Evaluation datasets and prompt libraries for the first client workflow to enable offline LLM-as-judge scoring from day one
Included with the course
7 working documents for delivering this service.
- AI Agent Delivery Checklist: Pre-Production Quality Gateschecklist
- Multi-Model Routing Strategy Worksheet for Cost Optimizationworksheet
- Client Onboarding SOP: Orq Workspace Setup and LLM Provider Integrationsop
- Trace Analysis Template: Debugging Agent Failures and Hallucinationstemplate
- LLM-as-Judge Evaluation Framework for Retainer Contractsguide
- Monthly Observability Report Template: Spans, Costs, and ROI Metricstemplate
- Agent Governance Playbook: Compliance Monitoring and Risk Controlsguide
Listed by name. These documents are not yet published as individual downloads.