Running Cadenya as a service, AI Agents

Cadenya Agent Runtime, Building Autonomous Workflows for Client Delivery

Learn how to architect and deploy autonomous agents for your clients by connecting their existing APIs through Cadenya's unified tool layer, then iterate on model performance and behavior using real-time token metering and outcome feedback. This course teaches agencies how to build productized agent services, measure which LLM models deliver the best results for specific workflows, and scale delivery without rebuilding infrastructure for each client.

Open the decision record for Cadenya

What does running Cadenya for clients commit you to?

Published figures for this service. Blank fields are not published.

Monthly tool cost
Vendor cost basis: Cadenya publishes a usage-based pricing model and no numeric tier prices in the supplied data, so the per-month floor is Not published. Agencies must budget for their own LLM provider credentials, which the vendor states are required after the initial OpenRouter credits.
Time to first value
Not published
Payback
Not modeled
Guided implementation
8 hours

Is Cadenya worth running as a client service?

The evidence supports Cadenya as a developer-focused agent runtime that layers agents and objectives on existing APIs, with model variation testing, token metering, webhooks, approval gating, and embeddable widgets. What remains unknown are the vendor's numeric pricing, your client prices, labor, overhead, and expected volume, so no ROI timeline can be modeled from the supplied 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

  • LLM provider credentials you supply yourself after the initial OpenRouter credits (OpenRouter, OpenAI, Anthropic Claude are the surfaced integrations)
  • At least one existing API or endpoint to expose as an agent tool
  • Optional MCP servers and OpenAPI specs to import through the single tool layer
  • A webhook endpoint URL to receive every event in the agentic loop

People and inputs

  • Engineering capacity to wire APIs, agents, tools, and sub-agents, Cadenya is not a no-code agent builder
  • Ability to run and compare model variations across agent configurations
  • A process for reviewing token metering and progressive tool discovery to keep usage cost visible
  • A human approval workflow for gating sensitive tool calls before execution

Included with the course

7 working documents for delivering this service.

  • Agent Architecture Worksheet - API Mapping and Tool Designworksheet
  • Model Comparison Testing Checklist - Claude vs GPT Variationschecklist
  • Client Onboarding SOP - MCP Server and OpenAPI Integrationsop
  • Token Cost Tracking Template - Per-Agent Daily Spend Reporttemplate
  • Agent Feedback Loop Guide - Collecting and Acting on Outcome Scoresguide
  • Webhook Event Streaming Setup - Real-Time Monitoring and Alertssop
  • Productized Agent Service Pricing Calculator - Launch and Pro Tier Marginstemplate

Listed by name. These documents are not yet published as individual downloads.