Running OJCP as a service, AI Agents

OJCP Agency Implementation, Building Multi-Board Job Agent Services

Learn how to build and monetize AI job agent services using OJCP's manifest-based discovery and normalized application paths. This course teaches agencies how to set up agent platforms that automatically integrate with multiple job boards and ATS systems, deliver fit-scoring services to clients, and scale job placement automation without custom vendor integrations.

Open the decision record for OJCP

What does running OJCP for clients commit you to?

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

Monthly tool cost
Not published, OJCP pricing_model is open-source with no paid plan tiers listed. Budget implementation hours plus any client-supplied hosting, JWS verification, and ATS access costs.
Time to first value
Not published, setup_complexity is high and time_to_value is weeks, but no explicit implementation duration is published
Payback
Not modeled, client price, labor cost, usage cost, overhead, and expected application volume are not supplied
Guided implementation
8 hours

Is OJCP worth running as a client service?

OJCP is an open standard, not a turnkey product, so the evidence supports building a managed implementation and interoperability service for job boards, ATS vendors, staffing agencies, and AI agent platforms that have technical staff. What remains unknown is vendor cost basis, agency pricing, and ROI, because no paid tiers, market rates, or volume data are published.

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

  • MCP-compatible client environment for calling search_jobs, get_job_detail, and begin_application
  • Hosting capable of serving /.well-known/ojcp.json manifests
  • Target job board or ATS access (Workday, Greenhouse, Lever, or Indeed) for apply-path integration
  • JWS signing capability for VerificationProof and verifier manifests
  • schema.org-compatible job data structures for listing normalization
  • Browser-native agent access via navigator.modelContext where WebMCP is required

People and inputs

  • Engineering time for high-complexity onboarding (setup_complexity: high)
  • An MCP-native tooling workflow covering search_jobs, get_job_detail, and begin_application
  • A consent-first data policy for CandidateContext scoping and resume-embedding fit-scoring without PII transmission
  • Agent-aware ranking configuration for personalized search results

Included with the course

7 working documents for delivering this service.

  • OJCP Manifest Audit Checklist for Job Board Integrationschecklist
  • CandidateContext Configuration Worksheet for Fit Scoringworksheet
  • Multi-Vendor ATS Integration SOP (Workday, Greenhouse, Lever, Indeed)sop
  • Agent Declaration and JWS Proof Template for Client Onboardingtemplate
  • Job Application Pipeline Tracking Dashboard Setup Guideguide
  • Consent-First Resume Embedding Privacy Compliance Checklistchecklist
  • Retainer Pricing Model for OJCP-Based Job Agent Servicestemplate

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