The Agent Service Catalog: How to Launch an Internal AI Marketplace in 60 Days

As AI agents proliferate in the enterprise, many organizations face a new problem: agent sprawl. While a majority of executives report placing AI agents in production, they often struggle with inconsistent governance, scattered development, and limited measurement of business impact. This guide outlines a step-by-step approach to building an internal agent service catalog—a centralized, governed AI marketplace that enables controlled, scalable automation with measurable value.

Why Build an Agent Service Catalog?

Agent service catalogs provide standardized provisioning, clear risk-based governance, operational efficiency, and a structure for tracking ROI. With legislative pressures mounting (like the EU AI Act), organizations must inventory and document AI systems, especially high-risk use cases. Service catalogs resolve these challenges, promoting reuse and strategic scaling while ensuring compliance.

60-Day Launch Framework

Phase 1: Lay the Foundation (Days 1-20)

• Align stakeholders (IT, Legal, Business) and define success metrics
• Create governance committee and set risk tiering (minimal, limited, high risk)
• Audit current AI deployments and catalog existing agents
• Select catalog platform (ServiceNow, Azure AI Foundry, or custom)

Phase 2: Catalog Construction (Days 21-40)

• Design service taxonomy (Customer Support, Data Processing, Workflow Automation, etc.)
• Standardize intake/request forms for agents
• Build workflow for approvals and compliance based on risk
• Define SLA and KPI frameworks (containment rate, time saved, impact, adoption)
• Configure cost tracking (resource tagging, showback/chargeback models)

Phase 3: Pilot & Rollout (Days 41-60)

• Launch a pilot with 3–5 representative agents
• Test governance, approvals, and feedback processes
• Refine catalog, request templates, and approval chains based on pilot data
• Begin organizational rollout, comms, and training
• Establish regular review cycles for compliance and optimization

Governance Framework

Align agents to risk categories, with corresponding approval and documentation:

• Low risk: Internal, automated approval
• Medium risk: Customer-facing or regulatory-sensitive, department approval
• High risk: Critical business, executive approval and full documentation

Templates and Tools

• Standardized agent intake request form (problem, use case, data sources, risk, compliance)
• KPI tracking spreadsheets (performance, accuracy, business impact)
• Risk checklist (data sensitivity, compliance, security, continuity)

Success Metrics

• Time-to-deployment of new agents (target <30 days)
• Adoption rate (>75% eligible processes)
• Operational cost reduction (15%+ savings)
• Compliance audit pass rate (100% doc completeness)
• User and stakeholder satisfaction ratings

Best Practices

• Start with low-risk, high-impact agents to win support
• Formalize cross-functional governance early
• Use familiar ITSM frameworks where possible (e.g., ServiceNow)
• Invest in communication and change management for adoption
• Update KPIs and risk criteria regularly to align with evolving regulations

Looking Ahead

An agent service catalog not only brings order to current deployments but also future-proofs the enterprise for incoming regulations and advanced AI use-cases (like multi-agent orchestration). Organizations that structure agent growth today will outpace those wrangling agent sprawl tomorrow.

To get started, download an intake template, schedule your stakeholder meeting, and conduct your current AI inventory. Structured, governed AI marketplaces are key to scalable, compliant AI value.