AgentPMT - The Agentic Economy
Building an Internal Agent Services Catalog

Building an Internal Agent Services Catalog

By Stephanie GoodmanNovember 30, 2025

Your agents can only use what they can find -- and right now, most enterprises have no reliable way for agents to find anything.

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Gartner predicted that by 2025, less than half of enterprise APIs would be managed. That prediction landed conservatively. The average enterprise now runs hundreds of internal services, dozens of third-party integrations, and a growing swarm of AI agents that need to find and use those services reliably. The traditional API catalog -- a spreadsheet, a Confluence page, a Swagger file someone last updated in Q3 -- was already failing human developers. It will fail agents faster, and with more expensive consequences.

This is the gap that an internal agent services catalog fills. Not a rebranded API portal. Not a tool marketplace. A purpose-built registry that answers a specific question for every agent, every time: what can I use, how do I use it, and what happens if I get it wrong?

The distinction matters because agents don't browse documentation the way engineers do. They don't read changelogs. They don't Slack a colleague to ask which endpoint is the "real" one. They consume structured metadata, match it against task requirements, and make a call. If the metadata is stale, incomplete, or ambiguous, the agent either fails or -- worse -- succeeds at the wrong thing. This is precisely the problem that platforms like AgentPMT were built to address -- giving agents a reliable, structured way to discover, authenticate against, and consume tools without the ambiguity that plagues traditional catalogs.

The Catalog Is Not an API Portal

The first instinct most platform teams have is to extend what already exists. You have a Backstage instance or an internal developer portal -- why not just expose it to agents?

The answer is that API catalogs and agent services catalogs solve different problems for different consumers. An API catalog is built for human developers who are evaluating, integrating, and maintaining code against an endpoint. It optimizes for documentation depth, code samples, versioning details, and authentication setup guides. The human reader brings context: they know what team owns the service, they can interpret caveats in a README, and they can make judgment calls about whether a beta endpoint is stable enough for their use case.

Agents bring none of that context. An agent selecting a tool at runtime needs structured, machine-readable metadata that answers narrow questions definitively. Not "here's our comprehensive guide to the Payments API" but rather: Does this tool accept a currency field? Is it idempotent? What's the maximum payload size? What's the cost per call? Is it approved for production use by my org's policy? Can I call it right now, or is it in a maintenance window?

Kong recognized this gap when they launched the MCP Registry in their Konnect platform earlier this month, explicitly linking MCP server entries to their underlying API dependencies, ownership, and inherited policies. Docker took a different angle with their MCP Catalog, packaging verified tools as container images with signed provenance. Both approaches share a core insight: the catalog agents need is not the catalog developers already have. It's a layer above it, or beside it, designed for a consumer that thinks in schemas and constraints rather than prose and tutorials.

The practical implication is that your agent services catalog will likely sit alongside your existing developer portal, not replace it. It pulls metadata from the same source-of-truth systems, but reshapes it for machine consumption. Think of it as a compiled view of your service landscape, stripped of everything an agent can't use and enriched with everything it can.

What Belongs in Every Listing

The metadata schema for each catalog entry is where most teams either over-engineer or under-invest. Over-engineering looks like a sixty-field form that nobody fills out. Under-investing looks like a name, a URL, and a prayer.

The sweet spot is a core schema that captures what agents and operators actually need to make decisions, plus a small set of optional fields that earn their place through use. Here's what that core looks like in practice:

Identity and ownership. A stable, unique identifier for the service (not just a human-readable name -- agents need something they can reference deterministically). The owning team, an escalation contact, and the source repository. This sounds obvious until an incident happens and nobody can figure out who owns the tool the agent just called forty times.

Capability description. A structured summary of what the tool does, expressed in terms an agent can match against a task. This is not a marketing blurb. It's closer to a function signature with a docstring: "Sends an email to a specified recipient with subject and body. Supports HTML. Does not support attachments over 10MB." The more precise this is, the better agents can select the right tool without trial-and-error calls that burn budget and time.

Contract and schema. The input/output schema, the authentication method required, rate limits, and whether the tool is idempotent. This is where Article 12's deterministic tool design principles live in the catalog: the contract details that let agents (and the policy layer from Article 19) make safe decisions before a call happens, not after.

Cost and billing. Per-call cost, or the cost model if it's variable. Agents operating under budget constraints -- which every production agent should be -- need this to plan multi-step workflows without blowing through caps mid-run. AgentPMT's per-tool pricing and budget controls demonstrate how this works in practice: every tool call carries a known cost, and agents are constrained by spending limits that prevent runaway consumption before it happens.

Lifecycle status. Is this tool in beta, generally available, deprecated, or sunset? When is the deprecation date? What's the migration path? An agent calling a deprecated tool isn't just making a bad technical choice -- it's accumulating tech debt autonomously, which is a genuinely novel form of organizational damage.

Policy tags. What risk tier does this tool fall into? Is it approved for use by agents without human approval, or does it require escalation? These tags are how the policy layer (the rules described in Article 19) binds to specific catalog entries. Without them, policy is abstract; with them, it's enforceable. AgentPMT implements this through vendor whitelisting -- organizations can pre-approve which tool providers their agents are permitted to use, ensuring that policy enforcement happens at the catalog level rather than after the fact.

A few fields that sound optional but earn their place quickly: SLA commitments (so agents can prefer faster tools under time pressure), data classification labels (so an agent doesn't accidentally route PII through a tool that isn't cleared for it), and dependency declarations (so the catalog can surface cascading impact when a foundational service degrades).

Structuring Discovery So Agents Actually Find Things

Having a complete catalog is necessary but insufficient. If agents can't discover the right tool for a given task efficiently, you've built a library with no card catalog -- or, more precisely, a library where the card catalog is also written in a language the reader doesn't speak.

Agent-oriented discovery needs to work along at least two axes: capability-based search and constraint-filtered selection.

Capability-based search means an agent should be able to express "I need to send a transactional email" and get back a ranked list of tools that match, without knowing the internal service names in advance. This is where semantic descriptions in the catalog pay off. If every listing has a well-written capability summary plus a set of capability tags (email, notification, transactional, HTML), the search layer can match intent to capability without requiring exact name lookups.

Constraint-filtered selection means narrowing results by runtime requirements: "Which of these email tools is idempotent, costs less than $0.002 per call, and is approved for production use?" This requires the structured metadata fields described above to be query-able, not just displayable. If your catalog stores cost data in a free-text "notes" field, no agent can filter on it.

This is essentially how we designed DynamicMCP at AgentPMT -- tools are fetched and searchable when the agent needs them, with on-demand loading that keeps context lean and costs low. The agent doesn't load every tool definition at startup. It describes what it needs, the system returns matching tools, and the agent selects from a constrained set. That pattern works inside an enterprise catalog the same way it works in a marketplace: discovery should be demand-driven, not dump-everything-into-context.

The anti-pattern to watch for is what you might call "registry stuffing" -- teams dumping every internal endpoint into the catalog without curation, so agents face a selection problem instead of a discovery problem. A catalog with 400 entries and no quality bar is worse than one with 40 entries that are all well-documented and maintained. Curation is a feature, not a limitation.

Lifecycle Management: Onboarding, Deprecation, and Sunsetting

Static catalogs rot. This is true for human-facing API portals, and it's catastrophically true for agent-facing registries where stale entries don't produce confused developers -- they produce failed runs, wasted budget, and silent data quality degradation.

The lifecycle of a catalog entry should mirror the lifecycle of the underlying service, tracked through explicit status transitions: proposed, beta, generally available, deprecated, and sunset.

Onboarding is where most catalog efforts stall. If registering a new tool requires filling out a thirty-field form and getting three approvals, teams will skip it. The intake process needs to balance completeness against friction. A reasonable approach: require the core fields (identity, ownership, capability, schema, cost, lifecycle status) at registration, validate the schema automatically, and let optional fields be added iteratively. Automated linting can catch common issues -- an endpoint declared as idempotent that doesn't accept an idempotency key, a cost field set to zero that's probably just unfilled.

Deprecation is where agents introduce a problem that human consumers don't. A human developer who sees a deprecation notice can plan a migration. An agent that calls a deprecated tool at 3 AM on a Saturday has no such awareness unless the catalog's policy tags trigger a block or a warning. The HTTP Deprecation and Sunset headers (defined in RFC 8594 and RFC 9512) provide a standardized way to signal this at the protocol level, but the catalog also needs to enforce it at the discovery level: deprecated tools should be ranked lower or filtered out by default, with an override available only if the policy permits it.

The deprecation window matters more for agents than for humans. A human team might need 90 days to migrate. An agent workflow can be redirected in minutes -- if the catalog maps deprecated tools to their replacements. That mapping (Tool A is deprecated, use Tool B instead, here's the input translation) is one of the highest-value metadata fields a catalog can carry, and one of the rarest in practice.

Sunsetting -- the actual removal of a tool from the catalog -- should trigger automated checks across all registered agent workflows that reference it. If you can't answer "which agents will break when we remove this tool?" then you don't have a catalog. You have a list. AgentPMT's audit trails provide exactly this visibility -- every tool call is logged with the agent, workflow, and context that initiated it, so when a tool is being sunset, operators can immediately identify which workflows depend on it and plan migration accordingly.

Who Owns the Catalog

The organizational question is harder than the technical one. Catalog ownership tends to fall into one of three patterns, each with predictable failure modes.

Pattern one: central platform team owns everything. This produces the most consistent metadata quality but creates a bottleneck. Every new tool registration, every update, every deprecation flows through one team. At scale, that team becomes a ticket queue, and teams start routing around them -- deploying tools that never make it into the catalog, which is exactly the shadow-API problem that catalogs exist to solve. Postman's research found that 31% of developers at large enterprises cited managing too many APIs as a primary obstacle -- centralized ownership can exacerbate this when the central team becomes a throughput constraint.

Pattern two: every team manages their own entries. This scales naturally but produces wildly inconsistent quality. One team's listing has complete schemas and cost data. Another team's listing has a name and a URL last updated eight months ago. Agents don't care about organizational excuses -- they'll fail equally on both, but the well-documented one will produce informative errors while the other produces mystery.

Pattern three: federated ownership with central standards. Tool teams own their entries. A platform team owns the schema, the validation rules, and the lifecycle automation. Registration is self-service but linted. Updates are team-driven but audited. Deprecation is proposed by the owning team but enforced by the platform. This is the Backstage model -- Spotify built it precisely because they had thousands of internal software components and needed a way to let teams self-register while maintaining discoverability and consistency.

The federated model works best for agent services catalogs because it aligns incentives correctly. The team that builds a tool has the deepest knowledge of its capabilities and constraints. The platform team has the deepest knowledge of what agents need to consume that information reliably. Neither can do the other's job well, but together they produce entries that are both accurate and machine-readable.

The one role that should not be optional in any model is a catalog curator -- someone (or some automated process) that regularly audits entries for completeness, flags stale listings, and identifies tools that are used in production but absent from the catalog. That last category -- the shadow tools, the ones agents discovered through hardcoded references or legacy configurations rather than catalog lookup -- is where the real risk lives.

Implications for Enterprise AI Strategy

The decision to build an internal agent services catalog is not purely a technical one -- it carries strategic implications that ripple across how an organization governs, scales, and secures its AI operations.

Governance becomes enforceable, not aspirational. Without a catalog, AI governance policies exist as documents that agents never read. With a structured catalog that carries policy tags, cost constraints, and lifecycle status, governance is embedded in every tool selection decision an agent makes. The gap between "we have a policy" and "the policy is enforced" closes.

Security posture improves structurally. A catalog with credential isolation -- where each tool's authentication is managed independently and agents never hold raw secrets -- reduces the blast radius of any single compromise. AgentPMT's approach to credential isolation exemplifies this: agents authenticate through the platform rather than holding API keys directly, so a compromised agent workflow cannot leak credentials for tools it wasn't actively using.

Scaling agent fleets becomes predictable. When every tool has known costs, known rate limits, and known SLA commitments registered in the catalog, capacity planning for agent operations becomes a data problem rather than a guessing game. Organizations can model the cost of scaling a workflow from 100 to 10,000 daily runs because the per-tool economics are explicit.

Vendor lock-in risk is surfaced early. A well-maintained catalog makes dependency concentration visible. If 80% of your agent workflows route through a single vendor's tools, that's a strategic risk that the catalog can quantify -- and that the DynamicMCP marketplace model can help mitigate by providing access to alternative tools with comparable capabilities.

What to Watch

Three developments will shape how internal agent services catalogs evolve over the next twelve to eighteen months.

First, the MCP registry ecosystem is consolidating fast. The official MCP Registry is progressing toward a stable v0.1 API, Kong and Docker have shipped enterprise-grade catalog products, and the tooling for linking registry entries to underlying API governance is maturing. Enterprises that treat their internal catalog as an isolated project rather than a node in a broader registry network will find themselves rebuilding sooner than expected.

Second, discovery is getting smarter. The gap between keyword-based tool search and genuine capability matching is closing as embedding-based search and structured constraint filtering become standard patterns. Catalogs that only support name-based lookup will feel primitive within a year.

Third, lifecycle automation is moving from nice-to-have to mandatory. As agent fleets grow, the blast radius of a stale or misconfigured catalog entry grows with them. The teams that automate deprecation enforcement, replacement mapping, and usage-based auditing now will avoid the incident that teaches everyone else why it matters.

The enterprises building agent services catalogs today aren't solving a futuristic problem. They're solving the same problem every growing software organization eventually faces -- "what do we have, and how do I use it safely?" -- except the consumer asking that question can now make a thousand decisions per hour, unsupervised, with a budget attached.

Key Takeaways

  • An agent services catalog is not an API portal with a new label. It's a machine-readable registry built for consumers that think in schemas and constraints, not documentation and tutorials.
  • Every catalog entry needs a core metadata set -- identity, ownership, capability, contract, cost, lifecycle status, and policy tags -- or agents will make decisions with incomplete information, which is a polite way of saying they'll make bad decisions.
  • Federated ownership with central standards is the model that scales: tool teams own accuracy, platform teams own structure, and automated linting catches the gap between them.

Ready to build your agent services catalog? AgentPMT provides the infrastructure -- DynamicMCP tool discovery, vendor whitelisting, per-tool pricing, credential isolation, and audit trails -- so your agents can find, use, and pay for tools reliably from day one.

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