Why enterprise alignment becomes critical when AI agents join the team—and how catalogs become the substrate that makes it possible.
In recent articles, we've explored the hidden costs of building redundant APIs and the acceleration capability of an internal API ecosystem when reuse becomes easier than rebuilding.
But what happens when AI agents join the team?
When developers work alongside AI assistants, when AI agents orchestrate workflows, when AI systems make autonomous decisions about which APIs to call—what does that ecosystem look like?
The answer: It looks like chaos, unless there's a substrate that provides coherence.
AI doesn't just accelerate development. It accelerates everything—including problems.
Every large organization is struggling with:
- Shadow APIs — services built without visibility or governance
- Fragmented governance — different standards, different processes, different tools
- Unclear ownership — who owns what, who's responsible, who to contact
- Poor versioning discipline — breaking changes, deprecated APIs still in use, version confusion
- Developer fatigue — too many tools, too many processes, too much cognitive overhead
- Slow integration loops — weeks to discover, days to integrate, hours to debug
AI accelerates all of these problems.
Without a unified substrate:
- AI agents make decisions based on incomplete information
- AI assistants suggest APIs that don't exist or are deprecated
- AI workflows break because dependencies aren't clear
- AI systems create shadow APIs faster than humans can track them
- AI agents can't distinguish between production and experimental APIs
The result: AI amplifies chaos instead of creating order.
The real opportunity is enterprise-wide alignment.
An API catalog isn't just a directory of APIs. It's the substrate that provides coherence across the entire organization.
When AI models plug into a unified fabric, they deliver transformational productivity.
Governance in one place:
- Standards, rules, and policies are centralized
- AI agents can check compliance before suggesting APIs
- Breaking changes are detected automatically
- Quality gates are enforced consistently
Documentation in one place:
- APIs are documented with current, accurate information
- AI agents can read schemas, examples, and guides
- Versioning is clear and discoverable
- Integration patterns are documented and reusable
Catalog in one place:
- All APIs are discoverable and searchable
- AI agents can find the right API for the right use case
- Dependencies are mapped and visible
- Ownership and lifecycle are clear
Workflows in one place:
- API design, review, and release workflows are standardized
- AI agents can participate in governance processes
- Change management is automated and consistent
- Integration patterns are reusable across teams
This isn't a point solution. This is a platform.
When an API catalog provides a unified substrate, AI and humans can collaborate effectively.
AI agents can:
- Discover APIs based on natural language queries
- Suggest the right API for a use case
- Check compliance before integration
- Detect breaking changes automatically
- Generate integration code from schemas
- Create Arazzo workflows that orchestrate multiple APIs into business processes
- Orchestrate workflows across multiple APIs
Humans can:
- Trust AI suggestions because they're based on accurate catalog data
- Focus on business logic instead of API discovery
- Rely on AI to handle routine integration tasks
- Use AI to navigate complex dependency graphs
- Leverage AI to maintain documentation and schemas
Together, they:
- Move faster than either could alone
- Maintain quality and compliance automatically
- Reduce errors and breaking changes
- Create reusable patterns and workflows
- Build on a foundation of trust and accuracy
But this only works if the catalog is comprehensive, accurate, and trusted.
Here's where it gets even more interesting.
AI agents don't just consume the catalog—they can also extend it.
AI agents can:
- Generate new API descriptions based on requirements
- Create API workflows described with Arazzo (orchestrating multiple APIs into business processes)
- Create service definitions from code analysis
- Build catalog entities from infrastructure discovery
- Extend existing APIs with new endpoints
- Propose new workflows and integrations
But here's the critical part: Scorecards check their governance.
When AI agents create or extend catalog entities, scorecards automatically evaluate:
- Compliance — Do the APIs meet organizational standards?
- Quality — Are the schemas well-designed and documented?
- Security — Are security best practices followed?
- Consistency — Do the APIs follow naming conventions and patterns?
- Completeness — Is all required metadata present?
- Workflow correctness — Do Arazzo workflows correctly orchestrate APIs and handle errors?
This creates a feedback loop:
- AI builds — AI agents create or extend catalog entities (APIs, services, workflows)
- Scorecards evaluate — Governance scorecards automatically check what AI created
- Catalog improves — Issues are flagged, quality is measured, compliance is verified
- AI learns — AI agents learn from scorecard feedback and improve future outputs
The result: AI agents become better contributors to the catalog over time, and the catalog maintains quality even as AI accelerates creation.
Without scorecards:
- AI agents create APIs that don't meet standards
- Quality degrades as AI generates more content
- Compliance issues multiply
- Technical debt accumulates faster
With scorecards:
- AI agents create APIs that meet governance standards
- Quality is maintained automatically
- Compliance is checked before deployment
- Technical debt is prevented, not just tracked
The scorecard becomes the quality gate that ensures AI acceleration doesn't become AI chaos.
Here's where it gets interesting.
An API catalog that only tracks APIs isn't enough.
To provide true enterprise-wide alignment, catalogs need to extend beyond APIs to include:
Services:
- Microservices, functions, and serverless endpoints
- Data pipelines and ETL processes
- Message queues and event streams
- Infrastructure components and configurations
People:
- API owners and maintainers
- Domain experts and subject matter specialists
- Teams and organizational structures
- Contact information and escalation paths
AI Agents:
- AI assistants and copilots
- Autonomous AI systems and workflows
- AI models and their capabilities
- AI decision-making patterns and policies
When catalogs include all of these entities, they become the true substrate of the enterprise.
AI agents can discover not just APIs, but the services that expose them, the people who own them, and the other AI agents that use them.
Humans can understand not just what APIs exist, but who built them, who uses them, and how AI agents interact with them.
The entire organization—people, services, APIs, and AI agents—becomes discoverable, governable, and aligned.
This isn't theoretical. It's happening now.
Organizations are already deploying AI agents that:
- Generate API integration code
- Create new API descriptions and catalog entities
- Generate Arazzo workflows that orchestrate multiple APIs into business processes
- Suggest API designs based on requirements
- Automate API testing and validation
- Orchestrate workflows across multiple APIs
- Monitor API health and performance
But these agents are only as good as the substrate they're built on.
Without a unified catalog:
- AI agents make mistakes because information is incomplete
- AI workflows break because dependencies aren't clear
- AI suggestions are wrong because APIs are deprecated
- AI systems create technical debt faster than humans can track
With a unified catalog:
- AI agents have complete, accurate information
- AI workflows are reliable because dependencies are mapped
- AI suggestions are current because lifecycle is tracked
- AI systems create value instead of debt
The difference is the substrate.
Building an API catalog that serves as the AI substrate requires:
Automation:
- APIs, services, and dependencies are discovered automatically
- Documentation and schemas are kept current automatically
- Ownership and lifecycle are tracked automatically
- Changes are detected and cataloged automatically
Comprehensiveness:
- All APIs are cataloged, not just the official ones
- Services, people, and AI agents are included
- Dependencies and relationships are mapped
- Historical data and change history are preserved
Trust:
- Information is accurate and current
- Ownership and responsibility are clear
- Lifecycle stages are reliable
- Governance policies are enforced
- Scorecards evaluate quality and compliance automatically
Extensibility:
- New entity types can be added (services, people, AI agents)
- New relationships can be discovered and mapped
- New governance rules can be applied
- New workflows can be automated
When these elements come together, the catalog becomes more than a directory. It becomes the substrate that enables AI-human collaboration at scale.
Organizations that build this substrate first will have a significant competitive advantage.
They'll move faster:
- AI agents can discover and integrate APIs autonomously
- Developers can focus on business logic instead of integration
- Workflows can be automated end-to-end
- Time-to-value accelerates dramatically
They'll maintain quality:
- Governance is automated and consistent
- Breaking changes are detected before deployment
- Compliance is checked automatically
- Technical debt is tracked and managed
They'll scale better:
- New teams can onboard faster with AI assistance
- New APIs can be discovered and integrated automatically
- Dependencies can be managed at scale
- Complexity can be navigated systematically
They'll innovate more:
- AI agents can suggest new API patterns
- Developers can experiment with confidence
- Reuse becomes easier than rebuilding
- Velocity compounds across the organization
The substrate enables all of this.
API catalogs aren't just organization tools. They're the substrate that makes AI-human collaboration possible at scale.
As AI agents join development teams, as AI systems orchestrate workflows, as AI assistants help developers build faster—the need for enterprise-wide alignment becomes critical.
A unified catalog that includes APIs, services, people, and AI agents provides that alignment.
It's not a point solution. It's a platform.
And organizations that build this platform first will have a significant competitive advantage.
Because when AI accelerates everything—including problems—you need a substrate that provides coherence.
The catalog is that substrate.
The future is already here. The question is: Are you building the substrate that makes it possible?