The Business Challenge
The business situation the organisation faced before the project began.
A technology company's AI workflows needed to call a large and growing number of internal and third-party APIs. Wiring each integration by hand was slow, inconsistent and hard to maintain, and AI agents had no reliable way to discover which endpoint to use for a given task.
Why AI Was the Right Solution
Why AI was the right approach — and what alternatives were considered.
The value of AI agents depends on the tools they can use. Hard-coding integrations limits agents to a fixed set of actions, while a discoverable orchestration layer lets them choose the right API for the task. This turns a large API estate into a practical toolset for AI.
How the Solution Was Delivered
Discovery, design, development, testing and rollout — the journey, not the tooling.
- 1Reviewed how AI workflows were connecting to APIs and where one-off integrations caused friction.
- 2Defined a consistent structure for describing endpoints.
- 3Built routing so requests reach the right API through one entry point.
- 4Added API discovery so workflows and agents can find the capability they need.
- 5Standardised integrations and expanded coverage to more than 500 APIs.
Key Technical & Architecture Decisions
Architecture, model selection, workflow and trade-offs.
A single orchestration layer instead of point-to-point integrations.
Structured endpoint definitions so APIs are described consistently.
Discovery built in, so tools can be found rather than hard-coded.
Centralised routing to keep integration logic in one place.
Challenges & How They Were Solved
Obstacles hit along the way and how they were overcome.
Inconsistent integrations: solved with a standard structure for every endpoint.
Scale: a consistent pattern made it practical to cover more than 500 APIs.
Tool selection for agents: discovery lets workflows find the right API instead of relying on fixed wiring.
User Adoption & Change Management
How users responded — training, change management and feedback loops.
Teams building AI workflows gained one place to find and use APIs, which reduced duplicated integration work and made it easier to build new workflows on existing capabilities.
Business Outcomes & Impact
The measurable outcomes from the underlying AI Deployment — with the story behind them.
500+ APIs made available to AI workflows through a single orchestration layer with discovery and routing.
The story behind the numbers
AI workflows now reach 500+ APIs through a single layer instead of bespoke connectors. Adding a new capability means registering it in the layer rather than writing a new integration, which speeds up delivery and reduces maintenance. Agents can discover available tools instead of being hard-coded to them, making workflows more flexible.
Lessons Learned
What surprised the team, what worked well, and what would be done differently.
- A consistent endpoint structure makes large API estates manageable.
- Discovery is what turns APIs into usable agent tools.
- Centralised routing reduces duplicated integration work.
Future Opportunities
Where this solution could go next.
Usage analytics to show which APIs workflows rely on most.
Automated health checks and fallbacks for failing endpoints.
Permission controls per workflow or agent.
Exposing the layer through standard agent tool protocols such as MCP.
Professional Reflections
Guidance for another builder tackling a similar problem.
Before scaling AI agents, invest in the layer that connects them to tools. Consistent definitions and discovery matter more than the number of integrations, and centralising routing keeps the system maintainable as it grows.