Copilot Studio: Custom API Connectors

Key insights

• Fake API (Dummy API)
  Build a temporary API to prototype features, test flows, or supply mock data without a real backend.
  Use a dummy API to iterate faster and validate agent behavior before connecting live services.
• Custom Connector
  Create lightweight wrappers for REST or SOAP endpoints inside Copilot Studio to expose actions and data shapes to agents.
  You can define operations manually or import an OpenAPI spec and then map responses into Copilot actions.
• Sample JSON
  Use Copilot to generate realistic sample JSON and paste stringified responses into the connector to model different scenarios.
  Importing sample responses speeds testing and helps the agent learn expected data formats.
• C# Code Integration
  Drop C# snippets into the connector to implement logic that returns different results per action or simulates backend behavior.
  Copilot can help expand or refine the code so the connector behaves like a real API endpoint.
• Testing and Generative Orchestration
  Test the connector inside Copilot Studio, then add it to an agent topic to run end-to-end scenarios and see how generative orchestration coordinates actions.
  Iterate by testing agents with sample responses and refining connector logic until behavior matches your needs.
• Enterprise Integration & Security
  Use the On-Premises Data Gateway to securely expose internal APIs and the Microsoft 365 Agents Toolkit or Power Platform tools to streamline development and deployment.
  This approach supports low-code options, richer business data in Copilot, and secure access to on-prem or proprietary systems.

Overview of the Video

In a practical walkthrough, Damien Bird demonstrates how to build a fake API inside Copilot Studio and connect it to an agent for live testing. He frames the exercise as a fast way to prototype features or provide temporary data without depending on a full backend. Consequently, viewers learn both the mechanics and the rationale behind creating a lightweight test endpoint.

Moreover, the video highlights the convenience of generating sample JSON and embedding it into a connector so that agents can call different actions and receive varied responses. Damien times the steps clearly, which makes it easy to follow along during the demonstration. As a result, the tutorial serves as a compact guide for proof-of-concept work and experimentation.

How the Connector Is Built

First, Damien shows how to create a new custom connector and define actions and a dummy host that will return different payloads depending on the called action. He uses C# to produce code that the connector will execute, and then walks through copying and pasting that code into the connector environment. In addition, the walkthrough includes generating and importing sample JSON so the connector can reply with predictable, testable data.

Next, the video demonstrates using Copilot to help generate and refine code, which speeds up the setup and reduces manual errors. Damien also strings JSON responses for easier testing, and he explains how to import those responses into the connector definition. Therefore, developers can mock endpoints quickly and focus on higher-level behavior rather than server configuration.

Testing and Generative Orchestration

Following creation, Damien tests the connector directly inside Copilot Studio, showing how the custom connector becomes an action available to agents. He then runs scenarios where an agent uses the connector while executing a workflow, which highlights the role of generative orchestration in coordinating calls and shaping outputs. As a result, the demonstration makes clear how agents can combine data from mock APIs and internal logic to produce richer responses.

Moreover, Damien runs the agent through multiple test cases to confirm that the connector responds correctly for different action inputs. He explains debugging steps and how to iterate on the connector when responses do not match expectations. Thus, the video offers both the hands-on mechanics and the testing mindset required for effective prototyping.

Tradeoffs and Practical Challenges

While building a fake API offers speed and flexibility, Damien notes tradeoffs that teams must consider. For instance, mock connectors simplify early development, but they can mask real-world issues like authentication, latency, and error handling that appear only when calling actual services. Consequently, teams should balance rapid prototyping against later integration testing with real endpoints.

Another challenge involves maintaining parity between mock data and production models; if sample JSON diverges from live API schemas, agents may behave incorrectly after deployment. Additionally, using mock connectors can make security testing less effective unless the team later validates gateway, token, and network controls. Therefore, Damien advises combining mock-driven development with staged validation against authentic services to reduce surprises.

Use Cases and Final Thoughts

Overall, the video frames mock connectors as a practical tool for proof-of-concept work, demo environments, and early-stage feature exploration. For organizations building AI-driven assistants, the approach allows Copilot agents to access tailored data flows without waiting for full API development, which accelerates experimentation and stakeholder feedback. In short, the method reduces friction during initial design while keeping a clear path to production integration.

Finally, Damien’s tutorial balances hands-on steps with advice about limitations, making it useful for engineers and citizen developers alike. He demonstrates a repeatable workflow: generate sample JSON, create a custom connector, add C# logic, test inside Copilot Studio, and then validate the agent with generative orchestration. Consequently, readers can follow the same process to prototype features quickly while planning for the additional testing and security work required before going live.

Keywords

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