##API Autopilot - APIs change. Your integrations adapt.

Inspiration

The idea for API Autopilot started while we were exploring Y Combinator's Request for Startups, where we came across the opportunity around self-maintaining APIs. It got us thinking: as applications become increasingly dependent on external services, who takes responsibility when those integrations inevitably break?

We looked at the problem from a developer's perspective. An API provider changes an endpoint, removes a response field, or modifies its authentication flow, and an integration that worked yesterday can suddenly fail. Developers then have to inspect logs, trace dependencies, compare documentation, identify the affected code, implement a fix, and make sure nothing else breaks.

We saw an opportunity to go beyond simply detecting breaking changes or generating code with an AI assistant. What if an AI agent could investigate an integration failure, propose a targeted repair, test that repair, and show the developer the evidence before asking for approval?

That question became the foundation of API Autopilot.

What It Does

API Autopilot is an AI-powered integration maintenance platform designed to help developers move from API failures to verified repairs.

It brings the repair workflow together in one place:

• Investigate: Analyze an integration failure and identify its likely cause.
• Trace: Connect the failure to the relevant code and affected integration.
• Repair: Propose a focused code change instead of blindly rewriting files.
• Verify: Run regression tests and inspect the results before considering the repair successful.
• Deliver: Let the developer review the changes and approve the creation of a GitHub pull request.

The goal is not to generate code as quickly as possible. It is to make repairs traceable, testable, and reviewable, while keeping the developer in control.

How We Built It

We designed API Autopilot around a modular architecture. The frontend, built with React, TypeScript, and Vite, provides a developer workspace for projects, incidents, investigations, proposed changes, and verification results. A Python and FastAPI backend handles application workflows and connects the interface to the underlying services.

The architecture separates investigation, repair generation, verification, and delivery so that proposing a fix is never treated as proof that it is correct. GitHub integration provides the path from an approved repair to a pull request, while Firebase Authentication and Cloud Firestore form the foundation for user identity and persistent project data.

We also designed an MCP-based interaction layer to expose core capabilities through structured tools, creating a path toward conversational API maintenance rather than limiting the experience to a conventional dashboard.

Throughout development, we focused on making the workflow modular, observable, and extensible.

Challenges We Faced

One of our biggest challenges was distinguishing an AI-generated suggestion from a trustworthy repair. A patch can look convincing while introducing new bugs. That is why verification is central to the product: a proposed change must be evaluated through tests, and the result must be communicated honestly.

Another challenge was balancing agent autonomy with developer control. The system needs enough freedom to investigate and propose changes, but potentially consequential actions—especially repository modifications and pull-request creation—need transparency and explicit approval.

We also had to balance our long-term vision with the limited time available to build the project. Rather than attempting to support every API provider and failure type immediately, we focused on establishing a coherent repair workflow that can be expanded over time.

What We Learned

Building API Autopilot reinforced an important lesson about agentic AI: autonomy without verification is just automation with uncertainty.

Useful developer agents need clear tool boundaries, observable intermediate steps, meaningful test results, and honest failure handling. They must distinguish between a suspected cause and a confirmed cause, and between a proposed patch and a verified repair.

We also learned that a compelling startup opportunity is only the starting point. Turning that opportunity into a useful product requires breaking the problem down, identifying the hardest engineering challenges, and designing around the trust developers need before allowing an agent to change their code.

What's Next

Our next steps are to expand API-change detection, support more integration patterns, strengthen isolated regression testing, and improve the evidence behind each proposed repair. We also want to make the MCP interface more capable so developers can investigate and manage integration failures conversationally.

Our long-term vision is simple: developers should spend less time firefighting broken integrations and more time building products.

API Autopilot aims to make integration maintenance proactive, explainable, and verifiable—one repair at a time.

Built With

• ai-agents
• artificial-intelligence
• automation
• cloud-firestore
• fastapi
• firebase
• generative-ai
• github-api
• model-context-protocol-(mcp)
• pytest
• python
• react
• restapi
• typescript
• vite