We’re thrilled to introduce you to Bito’s AI Architect. It’s a new type of agent that is available today via MCP in tools like Claude, Cursor, and Codex.
Growing, complex codebase needs engineers that understand how the whole system works. That is why teams rely on architects. They deeply understand your codebase. They know where the risks sit, and understand how all the pieces connect. They hold that mental map that everyone depends on.
Bito’s AI Architect builds that knowledge graph, then makes it accessible to developers, like you. This knowledge graph becomes codebase intelligence that engineers and AI agents rely on to reason about real system behavior, dependencies, and impact across the entire codebase.
It learns your codebase repo by repo, whether you have 50 repos or 5,000. It understands your APIs, modules, technology, and how everything fits and works together.
AI Architect makes that system view available through MCP so your tools and coding agents work with accurate context instead of guesswork.
What you can do with AI Architect
Today marks just the beginning This is the first step for the AI Architect and its capabilities will expand quickly.
The AI Architect supports the following workflows:
- 1-shot production-ready code: Your coding agents receive API contracts, schemas, patterns, examples, and call flows from the knowledge graph. This lets Cursor and Claude write grounded, system aligned, production ready code. Watch the demo: Production-ready code generation with AI Architect.
- Spec-driven development: Automatically generate highly detailed, implementation-ready technical requirement documents (TRDs) and low-level designs (LLDs) with a deep, context-aware understanding of your codebase. Learn more about Spec-driven development with AI Architect.
- Triaging production issues: Easily and quickly find root causes to production issues based on errors/logs/etc. See how you can troubleshoot production issues with AI Architect.
- API and library discovery: Ask for an endpoint and get its contract, schema, examples, and every place it is used across the system.
- Workflow understanding: Ask how a feature works and get the call chain across services and modules.
- System diagrams and structure: Generate updated dependency diagrams and readmes that reflect your current state, not outdated docs.
- Onboarding with actual system context: A new engineer can easily ask and get answers about various parts of your codebase, without having to read through code or ask other engineers. Bito’s on-prem version has full code access to be able to show you specific lines of code as well.
- Smarter agents in your IDE: Cursor and Claude pull context through MCP and answer with details from your own code, not a generic guess.
Watch the demo to see how it works:
How AI Architect works
Bito’s AI Architect builds a knowledge graph of your codebase by indexing your repositories and exposing that information through MCP. Here’s how:
1. Code indexing
The AI Architect reads every repository and pulls out the structure and behavior of your code. It understands file layouts, symbols, classes, functions, signatures, coding patterns, execution flows, cross references, and database schemas. This gives it a clear view of how each module is written and how its components work.
2. Capturing edges across repositories
The AI Architect learns how your services interact across repos. It records every API call, where it happens, the full request and response schemas, streaming behavior, example payloads, and the internal flow of each endpoint. This reveals how information moves across your system.
3. Building a system knowledge graph
The AI Architect links all repo-level data and cross-repo edges into a connected graph that reflects your architecture. It captures service boundaries, call paths, interface contracts, and upstream or downstream impact. This becomes the system map that engineers and coding agents use to reason about changes.
4. Serving through MCP
MCP makes the knowledge graph available to coding agents. Tools like Cursor and Claude Code can list repos, search across your workspace, pull API schemas and call flows, and fetch any system detail they need. Every answer they receive comes from the indexed structure of your codebase, not guesswork.
Get started
This first version of AI Architect is ready for teams that want system context inside their tools. You can set it up through MCP, connect it to your repos, and start working with a real map of your system. Follow the setup steps in the documentation and begin using AI Architect with your own codebase.
“We’ve spent too much time fixing agent-generated code. Now most changes work in one shot because Bito’s AI Architect actually understands our services and APIs. It’s been a huge boost to Cursor.”
