Every company in AI coding builds the input side — better specs, better environments, better reviews. Rosentic is the missing layer between agents and production.
When multiple AI coding agents work on the same codebase simultaneously, they produce patches that are individually correct but collectively incompatible. Agent A adds a required field to an API endpoint. Agent B builds a frontend that calls the old endpoint. Git merges both cleanly. Tests pass on each branch. Production breaks.
This problem didn't exist when humans wrote code. Humans coordinated via Slack, standups, and PR reviews. Agents don't coordinate. They read the repo state at the time they start, work independently, and push when done. By the time Agent B finishes, Agent A has already changed the contract Agent B depends on.
Sourcegraph is the library. Rosentic is the bouncer at the door.
Rosentic builds a semantic dependency graph — a persistent index of how every function, API, and schema in a codebase depends on every other. Conflict detection is the first application of that graph. But the graph enables impact analysis, architecture mapping, API drift detection, and agent guardrails.
The more the ecosystem fragments — more agents, more repos, more languages — the more valuable a neutral verification layer becomes. GitHub won't support GitLab. Anthropic can't check Cursor's output. Neutrality is the structural moat.
Every company solves input — specs, environments, understanding. Nobody verifies that all the outputs are compatible before they merge.
AST parsing via tree-sitter. Same input, same output. No LLM inference, no hallucination, no false positives from model uncertainty.
Once you index how everything connects, switching away becomes extremely hard. Parse once, query forever.
Works with Cursor, Claude Code, Codex, Copilot, Windsurf. Works with GitHub, GitLab, Bitbucket. No lock-in.
Amazon's Kiro AI coding tool caused a 13-hour AWS outage by autonomously deleting and recreating a production environment. Alibaba tested AI agents on 100 codebases over 233 days — 75% of models broke previously working code. Karpathy's autoresearch runs 100 experiments overnight on git branches. Aaron Levie is building for "100-1000x more agents than employees."
The outages are small now. They won't stay small. Every company deploying AI coding agents will need an output verification layer. The question isn't if. It's when.
Same-language function signatures vs call sites. Tree-sitter AST parsing across 11 languages: Python, TypeScript, JavaScript, Go, Ruby, Java, Kotlin, Swift, Rust, C#, C++.
Cross-language HTTP contract detection. Python FastAPI routes matched to TypeScript fetch/axios calls by URL path. Working demo.
GraphQL schemas, protobuf contracts, event bus topics (Kafka, RabbitMQ), database migration conflicts.
Breaking change classification. Required field added = breaking. Optional field = compatible.
Every conflict edge carries evidence source and confidence score.
Rosentic was built by a founder with 18 years in enterprise tech sales and partnerships, currently at Palo Alto Networks. The problem was visible from inside — watching AI coding agents proliferate across enterprise customers while nobody built the output verification layer.
The engine and full go-to-market were built in 72 hours using Claude Code. The founding team is growing from Palo Alto Networks' engineering organization.