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AI Agents That Write, Test, and Deploy Code
Autonomously.
From boilerplate to production code — we build AI agents that understand your codebase, follow your patterns, and ship with full test coverage.
Ship Code 100x Faster
Top engineering teams are automating their entire development workflow with AI agents. From code generation to deployment.
- Autonomous coding — Agents write, test, and refactor
- CI/CD integration — Auto-deploy passing builds
- Security scanning — Catch vulnerabilities early
- Performance optimization — Faster load times
- Design systems — Generate UI variations
Trusted by innovative teams building with AI
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"If you're looking for a team that can support you, help scale your product, and be a true technical partner, slashdev.io is the way to go. Features that took months to finish are getting done in days. They have completely changed my business!"
Development bottlenecks are everywhere
Teams spend 60% of their time on repetitive code, boilerplate, and maintenance
Boilerplate
Hours spent writing repetitive CRUD, validations, and endpoints.
Context Switching
Looking up patterns and docs breaks developer flow state.
Testing Gaps
Writing tests is slow, leading to coverage gaps and bugs.
Security Risks
Manual code review misses vulnerabilities in complex code.
AI agents that understand your code
Purpose-built coding agents trained on your codebase and conventions
Codebase Understanding
Agents analyze your entire repository structure, dependencies, and coding patterns to generate contextually appropriate code.
Style Compliance
Generated code follows your team's conventions, linting rules, and architectural patterns automatically.
Test Generation
Automatically create unit tests, integration tests, and edge case coverage alongside feature code.
Security Scanning
Built-in vulnerability detection catches common security issues before code reaches production.
Rapid Iteration
Refine and iterate on generated code through natural conversation until it meets your needs.
Multi-Language Support
Generate code in TypeScript, Python, Go, Rust, and more—with framework-specific patterns.
From prompt to production code
A seamless workflow that keeps developers in control
Codebase Indexing
The agent indexes your repository, learning your project structure, dependencies, naming conventions, and architectural patterns.
agent.index({
repository: "./",
include: ["src/**", "lib/**"],
analyzePatterns: true,
learnConventions: true,
})Intent Understanding
Describe what you want in natural language. The agent interprets your intent and maps it to concrete code requirements.
// Natural language input "Add a user preferences API endpoint with validation and caching" // Agent interprets as: → Create REST endpoint → Add Zod schema validation → Implement Redis caching → Follow existing API patterns
Code Generation
The agent generates complete, production-ready code that follows your patterns, includes error handling, and passes your linting rules.
// Generated: api/preferences/route.ts
export async function GET(req: Request) {
const session = await getSession(req)
if (!session) return unauthorized()
const cached = await redis.get(cacheKey)
if (cached) return json(cached)
const prefs = await db.preferences.find(...)
await redis.set(cacheKey, prefs, "EX", 3600)
return json(prefs)
}Test & Verify
Automatically generated tests ensure the code works correctly. The agent also runs security scans and suggests improvements.
// Auto-generated: __tests__/preferences.test.ts
describe("Preferences API", () => {
it("returns cached data when available")
it("fetches from DB on cache miss")
it("requires authentication")
it("validates input schema")
it("handles database errors gracefully")
})
// Security scan: ✓ No vulnerabilities
// Coverage: 94% statementsAgent architecture
How we build code generation agents that actually work
code_agent:
name: "CodeGen"
context: { indexer: "tree-sitter", store: "pinecone" }
models: { primary: "claude-3-opus", fast: "haiku" }
tools:
- file_system: [read, write, create]
- git: [diff, blame, history]
- linter: { config: ".eslintrc", auto_fix: true }
- test_runner: { framework: "vitest" }
pipeline: [intent, context, plan, generate, lint, test, review]Context Engine
Tree-sitter parsing combined with code-specific embeddings enables deep understanding of your codebase semantics.
Multi-Model Strategy
Different models for different tasks—Claude for reasoning, specialized models for code, fast models for quick edits.
Tool Integration
Direct integration with your development tools—Git, linters, test runners, and security scanners.
Human-in-the-Loop
Every significant change goes through review. The agent suggests, but you decide what ships.
Impact on development teams
Metrics from teams using our code generation agents