Turns delivery from "file a request and wait" into "submit a request and the code writes itself" — AI drafts the plan, Workers code, test, and deploy automatically, and you just review and approve. Every delivery also becomes the asset that makes the next one faster.
Business teams submit requests themselves, with clear goals and acceptance criteria.
Auto-generates a plan — technical approach, task breakdown, and effort estimate.
An AI Worker claims the task and writes, commits, and pushes the code.
AI generates test cases and runs unit and interface tests automatically.
Builds and deploys automatically, with multi-environment and canary support.
Auto commit & push — the whole delivery stays traceable end to end.
tsanCode runs reliably at L3 today: given a request, it branches, codes, self-tests, and opens a PR. Humans still set the goals and review the key checkpoints.
Business teams submit requests themselves — with priority, module tags, and status tracking — visible end to end from intake to delivery.
Once submitted, AI analyzes the request and drafts a plan — technical approach, task breakdown, and effort estimate.
Plans go to a reviewer online — approved ones auto-queue for execution, rejected ones go back for revision, all of it logged.
A standing process calls AI through the Agent SDK — claiming tasks, writing code, and committing & pushing to Git automatically.
AI generates test cases and runs unit and interface tests — coverage and results report in real time, with quality gates blocking bad builds.
Once tests pass, builds and deploys happen automatically — multi-environment configs and canary rollouts, no manual steps from code to production.
Configurable agents carry their own personalities and specialties, backed by an extensible skills system — dispatched to match each task precisely.
Built-in AI chat streams its answers and stays bound to the request's context — question, correct, and iterate any time, with no friction.
Register a Git repo and AI indexes its structure, business logic, and coding style — new code automatically follows existing conventions, blending in with zero ramp-up.
Execute → Evaluate → Accumulate Experience → Optimize Strategy → Evolve — every task makes the system understand your engineering a little better.
After each task, it checks output quality against test results and acceptance criteria, pinpointing exactly where it failed.
Automatically tunes prompts, tool calls, and workflow orchestration from historical data, continuously converging on a better solution.
Continuously picks up new skills from the knowledge base and recombines existing capabilities — capacity for complex tasks grows as projects accumulate.
Distilled from great engineers' coding style and experience into your own dedicated agents — on call 24/7, dispatched on demand.
A full-stack expert in Node.js and Vue, skilled at API design and front-end components — methodical, and quality-obsessed.
Focused on Vue 3 components and pixel-accurate UI — works closely with designers to ship polished interfaces fast.
An Express API and database design expert focused on interface standards and data consistency — tunes query performance to keep systems stable.
Turns ambiguous requests into clear, executable plans, producing well-structured Markdown docs.
Plans, test reports, and code changes are all captured automatically; repo rules are versioned with the code and read — and strictly followed — by AI on every run.
The model layer is pluggable — connect mainstream models via API config, scheduled automatically by task type and cost, with multi-model racing and fallbacks keeping latency low and availability high. Runtimes delivered to customers default to domestic models with full on-premises deployment; overseas models are an option in our internal engineering toolchain only and never enter the customer's delivery environment.