AI writes code quickly and cheaply. The hard part is not generating it — it is making sure the result is correct. That is why Xedant Agent is built around deep, multi-level validation of everything the model does. Every part of the interface helps you check and fix the result faster and more thoroughly, including where a plain console tool cannot help. Combined with the model teaching itself through the CLAUDE.md file and skills, the effect adds up: a model that once struggled with the simplest tasks works steadily and accurately for hours after a few dozen self-correction cycles.
All of these validation tools work together as one system. Use them on their own, or combine them into automatic validation chains — whichever suits you better:
Automated Build Validation
Compilers, linters (automatic checks for code errors and style), type checkers, and tests serve as automatic quality checkpoints. AutoFix closes the loop: a build finds an error, and the model fixes it on its own.
Automated Prompt Builds
After the main chat finishes, a separate reviewer agent starts. It works through the result and returns only its remarks — without cluttering the main chat with extra context.
Chat Output Validation
Statistics for messages and chats — a simple way to watch how fast and how efficiently the model works.
Git Change Validation
All code changes are visible on the panel to the right of the chat. Commit or roll back in one click, and every commit stays fully traceable to the conversation that produced it.
Deploy Output Monitoring
Logs from the running application are parsed in real time: errors and warnings stand out immediately, so you never have to read through them by hand.
File Viewer & Script Runner
A built-in viewer with syntax highlighting: quickly inspect code by hand, make edits, and run scripts right in the interface.
Model & Skills Control
Switch between models right in the middle of a conversation, and tune the agent’s skills and workflows for each task.
Docker Isolation & Skip Permissions
The agent works in a fully isolated container — a sealed-off environment separate from the rest of your machine — so you do not have to confirm every single action; only the end result gets checked.
Model Control with Hooks
Hook rules intercept the agent’s actions before they run — before each tool call and before each prompt is sent: they block dangerous commands, reject unwanted operations, and enforce the project’s rules.
Analytics & Observability
Detailed analytics on tokens (the pieces of text a model reads and writes), costs, speed, chats, commits, and performance. Statistics from all your projects can be sent automatically to your own ClickHouse database for centralized monitoring.
Sound Feedback Validation
Builds, deploys, commits, and messages can be monitored by ear: each event has its own sound, so the status is clear without watching the screen. The standard sounds are easy to replace with your own.