Tian AI presents an autonomous agent system that uses an LLM-driven task scheduler to plan, execute, and adapt tasks without human intervention.

The article was published on April 25, 2026, by the Tian AI Dev Team.

Unlike traditional AI assistants that merely answer questions, Tian AI proactively executes multi-step tasks to boost productivity.

An autonomous workflow example shows generating a promotional video and attempting to post to Dev.to; video generation proceeds while posting is blocked due to missing write permissions, underscoring safety-conscious design.

Greeting shortcuts bypass the LLM for efficiency by recognizing common greetings through predefined patterns.

The architecture workflow starts with a user request, then the LLM parses and classifies intent, followed by a task queue with dependency resolution and safety whitelist checks, leading to task execution via tools or shells, a self-reflection loop, and finally a result summary.

The task queue handles dependencies with topological sorting to determine execution order, such as checks that must precede backups and reports.

A safety whitelist restricts autonomous action: allowed directories are limited to the project root, shell commands are restricted (no dangerous commands), read-only by default with explicit write permission, and network access is limited to select sites like dev.to and huggingface.co.

A self-evaluation loop verifies task completion and output consistency and assesses whether alternative approaches should be tried after each task.

Key components include an LLM parser (using Qwen2.5-1.5B) that breaks requests into actionable tasks with intents like plan, execute, search, ask, and greet.