Research Objectives
We focus not on making AI blindly invoke tools, but on building a "judge-plan-execute-feedback" execution architecture. Each action must be traceable, interruptible, and improvable.
In client environments, agent execution must simultaneously satisfy business efficiency, permission boundaries, audit trails, and multi-system coordination — requiring a rigorously designed execution framework.
Define input/output, permission boundaries, failure retry, and human takeover mechanisms for each tool type — preventing uncontrolled execution.
Decompose complex tasks into recordable, replayable, and evaluable execution steps — supporting subsequent optimization and auditing.
Enable agents with different responsibilities to collaborate around the same business goal — forming a continuous loop from analysis to action.
Technical Pathway
Task Intent & Execution Planning
First identify task goals, business objects, constraints, and success criteria — then generate decomposable, verifiable, and interruptible execution plans.
System Connection & Tool Protocols
Build a unified invocation layer around APIs, databases, SaaS systems, and internal processes — standardizing authentication, data formats, and error handling.
Execution Feedback & Exception Handling
Record status, inputs, outputs, failure causes, and business impact for every execution — feeding results back to the AGI decision and memory systems.
Current Focus Areas
We are strengthening the engineering-grade execution capability of agents — moving from "automation scripts" to "business execution entities" that can independently handle complex workflows while maintaining full controllability.
