Core Technology
Context Engineering Driven Autonomous Agent Collaboration Architecture
Enterprise-grade AI infrastructure built on cognitive artifact theory, enabling multi-agent autonomous collaboration and differentiated knowledge management.
Four-Layer Architecture
End-to-end agent collaboration platform with complete technology stack from foundational capabilities to business applications.
Business applications including intelligent review and writing
FIM Agent framework enabling multi-agent autonomous collaboration
Multi-tenant knowledge architecture with three-tier management
Underlying AI infrastructure and data storage
Technical Innovations
Four core technical breakthroughs solving key challenges in enterprise AI deployment.
Context Engineering Driven Agent Collaboration
Addressing Recursive Information Decay
Drawing from cognitive artifact theory, we research context passing, compression, and recovery mechanisms in chained task execution. Our "Verbatim Grounding" strategy ensures information integrity and consistency in long-chain tasks.
Multi-Tenant Knowledge Tiered Adaptation
Default Isolation with Opt-in Sharing
Innovative knowledge permission model supporting differentiated management and on-demand sharing. Implements global, organizational, and tenant-private hierarchy for complex enterprise group governance.
Domain Semantic Understanding & Reasoning
Four-Element Evidence Chain
Building temporal-aware domain knowledge graphs with "Issue-Evidence-Logic-Conclusion" chains, ensuring full explainability and complete traceability to original sources.
Heterogeneous Compute & Governance
Shielding Hardware Differences
Proprietary Heterogeneous Abstraction Layer (HAL) for unified pooling of localized (GPU/NPU) and general compute; Model Mesh based microservice governance for fault recovery.
FIM Agent Core Capabilities
A lightweight Python Agent runtime where models own decisions and frameworks own scheduling. Drives goal-level autonomous execution through dynamic DAG planning and ReAct reasoning.
Dynamic DAG Task Planning
LLM decomposes complex goals into dependency-aware task graphs at runtime with built-in topological sort and cycle detection for reliable autonomous planning
ReAct Reasoning & Concurrent Execution
Structured reasoning-and-acting loops drive intelligent decisions while independent steps run in parallel; compatible with any OpenAI API, only three runtime dependencies
Pluggable Tool Registry
Protocol-based tool interface with centralized ToolRegistry supporting hot registration and plug-and-play capability extension
Execution Reflection & Adaptive Re-planning
Auto-evaluates goal achievement and iteratively re-plans when unsatisfied; built-in RAG retriever interface for knowledge-augmented decision making
Technical Metrics
Core performance metrics validated in production environments
Core Research
Academic publications from our core team members, providing theoretical foundations for our technical innovations.
CogCanvas: Verbatim-Grounded Artifact Extraction for Long LLM Conversations
Proposes the Verbatim Grounding framework to address information decay in long conversations
View PaperAI as Cognitive Amplifier: Rethinking Human Judgment in the Age of Generative AI
Proposes a theoretical framework for AI as cognitive amplifier
View PaperCognitive Workspace: Active Memory Management for LLMs
Proposes the Cognitive Workspace paradigm that goes beyond traditional RAG
View Paper