Multi-Agent Orchestration System
A language-enhanced LAMP framework with Think-Speak-Decide reasoning for coordinated agent work and explainable decisions.
LAMP Three-Stage Reasoning Pipeline
Language-enhanced multi-agent planning for coordinated work
Think Module · Continuous Reasoning
Parallel short-term and long-term reasoning for current state, risks, and trends
- Parallel short-term and long-term reasoning
- Environmental shock detection and severity assessment
- Trend tracking and strategic analysis
Speak Module · Agent Communication
Semantic communication between agents, with shared context and reasoning evidence
- Multi-candidate message generation with quality scoring
- Belief state management and trust updating
- Shared knowledge base and strategy preference learning
Decide Module · Decision Fusion
Adaptive decision making based on observations, reasoning, and belief state
- Three-source data fusion (numerical, reasoning, belief)
- Weighted scoring and LLM-based decision making
- Fallback mechanism for system stability
Core Capabilities
Technical building blocks for enterprise-grade agent orchestration.
Intelligent Task Routing
Analyze task complexity and choose single-agent or multi-agent execution automatically.
Hybrid Reasoning
Balance immediate reasoning with long-term trend analysis.
Semantic Communication
Share rich semantic context and reasoning evidence between agents.
Three-Source Decision Fusion
Combine numerical observations, reasoning analysis, and belief states into explainable decisions.
Vectorized experience memory
Retrieve and reuse successful experiences to reduce planning time by 40-60%.
Dynamic Replanning
Trigger replanning when conditions change, while preserving completed work.
Validated Performance Improvements
Measured gains from reasoning, scheduling, and experience reuse.
Architecture Flow
From task intake to coordinated execution and learning
Routing decision
Use an LLM to analyze task complexity and select the right execution strategy.
Task decomposition and planning
Retrieve similar historical experiences and generate an execution plan with DAG dependencies.
Parallel DAG scheduling
Use layered execution and topological sorting to maximize parallelism.
LAMP reasoning and decision making
Use Think-Speak-Decide reasoning, communication, and decision fusion during execution.
Experience persistence
Evaluate and store high-quality reasoning trajectories for future retrieval and reuse.
Explore Multi-Agent Orchestration
See how LAMP and DAG scheduling can improve your enterprise AI workflows.