Core Component
🎯
Multi-Agent Orchestrator
The brain of the QphiQ platform - coordinates specialized AI agents, manages task distribution, handles inter-agent communication, and builds consensus from multiple analysis streams.
01
How It Works
📥
Receive Query
Parse user input & parameters
→
🔀
Plan Workflow
Determine agent sequence
→
🚀
Dispatch Agents
Run agents in parallel
→
🤝
Coordinate
Handle agent communication
→
✓
Synthesize
Build final consensus
02
Agent Coordination Patterns
⚡
Parallel Execution
Multiple agents run simultaneously on independent tasks, maximizing throughput.
Promise.all([
literatureAgent.run(),
trendsAgent.run(),
authorAgent.run()
])
🔗
Sequential Pipeline
Agents pass results to the next in chain when output depends on previous analysis.
papers = await search()
analysis = await analyze(papers)
verified = await verify(analysis)
🤝
Consensus Building
Multiple agents analyze same data, orchestrator reconciles differences.
results = [agent1, agent2, agent3]
consensus = buildConsensus(
results, threshold: 0.85
)
03
Agent Communication Protocol
Message Types
TASK_ASSIGNOrchestrator assigns work to agent
PROGRESS_UPDATEAgent reports current status
DATA_SHAREAgent shares findings with others
CLARIFY_REQUESTAgent asks for more context
TASK_COMPLETEAgent reports completion
ERRORAgent reports failure
Message Structure
{
"id": "msg_123abc",
"type": "DATA_SHARE",
"from": "literature_agent",
"to": ["trends_agent", "orchestrator"],
"timestamp": "2024-12-30T...",
"payload": {
"papers_found": 247,
"top_papers": [...],
"confidence": 0.94
}
}04
Error Handling & Recovery
Failure Modes
Agent Timeout
→ Retry with exponential backoff
API Rate Limit
→ Queue requests, use fallback sources
Partial Results
→ Proceed with available data + disclaimer
Agent Disagreement
→ Escalate to verification agent
Complete Failure
→ Graceful degradation + user notification
Performance Metrics
Avg Response Time
35s< 60s
Success Rate
98.5%> 95%
Agent Utilization
87%> 80%
Consensus Rate
94%> 90%
Error Recovery
99%> 98%