Network Validation
Validation is continuous and distributed. Quality emerges from consensus.
Agent Validation
Fixies validate, infer, and update the graph
Consensus Mechanisms
Multiple validators must agree before information is accepted
Real-time Correction
Errors are detected and corrected automatically as they occur
The Validation Pipeline
Ingestion
New data enters the network from various sources and agents
Analysis
Fixies analyze data for consistency, accuracy, and completeness
Consensus
Multiple validators vote on data quality and accuracy
Integration
Validated data is integrated into the knowledge graph
Fixies: Validation Agents
Fixies are autonomous agents embedded in the system. They validate, infer, and update the graph directly. A Fixie might infer a new relationship and add it, or observe data that contradicts existing knowledge and flag it for review. They also gather feedback and incorporate learning.
Fixies operate on-premises, in the cloud, or embedded in Letta.
- Data collectors, validators, analyzers, and maintainers work together
- Agents can detect anomalies, infer new relationships, adapt from feedback, and maintain data integrity
How we ensure accuracy in real time
Validation is continuous and distributed. Fixies analyze, cross-check, and update the graph as new data arrives.
Consensus Mechanisms
Multi-Agent Validation
Multiple Fixie agents independently validate each piece of data before it's accepted into the knowledge graph.
- • Independent verification
- • Majority consensus required
- • Conflict resolution protocols
Expert Review
Human experts can be brought in for complex validations, especially for domain-specific knowledge. Reputation scores and token-based incentives (Florin) apply.
- • Domain expert validation
- • Community peer review
- • Reputation and trust score weighting
- • Florin token incentives
Quality Assurance
Continuous Monitoring
The network continuously monitors for changes, inconsistencies, and new information that might affect existing knowledge.
- Real-time anomaly detection
- Consistency checking
- Automated error correction
Trust Scoring
Every piece of data receives a trust score based on its source, validation history, and consensus level among validators.
Self-Healing Network
ƒ(xyz) networks automatically detect and repair inconsistencies, ensuring data integrity over time.
Automatic Correction
When conflicts are detected, the system applies corrections based on consensus and trust scores.
Adaptive Learning
Validation algorithms improve over time, learning from past validation decisions and outcomes.