Krnel Helps @ Every Stage
Of Agent Development
Representation engineering throughout the Model Development Life Cycle
Data
Use representation analysis to monitor data:
- Quality
- Complexity
- Drift
- Provenance
Train
Understand how models represent learned concepts during training
Align
Steer model behavior through representation manipulation and unlearning
Evaluate
Assess model beliefs and latent knowledge beyond output metrics
Deploy
Agent CI/CD:
- Detect anomalies and breaking changes in agent deployment
- TDD support feedbacks
Inference
Real time detection, control, and steering at runtime:
- Context management (prompt/RAG/Memory,...)
- Hallucination detection and management
- Tool execution risks
- Policy guardrails
Use representation analysis to monitor data:
- Quality
- Complexity
- Drift
- Provenance
Understand how models represent learned concepts during training
Steer model behavior through representation manipulation and unlearning
Assess model beliefs and latent knowledge beyond output metrics
Agent CI/CD:
- Detect anomalies and breaking changes in agent deployment
- TDD support feedbacks
Real time detection, control, and steering at runtime:
- Context management (prompt/RAG/Memory,...)
- Hallucination detection and management
- Tool execution risks
- Policy guardrails
Learn more
Explore our latest insights on representation engineering, the technology behind it and use cases
Read all posts
Discover the latest research, insights, and updates on representation engineering and AI control
Technology
Learn about our approach to understanding and controlling AI through representation engineering
Use cases
See how representation engineering can be applied across the Model Development Lifecycle
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