Token Optimization and Context Efficiency
Reduce enterprise AI context cost with dynamic tool loading, response projection, and JSON Compact or TOON compression in OL Gene.
Challenges
- Enterprise agents waste tokens loading full tool catalogs even when they only need a few operations.
- Large API payloads send unused fields and repeated JSON syntax into the model context.
- Teams cannot shrink context without changing integrations, RBAC, or recording policy.
Benefits
- Load full schemas only for the operations an agent is about to call, improving tool selection and cutting prompt size.
- Project responses with JMESPath so only task-needed fields remain, per call or as action defaults.
- Compact repetitive JSON when savings meet the configured threshold, lowering LLM cost and latency under existing governance.
OL Gene reduces the context tax of enterprise AI by controlling three sources of waste: oversized tool catalogs, unused API response fields, and repeated JSON syntax.
How it works
- Dynamic tool loading — agents discover applications and actions at summary level, then load full schemas only for operations they are about to call.