Better agents. Deeper questions.
How does enterprise intelligence keep improving?
Five questions behind Sage and Sygnal. From understanding your enterprise to learning from every execution.
Explore the questions, directions and measures below
Enterprise context & reasoningSage
How do agents build a working understanding of a changing enterprise?
Connect schemas, code, business rules and relationships into context that agents can retrieve, reason over and trace to its source. Explore how that context stays current as systems change.
Task success on unfamiliar schemas, evidence accuracy and resilience to changes in enterprise data.
Verification & reliable executionSygnal
How can agents tell whether their work actually succeeded?
Explore execution harnesses, independent checks and recovery from failed steps. Use verified outcomes to decide which experiences are useful for learning.
End-to-end task success, failures detected, incorrect results accepted and recovery without human intervention.
Learning from executionSygnal
How do individual episodes become learning that improves the next task?
Capture actions, context, feedback and outcomes. Study which decisions contributed to a result, and how to learn from both successful executions and failed attempts.
Improvement on held-out tasks, fewer repeated errors and the amount of verified experience needed to improve.
Reusable skills & memorySygnal
What should an agent remember, and what should become a reusable skill?
Turn successful approaches into skills with clear conditions for use. Explore how agents retrieve, compose and update those skills across tasks while retaining useful prior knowledge.
Transfer to new tasks, fewer execution steps and preserved performance on previously learned work.
Fine-tuned on-prem modelsSygnal
How can verified enterprise experience improve models that run on-premises?
Explore fine-tuning and distillation using verified execution data. Study when to update a model, when to improve its context, and when a new skill is enough.
Domain task accuracy, inference cost and latency, measured alongside regressions and deployment requirements.