How Agentic Intelligence Is Built
Training behaviour. Governing reasoning. Measuring outcomes.
Reasoning Over Truth
Traditional RAG retrieves documents.
Agentic RAG evaluates them.
It identifies missing information, retrieves across sources, and applies knowledge at the right moment in an interaction — guided by intent and policy.
This ensures responses remain grounded, accurate, and contextually correct.
Agentic RAG
Institutional Intelligence
Agentic training fine-tunes private models on an organisation’s data, logic, and language. This embeds:
Agentic Training
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Industry context
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Internal rules
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Brand voice
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Decision boundaries
The result is not just access to data — but model-level behavioural calibration that creates AI operating like a trained employee.
GPU-Backed Training & The Agentic AI Index
AgenticScale works directly with NVIDIA to train conversational and agentic behaviour on GPUs.
Every agentic system is:
Continuously evaluated
Benchmarked
Improved via feedback loops
This goes beyond prompt engineering.
Our proprietary benchmark evaluates AI performance on real enterprise work — not academic tests. We measure:
The Agentic AI Index
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Reasoning accuracy on multi-step, ambiguous tasks
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Policy compliance under regulatory and business constraints
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Self-correction rate before human escalation
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Performance on corporate artifacts — emails, documents, spreadsheets