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Announcing the launch of Org-Native Agents.
AI must understand your codebase, architecture, standards, and documentation — otherwise it hallucinates, produces inconsistent code, and creates technical debt. Look for:
Deep repository awareness and architecture-level understanding
Ability to follow organizational patterns and frameworks automatically
Accurate code completions, explanations, tests, and refactors grounded in real context
Minimal reliance on prompt engineering
Enterprise-wide consistency in outputs, even across diverse teams
SaaS, VPC, on-prem, or fully air-gapped. Enterprises — especially in regulated or mission-critical sectors — must be able to deploy AI inside their own security perimeter.
Look for:
Identical functionality across SaaS, VPC, on-prem, and air-gapped installs
Offline update mechanisms for disconnected networks
Support for export-controlled and certification-bound environments (e.g., ITAR, DO-178C)
Alignment with your DevSecOps and network architecture
Ability to isolate models, data, and logs within your environment
Without governance, AI becomes fragmented and high-risk. A control plane provides model access control, policy enforcement, audit trails, cost predictability, and real productivity measurement across the org.
Look for:
Central controls for model selection and usage boundaries
Policy enforcement for security, coding standards, and data access
Full audit trails of prompts, actions, and generated code
Org-wide metrics: acceptance rates, acceleration impact, adoption trends
Cost caps, usage visibility, and predictability across teams
