Model Governance explains how TealTiger governs which AI models are allowed, for what purposes, and under what risk boundaries in agentic AI systems.
The Model Governance Hub defines normative controls for model approval, task binding, risk boundary enforcement, and lifecycle accountability.
In agentic environments, uncontrolled model usage introduces serious risks — including inconsistent behavior, compliance exposure, cost volatility, and untraceable decision paths.
TealTiger treats model selection and usage as a governance decision, not a runtime convenience.
What This Hub Covers
- Governing approved models and providers
- Binding models to task types and risk classifications
- Preventing unauthorized or implicit model switching
- Enforcing deterministic model usage policies
- Producing audit‑ready evidence of model decisions
Why Model Governance Matters
Without explicit model governance:
- Different models may be used silently for the same task
- Risk characteristics change without review
- Compliance assumptions break
- Cost and behavior become unpredictable
TealTiger enforces explicit, contract‑defined model usage, ensuring consistency, traceability, and control.
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Model Governance defines how TealTiger controls model capability, selection, and drift in agentic AI systems.
In agentic systems, model choice is not a static configuration. Models may be selected, escalated, or switched dynamically at runtime. This hub focuses on governing model capability as a first‑class risk surface, ensuring that only approved, risk‑appropriate models are allowed to execute.
What This Hub Covers
- Approved model registries for enterprise AI
- Task–model binding to prevent silent capability escalation
- Detection and prevention of shadow model usage
- Runtime enforcement of model approval and escalation
- Audit‑grade evidence of model governance decisions
Why Model Governance Matters
Without explicit model governance:
- Agents silently escalate capability
- Unapproved or experimental models execute in production
- Risk posture changes without review
- Cost and compliance exposure increases
- Audits cannot explain why a model was allowed
TealTiger treats model choice as a governed execution decision, not an implementation detail.
Core Articles
- Model Governance Overview
- The primary model for controlling AI model capability, approval, and enforcement in agentic systems.
Additional Articles
- Approved Model Registries for Enterprise AI
- Task–Model Binding: Preventing Capability Drift
- Shadow Models and Hidden Risk in Agentic Systems
These articles expand the model governance framework by showing how approved registries, capability binding, and runtime enforcement prevent unintentional escalation and hidden model risk.
Related Insights
These Insights explain how model governance decisions translate into runtime accountability: