Why Governance Is Critical For AI Agent Success

By Shazia Manus, Chief Data and Analytics Officer for TruStage

As AI agents rapidly democratize—envisioned in scenarios like autonomous medical diagnostics—governance remains an overlooked but critical component. While agents dominate headlines, frameworks ensuring accountability must keep pace with autonomy.

Governance As The Supporting Actor

Workday CEO Carl Eschenbach anticipates AI agents will "peacefully coexist" with humans, but this requires reassurance through standards. Early-adopter firms now set precedents for future governance, balancing business value with risk.

Key Governance Strategies:

1. Seize the AI Moment: With enterprise AI spending rising 5.7% in 2025 (ISG Study), governance should be embedded in AI projects from inception.
2. Define Permissible Use Cases: Criticality matrices can exclude high-stakes decisions (e.g., end-of-life care) while enabling safe automation (e.g., appointment scheduling).
3. Measure Learning: Track experimentation frequency and adaptations. Example: A payments firm tweaked an AI fraud detector after false positives disrupted customer experience.

The Path Forward

Governance ensures AI narratives have "purpose and staying power" by establishing:

• Sustainable funding models
• Intentional use cases with boundaries
• Principled KPIs (e.g., adaptation metrics)

Forbes Technology Council members emphasize that human agency and operational safety must remain central as AI agents evolve.