Beatrice Prasad

Governance, not model quality, decides which agent pilots survive.

Secure multi-agent workflows by enforcing user permissions at every agent hop, not just login.

AI agents must inherit user permissions directly from source systems rather than duplicate them.

Bind agent requests to user identity so source systems enforce their own access policies.

Context matters more than any single productivity number for AI coding tools.

MCP standardizes qualification workflows by eliminating custom integrations between sales tools.

Domain experts should own AI workflows, not central IT teams.

Infrastructure-layer isolation stops poisoned memories from persisting and spreading across tenants.

Five agent types demand different permission models to contain their unique risks.

MCP's dynamic nature demands layered security that traditional APIs can't provide.

MCP connects AI to enterprise systems, but leaves security and governance to you.

Enterprises must discover and audit AI agents already running beyond IT's visibility.

Enterprise control planes unify fragmented AI agents through governance and orchestration standards.

Enterprises need infrastructure to govern prompts across tools, teams, and compliance regimes.

Stale data in AI systems erodes accuracy and user trust faster than better models can fix it.

MCP solves the governance problem hiding inside enterprise AI integration.

Structuring SOPs as agent context enforces business rules at runtime.

Six governance structures enterprises need to manage AI deployment at scale and speed.

AI agents need per-tool, per-action permissions, not blanket upfront access.

Organizations must treat AI skills as managed, discoverable assets or face compound fragmentation.

Enterprises need structured data access, not just smarter models, to deploy AI agents effectively.