What MCP Servers Can and Cannot Do for Enterprise AI
MCP connects AI to enterprise systems, but leaves security and governance to you.
Beatrice Prasad
AIENM — Page 4
How MCP Server Architecture Actually Works
MCP eliminates N-times-M integration sprawl by standardizing how AI apps talk to tools.
Omar Marroquin
Measuring Prompt Duplication as an AI Program Cost Driver
Duplicate prompts hidden across your organization are quietly doubling your AI inference bill.
Julian Nystrom
Tracking AI Agents Deployed Across Enterprise Teams
Enterprises must discover and audit AI agents already running beyond IT's visibility.
Beatrice Prasad
AI Agent Token Usage Tracking Across Deployments
Connecting agent behavior to token spend requires tracking below the API layer.
Omar Marroquin
What Enterprise AI Agent Marketplaces Actually Give You
Marketplaces alone won't solve your integration problem—governance and context do.
Clara Bianchi
Failure Modes and Cascading Errors in Multi-Agent Systems
One bad agent output masquerades as data and corrupts the entire chain.
Julian Nystrom
Observability and Debugging in Multi-Agent Systems
MCP's standardized protocol layer reveals what multi-agent systems actually do.
Julian Nystrom
enterprise AI control planes for multi-agent workflows
Enterprise control planes unify fragmented AI agents through governance and orchestration standards.
Beatrice Prasad
permission-aware data access approaches in multi-agent orchestration
Permission checks at every layer of the chain catch what trusting a single gatekeeper misses.
Omar Marroquin
tools for governed AI skill sharing across enterprise teams
MCP standardizes AI skill sharing across teams while keeping governance automatic, not gatekeeping.
Omar Marroquin
Agent registry and tool catalog solutions for enterprise AI governance
How companies track and control which AI agents can access which tools.
Julian Nystrom