Most teams building AI agents are blaming MCP when their integrations fall flat. Gil Feig, co-founder and CTO of Merge, says that's the wrong diagnosis entirely — and he built the infrastructure layer that connects agents to enterprise systems to prove it.

Gil makes the case that MCP is a thin wrapper around API endpoints, and the actual failure point is the access pattern underneath it. He lays out a clear framework for when synced-and-stored data is required versus when live connectors are sufficient, explains why the "talk to your data" promise keeps breaking in practice, and shares how Merge approached agent guardrails from day one — including why prompt-based soft restrictions are already being exploited and why temporary tokens are emerging as a hard security primitive for scoping what an agent can touch and for how long. He also argues that a world where all enterprise data flows into centralized AI-queryable lakes is economically flawed and probably not where the market lands.

Topics discussed:

• MCP as a thin API wrapper and why the access pattern is the real failure point

• Sync-and-store vs. live connectors: the decision framework for each

• Hard vs. soft agent guardrails and where soft blocks break down

• Temporary tokens as a scoped-access security primitive for agents

• Why "talk to your data" implementations fail without structured local data stores

• The true cost of full data replication, vectorization, and embedding at scale

• Enterprise vs. mid-market governance requirements for LLM data routing

• Why the all-roads-lead-to-data-lake future is economically unlikely