MCP at Scale: Governing AI Agent APIs When Autonomous Systems Are Your Consume…
summary
Claims a production MCP gateway (now called AI gateway) that federates many MCPs and enforces policies across 30 agents and 100+ MCPs (≈500 tool calls) runs on an enterprise container platform and prevents data exfiltration, runaway token costs, and audit gaps. It implements flow profile packages (guardrails, eval thresholds, access keys), a demo shows three MCPs and a tool browser, and it uses model fallback cascades (primary GPT‑5.4 → fallbacks) plus eval gating to keep autonomous workflows working.
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chapters (21)
- 0:00MCP governance introduction
- 1:08AI agent definition
- 2:32API to AI gateway evolution
- 3:36Agent autonomy levels
- 6:49Super agent platform building
- 8:14API gateways vs MCP failure
- 11:24Scaling risks without governance
- 14:55API vs AI gateway differences
- 16:18MCP gateway architecture
- 18:03AI gateway design disciplines
- 23:40MCP gateway federation
- 24:24Agent identity control
- 25:51Guardrails for data leakage
- 26:53Policy enforcement flow
- 28:15Resilience and fallback
- 28:38Gateway evaluations
- 28:59Tool discovery alignment
- 30:03Technical architecture
- 31:46MCP gateway demo
- 39:36MCP gateway value
- 41:21Q&A on agent identity
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