Engineeringstepstone

I built mcp-gate , a small Go reverse proxy for a problem I keep coming back to with LLM agents: How much authority should we actually give a model when it calls a tool? Repo: https://github.com/ananthaprakashb/mcp-gate A typical agent integration eventually ends up holding something powerful: an API key, service credential, OAuth token, or access to an MCP/tool server that can perform multiple operations. Even when the model is supposed to perform one very specific action, the credential it indirectly controls may authorize far more. I wanted the authorization boundary to look more like this: The model never receives the upstream API credential. Instead, the trusted orchestrator exchanges its gate credential for an ephemeral capability token authorizing one specific operation. For example: {   "route": "tickets",   "method": "POST",   "path": "/v1/tickets",   "ttl_seconds": 15 } The returned bearer token is HMAC-s...

Multi cloud architecture to enjoy benefit of different services and improve the availability is the trend. Especially financial institutions and global enterprises deploy workloads across public clouds (AWS, Azure, GCP) alongside legacy on-premises mainframes and private datacenters to balance scalability, vendor independence, and strict regulatory compliance. However, operating financial transactions across these disparate environments introduces critical architectural fragmentation and operational security gaps . Pretty hard to manage. The Core Problem: Architectural & Operational Friction When a financial transaction or data flow spans multiple cloud boundaries and on-prem systems, traditional perimeter-based security and monolithic orchestration engines break down.  Fragmented Identity & Authorization (IAM Silos): Each cloud provider and on-prem system maintains its own Identity and Access Management (IAM) framework, role definitions, and token formats (e.g., AWS IAM r...