Thirty minutes of GPU warm-up across thousands of accelerators can waste expensive capacity. AI efficiency depends on choices across hardware, capacity, inference, models, and routing, where gains and mistakes compound.

• Match hardware to workload: coding favors heavy prefill and context processing; multimodal pipelines may need different resources for vision and text.
• Treat caching, quantization, fleet partitioning, and engine startup as tradeoffs between throughput, flexibility, and utilization.
• Benchmark changes with repeatable workloads: model swaps and lower precision can improve speed, but they can also change output quality and behavior.

Stable baselines make it easier to turn efficiency gains into lower latency, more capacity, or larger reasoning budgets.

AI gateways can sit in front of model keys, MCP servers, and A2A traffic, making licensing and failure modes architectural concerns. Comparing LiteLLM and agentgateway shows how differently they behave once identity, routing, and operations matter.

• LiteLLM has broader provider coverage, but SSO, OIDC/JWT, secret managers, and governance controls require Enterprise.
• agentgateway keeps JWT, CEL, mTLS, MCP OAuth, and external authorization in its Apache 2.0 tree, with a smaller runtime footprint.
• Their attack surfaces differ: LiteLLM production deployments depend on PostgreSQL and Redis, while agentgateway can run as one binary.

The better default depends on whether provider breadth or an open, general-purpose control plane is the main constraint.

MCP’s 2026-07-28 revision removes protocol-level sessions, changing how clients scale across load balancers and replicated servers. Requests now carry the metadata they need, while discovery and optional capabilities move out of the old initialization flow.

• Stateless requests let servers process calls independently and scale horizontally without shared session state.
• Versioned extensions keep the core smaller, so features such as Tasks or MCP Apps can evolve separately and be adopted only where needed.
• server/discover lets clients inspect supported versions and capabilities without creating a session or sending notifications/initialized.

The main shift is toward a more scalable, modular protocol with less connection state and clearer capability negotiation.

An MCP client may cross several authorization servers, backend services, and organizational boundaries in a single workflow. MCP 2026-07-28 tightens how those trust relationships are identified, validated, and maintained using established OAuth and OIDC mechanisms.

• Clients now validate the iss parameter when present, reducing the risk of accepting authorization responses from the wrong authority.
• Dynamic registration requires an appropriate OIDC application_type, while credentials remain bound to the authorization server that issued them.
• Step-up authorization preserves existing scopes, and refresh-token guidance better supports long-running agent workflows.

The key change is clearer, more verifiable authorization across distributed MCP systems without requiring a new IAM model.