ai-systems-architect

You can prevent agent loops and cost overruns in AI architecture by defining iteration caps, timeouts, and token budgeting guardrails. These constraints ensure dependable multi-agent workflows by stopping infinite tool calls and restricting execution cycles.

Choosing between RAG and fine-tuning depends on update frequency, latency constraints, and deployment scale. RAG retrieves external context dynamically, while fine-tuning embeds static knowledge, requiring a decision framework to align model routing with production requirements.

Context window budgeting for multi-agent workflows allocates specific token limits across memory tiers and vector stores. This strategy prevents context overflow by managing hybrid embedding distribution and ensuring agents operate within their designated processing capacity.

Yes, you can use MCP tools with secure auth and rate limiting by selecting appropriate MCP transport options. Configuring these transport and authentication choices protects tool access and prevents unauthorized calls in production assistant environments.

LLM-as-judge evaluation needs golden-case testing to establish reliable quality gates against weak evaluation standards. Providing golden cases ensures the judge model verifies outputs accurately, preventing unreliable assessment in production AI architectures.

You should not use a reasoning model over a routing model when latency constraints and update frequency demand immediate response. Routing models provide faster dispatching for simple tasks, whereas reasoning models add overhead that may violate production AI requirements.