A capacity-planning guide for multimodal AI workloads where text, voice, image and video generation have different latency, memory and GPU utilization profiles.
How to sandbox AI agents with scoped credentials, isolated execution, allowlists, approval gates and audit logs so tool use stays useful without becoming unrestricted.
A practical framework for setting AI inference service-level objectives across latency, task quality, availability and cost instead of optimizing one metric in isolation.
A practical defense-in-depth approach to prompt injection for AI agents that browse, retrieve documents and call tools with real side effects.
How to design multi-region AI inference with provider failover, regional data boundaries, health checks and graceful degradation.
A practical CI/CD workflow for testing LLM prompts, models, retrieval and agent behavior before changes reach production.
How to control vector database costs in production RAG systems through chunking, retention, indexing, caching and workload-aware architecture.
A production framework for tracing AI agent decisions, tool calls, retries, latency and failures without drowning teams in raw logs.
How to analyze AI unit economics by workload, model, customer and successful task instead of relying on one blended cost per user.
A production guide to idempotency, retries, timeouts and safe side effects when AI agents call external tools.
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