AI systems

AI systems are more than models. This topic covers the engineering choices that turn models, data, tools, and workflows into systems people can use reliably and efficiently.

It includes production readiness, model evaluation, MLOps, agent harnesses, cost and capability trade-offs, and the operational details that determine whether an AI system keeps working after the demo.

Start here

• The agent harness is part of the model — why tools, prompts, evaluation, and runtime shape the system’s actual capability.
• The Harness Changes the Score — how the surrounding workflow changes what a model can achieve.
• A Model Gateway Made Provider Switching Much Easier — the case for a control layer between applications and model providers.
• The model has to stay up — reliability is part of the product, not an operational afterthought.
• Blender Is Becoming a General-Purpose Canvas for Coding Agents — what an application API makes possible for an agent.

Questions this hub will answer

• How should a team evaluate an AI system beyond a model benchmark?
• What belongs in an agent harness, and what belongs in the model?
• When does a model gateway justify its operational cost?
• How can teams measure reliability, latency, and cost in production?

Browse all writing → · See the AI governance hub → · See the AI application hub →