AI Architecture

As software gains autonomy, permissions alone become insufficient. Safe agent systems need authority isolation, constrained action spaces and boundaries outside the agent's control.

Agent memory requires typed records, controlled writes, scoped retrieval, consolidation, provenance and deletion—not just a vector database.

Learn the components of a production AI agent and where reasoning, memory, state, tools and approval should live.

The coordinator owns the run lifecycle and accepted transitions, allowing organizations to use probabilistic models without surrendering process control.

AMR captures who did what to whom; execution graphs define state, permitted transitions, tools and validation for agentic workflows.

Explore how models, agents, knowledge graphs, workflows and validation fit into a controllable generative AI production architecture.

Use a knowledge graph to give AI agents an inspectable map of entities, relations, provenance, permissions and dependencies.

A stateful agent needs more than conversation memory: it needs explicit state ownership, transitions, versioning and recovery.

Generative models become more capable by absorbing production decisions. This essay argues for keeping truth, causality and production state outside the model while using LLMs and video generators as bounded probabilistic operators.

Choose between deterministic pipelines, coded AI workflows and dynamic agents with a practical production architecture framework.

AI-powered image generation has advanced rapidly, with four distinct architectures standing out among the vast array of models: VAEs, GANs, ViTs, and SD. This blog post provides a comprehensive comparison of their primary purposes, methods, and performance metrics to understand the fascinating world of AI-generated imagery. Each architecture has subtypes and variations, and researchers frequently combine elements to create hybrid models, with generative models rapidly evolving as new techniques and improvements emerge.