AI Agents

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.

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

Choose Graph RAG for connected retrieval, agent memory for reusable experience and world state for authoritative current truth.

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.

MCP exposes capabilities, skills encode working methods, cowork systems host delegated work, and the coordinator preserves process control and traceability.