Moving beyond naive single-prompt loops to fault-tolerant, multi-tier agent systems. Production AI agent architecture demands decoupled supervisor-worker state machines, deterministic execution harnesses, and formal loop governance to prevent cascading hallucinations.
The central architectural benchmark establishing core patterns, failure modes, and governance for this cluster.
Building enterprise agents isn't just about calling an LLM. It requires seven architectural tiers: Foundation Models, Prompt Engineering, Context Engineering, Harness Engineering, Loop Engineering, Graph Engineering, and Ontology Engineering. Here is the complete blueprint.
Single monolithic agents collapse under multi-phase tasks. By organizing agents into hierarchical Supervisor-Worker architectures, engineering teams achieve fault tolerance, isolated context boundaries, and deterministic sub-task verification.
More capability doesn't automatically mean more intelligence. Why unrestricted tool access breaks production agents, and the five architectural control patterns needed to build dependable systems.
Every agent build is a trade-off between speed, control, and governance. Four real paths, four real trade-offs — here's how to pick without guessing.
Most executives think of AI as a chat window. But real business ROI begins when you transition from tools that talk to systems that work. Here is the plain-English executive breakdown of Chatbots, Copilots, and Autonomous AI Agents.
Spinning up a real local agent lab on a Windows machine, ADK, Google's Gemini Enterprise Agent Platform, and the wiring in between. First real build of the series, mistakes included.
Engineering blueprints for Anthropic's Model Context Protocol (MCP). Build production Streamable HTTP gateways, legacy SSE servers, and hardened tool schemas.
Eliminate hallucinations, brittle function calls, and cascading failures. Architectural patterns for schema hardening, permission boundaries, and evaluation loops.
Master context window economics: AST skeletonization, state handover checkpoints, negative prompt boundaries, and mitigating semantic drift in multi-turn agents.
