Enterprise AI Workflow Patterns: Autonomy vs Control
Choose between single inference, fully autonomous agents, deterministic workflows, and guided hybrid agents. Comparison matrix, JSON contracts, and when to escalate from one call to a full agent.
G.A.I.N
Articles grounded in the G.A.I.N operating framework for enterprise architecture and governed AI.
One Agent with Routes vs Specialized Agents: When to Split
Enterprise agent architecture: when a shared runtime with governed routes is enough, and when different execution boundaries require specialized agents.
Agent Identity: Who Is Asking vs Which Software Is Acting
User identity answers who is asking; agent identity answers which software is acting. Least privilege, audit, revoke, and dual gateway checks for real-time and batch.
LLM Hosting Options for Regulated Industries
From public SaaS to air-gapped on-prem: a practical ladder of LLM hosting options, and how to choose based on regulatory pressure and engineering maturity.
Is MCP Really Necessary for Business Agents in Large Regulated Enterprises?
MCP is a valuable standard for AI tool interoperability. For regulated business agents, pin agent governance first (route contract, tools, permissions, context), then choose MCP only where it beats governed business APIs.
How to Design an Intent Router for Agentic AI
A practical design guide for intent routing — route tables, layered classification, confidence thresholds, session stickiness, eval gates, and wiring dispatch into the agentic app before the model loop runs.
What Is an Intent Router — and Why It Matters in Agentic AI
An intent router is the first deterministic decision in an agent stack. It maps user requests to the right workflow, agent, and tool manifest before the model loop runs — and when it fails, every downstream stage can execute perfectly and still miss the user’s goal.
AI Observability In Enterprise
AI observability is not a dashboard. It is a capture-and-retention architecture with five signals, five retention policies, and four consumers.