Architecture decisions, implementation guides and field notes from building AI systems that have to work outside the demo.
A practical framework for governing AI agents with autonomy levels, scoped permissions, human approval gates, audit trails, runtime policies and recovery controls.
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A practical guide to how stateful AI agents persist context, memory, checkpoints and workflow state across sessions, failures, approvals and long-running tasks.
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A practical, vendor-neutral guide to LLM gateways: unified APIs, model routing, failover, cost controls, caching, observability, guardrails, virtual keys and build-vs-buy decisions.
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See 10 practical MCP server use cases for connecting AI agents to CRM, ERP, databases, support systems, knowledge bases and controlled business actions.
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An on-premise AI chatbot can keep model inference, retrieval and company data inside infrastructure you control—but only if identity, permissions, integrations, security and operations are designed around the…
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How to size a private AI server for business by model, memory, context and users, with 2026 hardware cost tiers, runtime choices, security, TCO and cloud alternatives.
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An LLM evaluation framework is the system used to determine whether a change to an AI application is safe to release. It combines versioned test datasets, deterministic and…
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Model Context Protocol (MCP) is often described as a client-server protocol, but that description leaves out one of the most important parts of its architecture: the host. Understanding…
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A modern private AI architecture can run on-premises, inside a private cloud or VPC, or across a hybrid environment that keeps sensitive workloads private while selectively using external…
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AI coding agents have moved well beyond autocomplete. Cursor, Claude Code, Codex and open-source tools such as OpenCode can now inspect repositories, edit multiple files, execute commands, run…
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AI agent observability is the ability to reconstruct what an AI agent did, why it did it, what resources it consumed and whether the final outcome was actually…
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Enterprise process automation gets complicated when a workflow crosses modern SaaS applications, internal databases, email, documents and a legacy ERP that was never designed to integrate with any…
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