Insights on declarative agent architecture, orchestration patterns, and building production AI agents.
A practical guide to autonomous AI agents - what separates them from chatbots and copilots, the anatomy of an agent that works on its own (computer, memory, credentials, triggers), and how to tell real autonomy from the label.
Read moreAI agents and AI employees are not the same thing. Learn the real distinction - role, persistent identity, memory, and accountable output - a side-by-side comparison, when to use each, and how the employee model works in practice.
Read moreHow computer-use agents work: the observe-reason-act loop, why a real browser beats API-only agents, and the two things that make it production-grade - persistent state that stays logged in and residential networking that signs in without being blocked.
Read moreWhy declarative agent architecture produces better AI agents with less code. Compare the declarative approach to imperative graph-based frameworks and learn how to build agents that are model-agnostic, observable, and production-ready by default.
Read moreA practical guide to AI agent orchestration - what it is, why it matters for production agents, how the major approaches compare (graph-based, role-based, provider-specific, declarative), and what to look for when choosing a solution.
Read moreThe infrastructure decisions that separate production AI agents from prototypes. Covers session management, tool execution, model selection, streaming, and observability - the problems every team hits when deploying agents to real users.
Read more