Agents are the successor to the chatbots of the last decade. The difference in 2027: agents act — they call tools, spend money and touch customers — so the writing here is about tools, memory, guardrails, evals and operations, not demos.
What LLMOps actually means in 2027 — prompts, evals, tracing, gateways and agent operations — and how it grew out of MLOps. The updated edition of Engatica's LLMops post.
What LLM agents are, how they work — tools, memory, planning, guardrails — and a build-vs-buy framework for teams shipping their first production agent.
How leading support teams deploy AI agents in 2027 — scope, grounding, handoff, metrics that matter, and why 'ai customer support' is the highest-value term in conversational AI.
An AI agent is a system in which a large language model directs its own process: it plans steps, calls tools, checks results and decides what to do next, inside guardrails a team sets. That is what separates an agent from a workflow, where the steps are predefined in code, and from a chatbot, which only talks.
The practical guide to building LLM agents, the operating guide for AI customer support agents, field notes from retail, and the AI Agents Power List — the builders the agent era is being built on.
With one narrow, high-volume, verifiable and reversible task — drafting support resolutions for human approval is the classic. Measure task completion and correction rates weekly, and graduate autonomy only as the evaluation numbers earn it. The practical guide walks through the pattern end to end.