Agentic AI
What Is AgentOps? Operating AI Agents in Production
AgentOps is the practice of running agents in production: observability, evaluation, guardrails, cost governance, and human oversight.
6 min read
7 articles
AgentOps is the practice of running agents in production: observability, evaluation, guardrails, cost governance, and human oversight.
Git worktrees let you check out several branches into separate folders at once. They went from obscure to default because running coding agents in parallel needs isolated workspaces.
Anthropic's Claude Tag drops a shared, persistent agent into Slack channels. The interesting part is not the model, it is the move from one-off chats to a teammate that remembers.
How to choose how to structure an agent: the plain while loop, the explicit state machine, and the full graph. What each gives you, what each costs, and when to move up.
Subagents are the main lever for stopping a long task from drowning in its own context. Here is how DeepAgents implements them in TypeScript, and the mistakes that make them useless.
Deep Agents bakes planning, context management, subagents, and human approval into the agent loop. Here is why it exists, every main feature, and a production-grade customer support agent built with it.
Agents, explained without the hype: what they are, when one is enough, when you need several, and how to build a working multi-agent system with LangChain.