AI Agents
Agents, tool use and protocols such as MCP: how to run them safely in production.
AI Agents
Agent observability: what to trace in every AI agent run
Trace each AI agent run as one tree of spans: every model call, tool call and approval wait, with inputs, results, errors, tokens, cost and who it acted for.
AI AgentsAI agents in production: what breaks and how to fix it
AI agents break in production through failing tools, loops, runaway cost and loose permissions. Limits, traces, approvals and evals keep them safe.
AI AgentsHuman-in-the-loop AI: when an agent should ask before it acts
An AI agent should ask a person before any action that is hard to undo, moves money, contacts outsiders or goes beyond its usual scope, and run the rest itself.
AI AgentsMCP explained: how Model Context Protocol connects AI to systems
MCP is an open standard that lets AI apps call tools and read data from your systems through one protocol, so long as you control permissions and inputs.