AI Agent Knowledge Library

Operate

Everything required to move AI agents from prototypes into secure, observable, reliable, scalable production systems.

31 Published resources

Explore: Cost & Performance Deployment Evaluation Observability Reliability Security & Safety

Knowledge library

More resources — Page 3

31 published articles

Explainer Intermediate

Reliable AI Agent Architecture

A practical, production-oriented explanation of reliable AI agent architecture, with examples, boundaries, trade-offs, and failure handling patterns.

Explainer Beginner

Human-in-the-Loop for AI Agents

A practical, production-oriented explanation of human-in-the-loop control, with examples, boundaries, trade-offs, and failure handling patterns.

Explainer Intermediate

Sandboxing AI Agents

A practical, production-oriented explanation of sandboxed agent execution, with examples, boundaries, trade-offs, and failure handling patterns.

Explainer Intermediate

Tool Permissions and Least Privilege for AI Agents

A practical, production-oriented explanation of least-privilege tool permissions, with examples, boundaries, trade-offs, and failure handling patterns.

Explainer Intermediate

Prompt Injection in AI Agents

A practical, production-oriented explanation of prompt injection in tool-using agents, with examples, boundaries, trade-offs, and failure handling patterns.

Explainer Beginner

AI Agent Security Explained

A practical, production-oriented explanation of the security model of an AI agent, with examples, boundaries, trade-offs, and failure handling patterns.

AI agent observability stack where user and agent interactions with model, retrieval, tools, and other agents produce logs, traces, metrics, and events for an observability platform.

Explainer Intermediate

Observability for AI Agents

A production observability model for agent, model, retrieval, tool, sub-agent, and infrastructure signals—with privacy and redaction controls.

Side-by-side comparison of LLM evaluation from input to model output and agent evaluation from goal through model, tool, observation, state, action, and outcome.

Comparison Intermediate

LLM Evaluation vs Agent Evaluation

LLM evaluation scores model outputs; agent evaluation measures the whole goal-directed system, including tools, state, constraints, reliability, latency, and cost.

Agent evaluation pipeline from tasks and agent runs through traces and outcomes, evaluators, scores, failure analysis, and improvement.

Tutorial Intermediate

How to Evaluate an AI Agent

A practical workflow for defining agent success, building evaluation datasets, capturing traces, scoring behavior, analyzing failures, and preventing regressions.

AI agent evaluation stack showing goal, outcome, trajectory, tool calls, constraints, and reliability, cost, and latency.

Explainer Beginner

AI Agent Evaluation Explained

Agent evaluation measures task outcomes, trajectories, tool behavior, constraints, safety, reliability, latency, and cost—not only final prose.