
AI Agent Evaluation and Observability: How to Know an Agent Is Working
Evaluate AI agents using task outcomes, tool traces, safety tests, cost and production monitoring instead of anecdotal demos.
Practical guidance for buyers, operators and product teams. 17 articles in this topic.

Evaluate AI agents using task outcomes, tool traces, safety tests, cost and production monitoring instead of anecdotal demos.

Design identities, permissions, approvals and audit trails for AI agents that access business systems or act for employees.

Compare agent orchestration with deterministic workflow automation by uncertainty, control, integration, cost and operational risk.

Design AI model routing around task complexity, confidence, latency, privacy, cost and deterministic fallbacks.

Assess computer-use agents that operate websites and desktop interfaces, including reliability, security, approvals and better integration alternatives.

Understand where MCP fits in business AI integrations, how it differs from ordinary APIs and which security and governance controls still matter.

Plan multimodal search across documents, images, audio and video using governed ingestion, metadata, permissions and retrieval evaluation.

Compare on-device and cloud AI by privacy, latency, capability, hardware, model lifecycle and total operating cost.

Reduce prompt-injection risk in business AI assistants with trust boundaries, least-privilege tools, structured data, output validation and testing.

Build a retrieval-augmented generation knowledge assistant with governed sources, tested retrieval, citations, access controls and human escalation.

Implement AI customer service with verified knowledge, deterministic actions, human escalation, evaluation, security and production monitoring.

Design AI document processing with controlled ingestion, extraction, validation, human review, audit evidence and reliable system integration.

Compare rules, RPA and AI workflow automation by input type, predictability, risk, integration and the human controls each process needs.

Design human review around business risk, authority and exception handling so AI assists work without hiding consequential decisions.

Prepare documents, records and knowledge for AI by defining ownership, quality, permissions, evaluation sets and a controlled update process.

A practical priority list for choosing the first AI-agent workflow that will save time without creating new risk.

A practical way to decide whether an AI chatbot solves a real business problem, and which type to build.
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