Agent Evaluation
Practical selections and guides for evaluating AI-agent tools, testing their behavior, and considering safety and governance.
Topics
Browse focused coverage of agent evaluation, observability, testing tools, safety, governance, and audit methods.
Articles
Find a selected public article offering a useful perspective on evaluating or auditing AI agents.
Safety & Governance
Build an Excel register for AI-agent risks by mapping each entry to NIST AI RMF functions, assigning an owner, recording evidence, and tracking a response and review date.
Articles
Explore the latest public selections and how-to guides for assessing agent tools and improving audit practice.
Safety & Governance
Configure an approval workflow so an agent must receive human sign-off before executing writes, deletions, or configuration changes in production systems.
Testing Tools
Record real agent-LLM exchanges, replay them offline, and assert correct tool selection with a repeatable Pytest suite.
Buying Guides
A practical guide to testing agent safety tools against prompt injection attacks. Learn to craft adversarial prompts, measure detection accuracy, assess false positive rates, and evaluate latency impact to make an informed purchasing decision.
Observability
Add span attributes and error boundaries to agent workflows so failures in tool calls or sub-agents surface with enough context to debug quickly.
Audit Methods
Compare tools and policy workflows for examining cloud permissions assigned to AI-agent service identities, with attention to provider coverage, audit setup, and useful evidence for reviewers.
Observability
Compare tracing platforms that capture agent spans, tool-call inputs/outputs, token usage, and errors across multi-step runs.
AI Agent Auditor
Learn about the publication's focus on tools, evaluation approaches, observability, testing, and responsible AI-agent oversight.
FAQ
Get concise explanations of common evaluation, testing, and governance questions.
An audit can examine an agent's behavior, reliability, observability, safety controls, and fit for its intended use. The scope depends on the system and the risks being considered.
Start with the behaviors and risks you need to assess, then compare tools by their evaluation methods, observability, integrations, and limitations. Check current documentation to confirm a tool fits your environment.
Agent observability is the ability to inspect relevant activity and outcomes, such as the steps an agent takes and how it uses tools. It can help teams investigate behavior and identify issues.
Consider whether responsibilities, oversight, and risk controls are clear, and how the system's behavior can be reviewed. The right questions depend on the agent's use and potential impact.
Define the intended tasks and the outcomes that would count as success or failure, then choose methods suited to those criteria. Include relevant edge cases and risks in the evaluation plan.