In-depth guides on measuring what your AI agents do in production, scoring their quality, and making them better — plus the governance and security to run them safely.
See what your agents actually do in production — every step, tool call, token and cost.
How to see what your AI agents are actually doing in production — from tool calls and token usage to groundedness, policy compliance, and cost.
Read guide →Watching AI agents in production — what to track, how it differs from traditional monitoring, and how it feeds the evaluation loop.
Read guide →The main driver of AI agent cost — what it is, why agents amplify it, and how to track and control it.
Read guide →How to measure whether your AI agents complete their tasks, what quality their outputs reach, and what they cost — in one view.
Read guide →How to track, allocate, and control AI agent costs at the agent, team, and task level — before they become budget surprises.
Read guide →Score whether agent output is actually good — offline and on live production traffic.
The shift from evaluating models at dev time to evaluating agents in production — what it means, what it measures, and why model benchmarks don't tell you if your agent works.
Read guide →What evals are, the four types that matter for agents, and how to ship your first eval this week — from vibes to verdicts.
Read guide →How one model scores another — the scalable backbone of modern agent evaluation, from judge prompts and bias controls to agent-as-a-judge.
Read guide →The components of a system for evaluating AI agents — datasets, graders, metrics, and the harness that ties them together.
Read guide →What AI evaluation tools do, the categories that exist, and how to pick one for evaluating agents — not just model outputs.
Read guide →Measuring whether a retrieval-augmented system fetches the right context and generates faithful, relevant answers.
Read guide →The curated set of real cases with known-good answers that every agent eval suite is built on.
Read guide →The single, trackable number that tells you whether an AI agent is doing its job well — rolled up from its evals.
Read guide →What agent benchmarks are, the ones that matter (tau-bench, SWE-bench, GAIA and more), and why a leaderboard score is not the same as production readiness.
Read guide →Why AI agents make things up, how to detect it, and the guardrails that stop a hallucinated answer from becoming a harmful action.
Read guide →Turn what you measure into a better agent — then prove the change worked.
Closing the loop — using what observability and evaluation tell you to actually make the agent better, then proving it with the next eval.
Read guide →The cheapest lever in the optimization loop — systematically improving an agent's prompts and proving it with evals.
Read guide →Designing agents so a person reviews, approves or corrects the steps that matter — a safety control and an improvement engine.
Read guide →The framework that treats prompting as a programming and optimization problem instead of hand-written strings.
Read guide →Test-driven development for agents — write the eval before the fix, ship only when it passes.
Read guide →Two ways to change an agent's behaviour — and a simple rule for which to reach for first.
Read guide →Treating an agent's prompts as versioned, tested, reversible assets — the ops discipline that makes prompt optimization safe.
Read guide →Two very different meanings — and the one that actually ships in production.
Read guide →The continuous cycle that keeps an AI agent reliable in production — and how the three pillars fit together.
Read guide →The wider discipline agents inherit — and where AgentOps goes beyond it.
Identity, policy, and runtime control for agents operating in regulated environments.
A complete guide to governing autonomous AI agents in production — from policy design to runtime enforcement.
Read guide →The infrastructure layer that gives enterprises runtime visibility and control over every AI agent in production.
Read guide →How enterprises assign, track, and govern unique identities for AI agents — the foundation of agent security and accountability.
Read guide →The threats, attack surfaces, and defences that matter when autonomous AI agents operate in production environments.
Read guide →How to enforce policies and controls at the agent execution layer — where autonomous agents make decisions and take actions.
Read guide →Why enterprises need both security and governance — and how to evaluate which to prioritise.
Read guide →The mechanism that intercepts, evaluates, and controls every AI agent action at the moment it happens — before it takes effect.
Read guide →The enterprise inventory that catalogues every AI agent — who owns it, what it can do, and whether it is governed.
Read guide →How to detect, classify, and control personal data flowing through AI agent interactions — at runtime, before exposure occurs.
Read guide →A vendor-led, criteria-based guide to the serious agent observability tools — maintained by Prefactor and refreshed monthly, with a candid view of where Prefactor leads and where others are the better fit.
Compare tools →A vendor-led, criteria-based guide to the tools for evaluating AI agents — offline and in production — maintained by Prefactor and refreshed monthly, candid about where Prefactor leads and where others fit.
Compare tools →12 controls to verify before deploying AI agents to production.
Open checklist →A structured approach to governing AI agents across your organisation.
Open checklist →15 questions to answer before your AI agent goes live.
Open checklist →How to maintain control, visibility, and compliance when agents orchestrate other agents.
Read use case →How to govern which tools agents can use, with what data, and under what conditions.
Read use case →How to generate audit-ready compliance evidence from agent runtime data without manual effort.
Read use case →How to detect, inventory, and govern AI agents deployed outside sanctioned channels.
Read use case →How to track, allocate, and control AI agent costs across teams, projects, and business units.
Read use case →How to govern agents through every phase — registration, testing, deployment, monitoring, and decommissioning.
Read use case →How to require human approval for high-stakes agent actions without creating operational bottlenecks.
Read use case →How to maintain consistent governance when agents run across on-premise, cloud, and edge infrastructure.
Read use case →How to detect and protect sensitive data in agent interactions before it reaches external APIs or logs.
Read use case →How to create a single source of truth for every AI agent in your organization.
Read use case →How to route risky agent decisions for human review without creating bottlenecks.
Read use case →Enterprise adoption rates, market size, and business impact — sourced from Gartner, McKinsey, PwC, and Deloitte.
View statistics →Market size, governance maturity, and regulatory readiness — sourced from Gartner, Deloitte, IBM, and industry surveys.
View statistics →Breach costs, shadow AI, and attack vectors — sourced from IBM, Gartner, and security researchers.
View statistics →Prefactor helps teams observe, evaluate, and improve their AI agents in production — across every framework and provider.