Observe, evaluate & improve your agents
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.
What is Agent Observability?
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 →What is Agent Monitoring?
Watching AI agents in production: what to track, how it differs from traditional monitoring, and how it feeds the evaluation loop.
Read guide →What is Token Usage?
The main driver of AI agent cost: what it is, why agents amplify it, and how to track and control it.
Read guide →What is Agent Analytics?
How to measure whether your AI agents complete their tasks, what quality their outputs reach, and what they cost, in one view.
Read guide →What is Agent Cost Attribution?
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.
What is Agent Evaluation?
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 is LLM Evaluation?
How language model quality is measured, with benchmarks, metrics, judges and human review, and why a state-of-the-art model can still be a broken agent.
Read guide →Agent Evals: A Practical 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 →What is LLM-as-a-Judge?
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 →What is an Agent Evaluation Framework?
The components of a system for evaluating AI agents: datasets, graders, metrics, and the harness that ties them together.
Read guide →AI Evaluation Tools: How to Choose
What AI evaluation tools do, the categories that exist, and how to pick one for evaluating agents, not just model outputs.
Read guide →What is RAG Evaluation?
Measuring whether a retrieval-augmented system fetches the right context and generates faithful, relevant answers.
Read guide →Golden Datasets for AI Agents
The curated set of real cases with known-good answers that every agent eval suite is built on.
Read guide →What is an Agent Quality Score?
The single, trackable number that tells you whether an AI agent is doing its job well, rolled up from its evals.
Read guide →AI Agent Benchmarks: How Agents Are Measured and Compared
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 →AI Agent Hallucinations and Guardrails
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 →How Do You Evaluate a Voice Agent?
What changes when the agent talks: transcription accuracy, latency, turn-taking and tone, and how to measure them on real calls.
Read guide →How Do You Evaluate a Coding Agent?
Outcome-based scoring for agents that write code: did the tests pass, how reliably, and at what cost, on benchmarks and on your own repo.
Read guide →Turn what you measure into a better agent — then prove the change worked.
What is Agent Optimization?
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 →Prompt Optimization for AI Agents
The cheapest lever in the optimization loop: systematically improving an agent's prompts and proving it with evals.
Read guide →Human-in-the-Loop for AI Agents
Designing agents so a person reviews, approves or corrects the steps that matter: a safety control and an improvement engine.
Read guide →What is DSPy?
The framework that treats prompting as a programming and optimization problem instead of hand-written strings.
Read guide →What is Eval-Driven Development?
Test-driven development for agents: write the eval before the fix, ship only when it passes.
Read guide →Fine-Tuning vs Prompting for AI Agents
Two ways to change an agent's behaviour, and a simple rule for which to reach for first.
Read guide →Prompt Management and Versioning
Treating an agent's prompts as versioned, tested, reversible assets: the ops discipline that makes prompt optimization safe.
Read guide →What Are Self-Improving AI Agents?
Two very different meanings, and the one that actually ships in production.
Read guide →The Agent Quality Loop
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.
What is AI Agent Governance?
A complete guide to governing autonomous AI agents in production, from policy design to runtime enforcement.
Read guide →What is an Agentic Control Plane?
The infrastructure layer that gives enterprises runtime visibility and control over every AI agent in production.
Read guide →What is Agent Identity Management?
How enterprises assign, track, and govern unique identities for AI agents: the foundation of agent security and accountability.
Read guide →What is AI Agent Security?
The threats, attack surfaces, and defences that matter when autonomous AI agents operate in production environments.
Read guide →What is Runtime Governance for AI Agents?
How to enforce policies and controls at the agent execution layer, where autonomous agents make decisions and take actions.
Read guide →What is the Difference Between AI Security and AI Agent Governance?
Why enterprises need both security and governance, and how to evaluate which to prioritise.
Read guide →What is Runtime Enforcement for AI Agents?
The mechanism that intercepts, evaluates, and controls every AI agent action at the moment it happens, before it takes effect.
Read guide →What is an Agent Registry?
The enterprise inventory that catalogues every AI agent: who owns it, what it can do, and whether it is governed.
Read guide →What is PII Detection for AI Agents?
How to detect, classify, and control personal data flowing through AI agent interactions, at runtime, before exposure occurs.
Read guide →The platform landscape, compared criterion by criterion.
Best Agentic Coding Tools (2026)
A criteria-based guide to the tools that plan, write, and verify code as agents rather than autocomplete. Verified against primary sources on 19 July 2026; refreshed monthly.
Compare tools →Best Agent Observability Tools (2026)
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 →Best Agent Evaluation Tools (2026)
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 →Working documents to take into your next review.
AI Agent Security Checklist
12 controls to verify before deploying AI agents to production.
Open checklist →Enterprise AI Governance Framework
A structured approach to governing AI agents across your organisation.
Open checklist →Agent Deployment Readiness Assessment
15 questions to answer before your AI agent goes live.
Open checklist →How teams put the loop to work on real agents.
Governing Multi-Agent Workflows
How to maintain control, visibility, and compliance when agents orchestrate other agents.
Read use case →Securing MCP Tool Access for AI Agents
How to govern which tools agents can use, with what data, and under what conditions.
Read use case →Automating Agent Compliance Reporting
How to generate audit-ready compliance evidence from agent runtime data without manual effort.
Read use case →Preventing Shadow AI Agents in the Enterprise
How to detect, inventory, and govern AI agents deployed outside sanctioned channels.
Read use case →Implementing Agent-Level Cost Attribution
How to track, allocate, and control AI agent costs across teams, projects, and business units.
Read use case →Managing Agent Lifecycle from Development to Retirement
How to govern agents through every phase: registration, testing, deployment, monitoring, and decommissioning.
Read use case →Enforcing Human-in-the-Loop Controls for AI Agents
How to require human approval for high-stakes agent actions without creating operational bottlenecks.
Read use case →Governing AI Agents Across Hybrid Cloud Environments
How to maintain consistent governance when agents run across on-premise, cloud, and edge infrastructure.
Read use case →Real-Time PII Detection in AI Agent Workflows
How to detect and protect sensitive data in agent interactions before it reaches external APIs or logs.
Read use case →Building and Maintaining an Enterprise Agent Registry
How to create a single source of truth for every AI agent in your organization.
Read use case →Designing Approval Workflows for High-Stakes Agent Actions
How to route risky agent decisions for human review without creating bottlenecks.
Read use case →The numbers behind agent adoption, governance, and risk — all sources cited.
AI Agent Adoption Statistics 2026
Enterprise adoption rates, market size, and business impact, sourced from Gartner, McKinsey, PwC, and Deloitte.
View statistics →AI Governance & Compliance Statistics 2026
Market size, governance maturity, and regulatory readiness, sourced from Gartner, Deloitte, IBM, and industry surveys.
View statistics →AI Security & Risk Statistics 2026
Breach costs, shadow AI, and attack vectors, sourced from IBM, Gartner, and security researchers.
View statistics →