The safety layer: test suites, guardrails, tracing and cost and speed monitoring that keep agents reliable.
Discipline: AI Agents
Working term: Scored quality you can track
Formats: ongoing monthly work or a single audit
Who you talk to: the senior lead, on every weekly call
The service
Our approach to Agent Evals & Monitoring
An agent you cannot measure is an agent you cannot trust. We build test suites based on real failure cases, guardrails on inputs and outputs, checks for personal information and jailbreak attempts, full request tracing and dashboards for cost, speed and quality. Regression checks stop a prompt edit from quietly breaking behaviour, and personal data handling is designed with PIPEDA in mind.
Before work starts, we agree on the number that matters most to you. Each sprint is planned against it. You get a short recorded update every week and a dashboard you can open any time, and the senior lead joins every call.
A senior lead runs your account from day one
A recorded walkthrough each week and a shared reporting dashboard
We work inside the tools and CMS your team already uses
Target ranges written into the agreement before we begin
Our Process
Four stages of Agent Evals & Monitoring
The same order every time: find the constraint, plan, deliver, then review. Here is what each stage involves.
01
Write down what good looks like
Task-based test suites turn "is the agent doing well" into scores you can track.
02
Spot regressions early
Production monitoring flags drops in quality before customers notice them.
03
Check safety constantly
Ongoing checks catch answers that are unsafe or outside what the agent should handle.
04
Compare each release
Every new version is scored against the last, so improvements are measured.
Most teams cannot say how good their AI agent is. We build evaluation sets from real tasks, agree on what a correct and useful answer looks like and score the agent against them. Each new version is compared with the last, so improvements and setbacks are visible.
Measured quality is the base for every dependable AI system.
Evaluation sets built from real tasks
Agreed quality criteria and scores
Comparison between versions
Quality you can track and improve
02
How progress tends to build
Commercial terms usually move later than technical fixes
Catch drift & regressions
Find problems before customers do
Agents can get worse quietly as models, data and usage change, and without monitoring the first warning is an annoyed customer. We track quality, cost and behaviour in production and alert your team when something shifts.
Ongoing monitoring keeps a live agent reliable.
Quality and behaviour tracking in production
Drift and regression alerts
Cost and performance dashboards
Warnings before users are affected
03
Content velocity
Briefed, drafted, edited, shipped
Safety & guardrail checks
Watch continuously for unsafe output
A live agent can produce harmful, off-topic or non-compliant replies. We set up checks that watch for policy breaches, unsafe content and failures on unusual inputs, log incidents and route them for review. Logs are designed with your privacy obligations in mind, so monitoring does not create a new store of personal data.
This is what lets you keep an agent in production responsibly.
Continuous safety and policy checks
Detection of guardrail and edge-case failures
Incident logging and review
Privacy-aware logging
Our working terms
What a Agent Evals & Monitoring engagement includes
These describe how we staff, report on and scope the work. They hold from the first week, before any result exists to point at.
A named senior specialist leads each account
Progress shown in live Looker Studio dashboards
Measured on leads and revenue
Scored
Quality you can track
Early
Regressions caught
Ongoing
Safety checks
Measured
Release over release
FAQs
Before you sign: common questions
Straight answers on scope, timing and how we report.
Why do AI agents need evaluation and monitoring?
Without them you cannot tell whether the agent is getting better or worse. Evaluation defines and measures quality, and monitoring catches regressions, cost spikes and unsafe behaviour in production.
What are evals?
Sets of real tasks with agreed criteria for a good answer. Running the agent through them gives a score you can compare across versions.
How do you detect quality drift?
By tracking quality, cost and behaviour in production and alerting when results move outside expected ranges.
Can you add this to agents we already run?
Yes. We can wrap evaluation and monitoring around existing agents and AI features, whoever built them.
Tell us about the business and a senior specialist will get back to you within one business day. If starting with AI Agents looks like the wrong first step, we will say so and suggest what to fix first.
