AI agentsGuide for managers

AI agent evals: how to know your agent works before production

What an eval is, the 3 levels of evaluation (tools, trajectory, outcome), the metrics that matter and how to start with 50 real cases.

In this article

One change to the agent, with and without evals

Today · tested by eye

  1. 1ChangeSomeone tweaks the agent
  2. 23 manual tries"Looks fine"
  3. 3Goes liveNo threshold
  4. 4ComplaintThe customer finds the error

With evals · example 5 minutes per run

  1. 1ChangeSomeone tweaks the agent
  2. 250 cases, automaticAnswer vs. the correct one
  3. 3ThresholdUnder 95 %: it does not ship
  4. 4New failureAdded as a case
Illustrative example. The change can be the instructions, the model or a new tool.

What gets tested: 3 levels

LevelWhat it checksBank exampleRed flag
1 · ToolsEach lookup into your systems on its ownBalance lookup with a mistyped account numberReturns another account's data
2 · StepsThe order in which the agent uses its toolsAsks for confirmation before blocking a cardActs without confirming
3 · AnswerWhat the customer reads, against a written rubricSME loan requirements under the current policyQuotes an expired policy

1 · Tools

What it checks
Each lookup into your systems on its own
Bank example
Balance lookup with a mistyped account number
Red flag
Returns another account's data

2 · Steps

What it checks
The order in which the agent uses its tools
Bank example
Asks for confirmation before blocking a card
Red flag
Acts without confirming

3 · Answer

What it checks
What the customer reads, against a written rubric
Bank example
SME loan requirements under the current policy
Red flag
Quotes an expired policy
Start with level 3 (what the customer sees) and drop to 1 and 2 when something fails.

The 4 numbers that decide if it ships

MetricWhat it measuresThreshold to ship (example)
Success rate% of cases solved per the rubric95 % or more
Handoff to a human% of cases the agent passes to a personUnder 20 %, and always for sensitive cases
Cost per solved caseModel + infrastructure ÷ solved casesLower than the person's cost
Response timeSeconds to the final answerUnder 10 s in chat

Success rate

What it measures
% of cases solved per the rubric
Threshold to ship (example)
95 % or more

Handoff to a human

What it measures
% of cases the agent passes to a person
Threshold to ship (example)
Under 20 %, and always for sensitive cases

Cost per solved case

What it measures
Model + infrastructure ÷ solved cases
Threshold to ship (example)
Lower than the person's cost

Response time

What it measures
Seconds to the final answer
Threshold to ship (example)
Under 10 s in chat
Set thresholds with the business owner before the pilot, not after.

What it costs and when it pays back

What evals cost and when they pay back

1 · What errors cost today

Country (hourly cost base)

2 · What evals cost

Model that runs the tests
Total cost of evalsUSD 40.47 per month
  • Model (tests): gpt-6-lunaUSD 0.38 · 0.9 %
  • TeamUSD 40.09 · 99 %
  • ToolUSD 0.00 · 0.0 %
Savings from errors that never reach the customerUSD 301 per month90 errors · 45.0 h of your team
Net savingsUSD 260 per month
Months to pay back the setup9.6With the example range (USD 1,500 to 4,000): 5.8 to 15.4 months

Pays back in 9.6 months. Start with 50 real cases.

estimated

Model prices: OpenAI API pricing, checked 11 Oct 2026 (USD per million tokens, input / output): gpt-6-luna 0.10 / 0.50; gpt-6.1-sol 2.00 / 10.00. Each case = one agent answer (4,000 in + 500 out) + one grading call against the written rubric (2,000 + 200).

Hourly cost: Paraguay, minimum Gs. 12,683 per hour (MTESS, Res. 670/2026) at Gs. 5,694.47 per USD (BCP, 9 Oct 2026) = USD 2.23. Ecuador: basic wage USD 482 ÷ 173.33 h (Ministry of Labor) = USD 2.78. The multiplier is an example for a skilled profile.

Net savings = hours not lost to errors × hourly cost − total cost. Months = setup ÷ net savings. Excludes lost customers and fines, so the real value is usually higher. Error %, % stopped, minutes, tool and setup are examples.

Customer data in tests

Use real cases without personal data: replace names, ID numbers and accounts before saving them. In Paraguay, Law 7593/2025 requires it (in force from 27/11/2027, art. 57); in Ecuador, the LOPDP. In banks, keep the report of every run: it is your evidence for the BCP's SIB or Ecuador's Superintendencia de Bancos. Country cases: Paraguay · Ecuador.

Your first 50 test cases

Example evals rollout

  1. Week 1CasesReal history, past errors and cases to refuse.KPI: 50 cases with approved answers
  2. Week 2Rubric and thresholdWhat counts as a correct answer and when it does not ship.KPI: threshold signed off by the business
  3. Week 3Automatic runThe agent does not ship below the threshold.KPI: report on every change
  4. Week 4Loop with productionEvery new complaint joins the exam.KPI: real failures turned into cases

Is your agent already talking to customers without an exam? In the free diagnostic we review your cases and tell you where to start.

Frequently asked questions

What is an eval in AI agents?

A versioned set of test cases with real inputs and success criteria that measures whether the agent does the job well, before and after every change.

How many cases does a useful eval need?

About 50 real business cases to start, including frequent failures and edge cases. It grows with every failure found in production.

How often should evals run?

On every prompt, model or tool change, ideally automatically in the deployment pipeline, just like software tests.

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