96% looks better than 92%.
Two agents. The same 1,000 tasks. Agent A completes more of them successfully. A leaderboard would put it first.
So far, so simple.
Independent agent evaluation
Seeking design
partners
Know which agent
to deploy.
And where to
keep a human.
We compare AI agents on your real workflows, measure the cost and consequences of failure, and recommend what to automate, review, or keep manual.
You get: a comparative evaluation and a
deployment recommendation, with the supporting evidence and
limitations.
-
Compare on your workflow.
The same support tickets, rules, and constraints.
- Agent A
- Agent B
- Agent C
-
Measure what matters.
- Quality
- Task outcomes
- Cost
- Including review
- Risk
- Failure impact
-
Define the deployment plan.
- Routine routing
- Automate
- Ambiguous requests
- Human review
- High-impact actions
- Keep manual
Support triage example only. Actual recommendations depend on your evaluation.
For teams with an agent deployment decision to make.
A closer lookA score tells you how it performed. Context tells you if it belongs.
Your workflowYour constraintsYour decision
The winner changes
when the stakes do.
A polished demo can hide an expensive mistake. Follow a simple example of what changes when you look beyond task completion.
Not all failures cost the same.
Agent A makes 20 critical errors. Agent B makes 2. A wrong action isn’t the same as a request for clarification. Your evaluation shouldn’t treat them equally.
Averages don’t show the whole picture.Choose the agent. Define the limits.
The answer isn’t “B is always better.” It’s which agent fits the task, what needs human review, and where the evidence is still too thin.
That’s a deployment decision.
02 / Put your assumptions on the table
What does a wrong
What does a wrong
decision really cost?
Move the slider. See how the preferred agent changes when the consequences do.
per critical error
Same synthetic 1,000-task example. Routine failure: €10 each. Execution: A €200 / B €400. Only the critical-error cost changes.
Modeled cost / 1,000 attempted tasks
Agent A€5,40096% task completion
Agent B€1,68092% task completion
Agent B has €3,720 lower modeled cost per 1,000 tasks under these assumptions.
Illustrative sensitivity analysis, not an evaluation or deployment recommendation. This simplified model excludes the value of successful outcomes, review cost, latency, and uncertainty. A real decision model must account for your workflow and constraints.
Inspect the calculation
A = €200 + (20 routine failures × €10) + (20 critical errors × your estimate). B = €400 + (78 routine failures × €10) + (2 critical errors × your estimate). Outcomes are mutually exclusive. Break-even is approximately €43.33 per critical error. These are invented counts for explanation, not estimated production rates.
Your real work.
A common test.
Start with one workflow and a decision worth getting right. A focused evaluation, designed with your team—not a universal score.
Define what matters.
Agree on outcomes, failure costs, constraints, and what must always go to a human. Write the decision criteria before the test.
Test the work. And the edge cases.
Compare candidates on representative scenarios, missing information, and consequential failures. Keep tools and budgets comparable.
Verify, don’t just judge.
Use known outcomes, deterministic checks, and domain-expert review where appropriate. Trace findings back to the underlying cases.
Make the trade-offs explicit.
Compare quality, total cost, risk, and oversight. Document uncertainty and recommend where to act, review, abstain, or test further.
When models, prompts, or tools change, the decision deserves fresh evidence.
04 / The deliverable
Less “here’s a score.”
Less “here’s a score.”
More “here’s why.”
A scoped pilot is designed to give engineering, workflow owners, and decision-makers a shared basis for choosing what comes next.
Discuss your evaluationYour next deployment.
An evidence trail.
One workflow. Comparable candidates. Explicit assumptions.
- Comparative agent scorecard 01
- Critical failures & boundary cases 02
- Cost, review burden & trade-offs 03
- Uncertainty & known limitations 04
- Recommended deployment conditions 05
Traceable by designIllustrative outline
The principles behind the work
Independent by intent.
The aim is a decision you can defend, not a favorable rating. Evaluation assumptions and commercial relationships should be explicit.
Uncertainty included.
Sample sizes, confidence intervals, and coverage gaps belong in the result. “We don’t know yet” is a useful finding.
Humans in the model.
Autonomy isn’t all or nothing. Review, escalation, and abstention are part of a deployment policy—not afterthoughts.
Start with a decision, not a subscription. Early-stage
project
Two agents.
One real workflow.
Let’s find the evidence.
Comparing agents or configurations? Bring a workflow, a decision, and a definition of success. Let’s explore a focused evaluation pilot together.