Use case / Agent development

Model, harness, and agent evaluation

Compare model, harness, and tool configurations on the same versioned tasks. Inspect task outcomes, interventions, cost, and latency with enough detail to identify what changed.

Enterprise + AI labsScope this use case

An illustrative scenario

Which change actually improved the agent?

A team sees a higher completion rate after changing both the model and retry policy. The new configuration also makes more tool calls. A useful comparison separates the model effect, harness effect, and extra resource budget before selecting a deployment candidate.

Human intentAgent decisionTask outcome

What to vary

Keep the comparison focused. Change the conditions that can alter the right next action.

Model configuration
Candidate model, prompt version, sampling settings, and context window policy.
Harness configuration
Planning, tool definitions, memory, retries, stopping rules, and human escalation.
Environment conditions
User behavior, task difficulty, tool failures, time limits, and available observations.

What to measure

Agree the checks before the evaluation. Inspect outcomes and failures alongside the summary.

Task outcomes
Report completion and failure categories on held-out task families, not only an aggregate score.
Resource use
Compare latency, tool calls, and cost under an agreed budget and measurement method.
Review and uncertainty
Inspect repeated runs, expert disagreement, unnecessary interventions, and high-impact misses.

Define your engagement

Start with a decision you can evaluate.

You bring
Candidate configurations, a baseline agent, target task families, tool adapters, and the evaluation budget.
We scope
A versioned comparison protocol, paired task runs, reproducible traces, and a readout of model-versus-harness tradeoffs.

Use the same task distribution and record all configuration changes. Automated graders need human checks; a higher score on one environment does not establish general agent capability.

Plan a scoped pilot

Questions about this use case

What is an agent harness?

An agent harness is the surrounding software that manages observations, tools, memory, execution, retries, and stopping rules. Helm scopes evaluations of that complete configuration alongside the underlying model.

Can this compare models and agents from different providers?

A comparison can be scoped when the interfaces expose the required observations, actions, and outcomes. Adapter work and the fairness of the comparison are agreed before the engagement.

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