Agent Behavior Evaluation: an Agentforce Architecture Guide

What is Agent Behavior Evaluation in Agentforce architecture?

In Agentforce architecture, evaluate quality, consistency, safety, and business value across representative tasks. This guide explains the design decisions, controls, and implementation checks needed to apply the pattern in production.

Agent evaluation asks whether the system behaves well across a distribution of real tasks, not whether one response is technically correct. Quality includes factuality, relevance, completeness, tone, policy compliance, action correctness, and business outcome.

The evaluation design should reflect risk. A low-impact phrasing variation and an unauthorized financial action cannot carry the same weight.

Measure behavior from several angles

• Combine deterministic checks, model-based evaluation, operational metrics, and targeted human review.
• Segment results by job, channel, user group, language, and risk level instead of relying on one average.
• Measure consistency across repeated and paraphrased inputs because model outputs vary.
• Connect quality scores to real outcomes such as resolution, escalation, correction, abandonment, and user satisfaction.

Build an evaluation program

1. Define a rubric with examples of passing, weak, and unacceptable behavior.
2. Create representative and adversarial datasets with explicit coverage targets.
3. Calibrate automated evaluators against human judgments and review disagreements.
4. Baseline before launch and monitor for meaningful drift after every release.