Scorecard helps teams evaluate AI agents before issues reach users. Model real scenarios, run systematic checks, and track product-focused metrics that reflect success in context. Blend model tests, human feedback, and product signals to learn what improves outcomes and reduces risk. With observability, comparisons, and alerts, you catch regressions early, explain changes, and ship dependable behavior with evidence. Keep work reproducible with dashboards tracking reliability, latency, and costs.
Model your real user journeys as runnable scenarios. Scorecard executes prompts, tools, and retrieval steps end to end, then scores outcomes with metrics that reflect success in context. You compare versions, flag risky changes, and document results, replacing ad hoc reviews with repeatable experiments that mirror actual use cases across products and teams. Dashboards surface reliability, latency, costs, and outcomes for tuning now. Templates and roles keep scopes and defaults consistent across environments.
Trace agent runs with inputs, intermediate calls, tool outputs, and final results. Dashboards reveal latency, cost, and error patterns so owners can tune prompts and tools. Link traces to tickets and docs to keep work visible. With consistent telemetry, teams understand what happened, why it happened, and how to fix it without guessing across logs or screenshots. Schedules and triggers coordinate recurring runs and reports for reviewers.
Collect structured ratings from reviewers, then blend them with product signals like clicks, resolutions, or conversions. This gives a fuller picture of quality beyond raw scores and helps optimizations target meaningful outcomes rather than synthetic benchmarks alone. Feedback loops guide agents toward safer, more helpful behavior in production. Usage limits and quotas control spend while experiments remain reproducible.
Test changes before release and compare models, prompts, tools, and policies. Set thresholds and alerts to catch regressions automatically in CI. Owners see exactly where behavior improved or broke and can roll forward with evidence, turning launches into measurable steps instead of risky flips. Notes and versions capture why prompts or policies were adjusted over time. Integrations forward traces to tickets, docs, and data warehouses downstream.
Roles, projects, and review workflows keep evaluation accountable. Reports and exports share results with leaders and customers. Standardized artifacts make audits faster and help cross functional teams agree on what good looks like, reducing debate and keeping quality bars consistent. Exports preserve evidence for audits, demos, and stakeholder walkthroughs. Dashboards surface reliability, latency, costs, and outcomes for tuning now.
Applied AI teams, product and platform owners, data scientists, and QA groups building agents in support, search, analytics, or automation; organizations that need reliable metrics, human review, and observability; and leaders who want clear reports, thresholds, and CI checks so models and prompts improve without surprising users or stakeholders. Templates and roles keep scopes and defaults consistent across environments. Schedules and triggers coordinate recurring runs and reports for reviewers. Usage limits and quotas control spend while experiments remain reproducible.
Manual spot checks and scattered logs hide regressions and slow releases. Scorecard replaces guesswork with scenarios, traces, metrics, and human feedback in one workflow. Teams see impact clearly, compare options, set alerts, and document changes, so agents become safer and more reliable while shipping faster and learning continuously. Integrations forward traces to tickets, docs, and data warehouses downstream. Exports preserve evidence for audits, demos, and stakeholder walkthroughs.
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