In agentic AI, success isn’t just acting—it’s acting right. Evaluations (“evals”) are systematic checks that determine whether AI actions are correct according to defined business rules and objectives. Evals keep AI aligned with policy, performance, and cost—so autonomy scales without losing control.
Why AI Often Fails at the Point of Application
In their 2025 episode AI’s Unsung Hero: Data Labeling and Expert Evals 1, Andreessen Horowitz highlights a key trend: as AI systems become more capable, the challenge is no longer generating output—it’s evaluating whether those outputs were actually good decisions.
As they explain, “every successful application—voice, code, agents—had a great eval set behind it.” Not as a final check, but as the operational layer that helps the system learn, improve, and stay aligned with what the business needs.
It’s easy to demo a model that works once. The real test is whether that system can operate—reliably, repeatedly, and with accountability—across real-world scenarios. And for that, evaluation is required.
Evolving Role of Evals
Evals have always been important for validating system performance. What’s changed is the environment in which AI operates. Modern AI systems are more autonomous, make decisions at speed, and can generate outputs that look convincing even when they’re wrong. In high-impact workflows, that can mean downstream consequences that are expensive and disruptive—for example, ordering the wrong part, dispatching a technician to the wrong location, or triggering a warranty process unnecessarily. As AI takes on more responsibility, the role of evaluation has shifted from a periodic check to a constant safeguard that enables safe autonomy. Without it, you don’t have a transparent, governed system—you have a black box.
Evals Are a Design Requirement, Not an Afterthought
Treating evals as part of the design blueprint is what makes AI safe to scale. They need to be embedded across the full lifecycle of building and operating an AI agent—from defining success criteria, to validating changes before release, to monitoring and learning from live performance. This ensures that every decision is tied to measurable outcomes and that autonomy improves over time instead of drifting.
AI Platform Architecture Enables Scalable Evals
Evaluation at this level isn’t a feature—it’s an architectural requirement. To scale automation across products and workflows, evals need to be embedded in a shared platform. That means:
| Capability | Role in Evaluation |
| Eval Design (Design-time) | Map business requirements to measurable tests (FTF, MTTR, cost-to-serve, warranty leakage); build realistic eval datasets and success criteria. |
| Pre-Production Gates | Every model/workflow change runs the full eval suite; automated scorecards for go/no-go; human validation with business context. |
| Production Monitoring | Live scoring on real cases; drift detection on accuracy/cost/SLA; auto-escalation to retrain/rollback when thresholds are breached. |
| Feedback Integration | Promote discovered edge cases to reusable tests; version and reuse across agents, products, and regions; harden performance over time. |
| Full-Scope Monitoring | Accuracy & budgets, fairness/bias, robustness/security, explainability, privacy/compliance, and operational health—measured continuously. |
In Bruviti’s Aftermarket Intelligence Platform, evals are native: every agent shares the same scoring framework, so each decision is scored end-to-end—functional accuracy, SLA/policy compliance (including warranty/entitlement), FTF/MTTR and cost budgets, robustness/security, privacy, and live drift/operational health—case by case. With eval gates and live drift monitoring in place, development cycles run up to 95% faster, and production rollouts avoid regressions by enforcing go/no-go scorecards.
With this structure, every decision made by the system—whether about a triage path, a part swap, or a service recommendation—can be evaluated in context. That enables better governance, better learning, and ultimately, truly autonomous automation.
A Proof Point from Deployment
In one deployment, an AI agent was tasked with resolving inbound support cases for a complex equipment line. The cases involved a mix of chat logs, sensor alerts, service history, and policy constraints.
Each action was scored across resolution accuracy, cost-to-serve, SLA/policy adherence, and downstream rework, creating a clear chain of evidence for every case. That let us shift from human approval on every step to exception-only review: when scores met agreed thresholds, the agent executed autonomously; when they didn’t, it auto-escalated. Trust increased, manual touchpoints dropped, and the same signals fed the next round of improvements.
Three Principles to Carry Forward
- Make evals both the entry gate and the kill switch.
Set go/no-go scorecards before every change (models, prompts, workflows) and wire auto-rollback/exception-only review when live scores breach thresholds for SLA, cost, safety, or policy. - Govern the eval set like code—and grow it from production.
Put eval datasets and rubrics under version control, require change approvals, and promote every new edge case from the field into the shared eval suite so all agents inherit the fix. - Measure outcomes, not just outputs.
Score each decision end-to-end—functional accuracy, SLA/policy compliance, FTF/MTTR and cost budgets, robustness/security, privacy, and drift/operational health—and use these as service SLOs for deploy/not-deploy calls.
FAQs
What are “evals”?
Systematic checks that the AI made the right decision—not just a right-looking output—against your business rules and targets (FTF, MTTR, cost, warranty/entitlement, SLA, compliance).
Do evals slow us down?
Done right, they speed you up. Design-time tests and pre-prod go/no-go scorecards prevent bad releases; live drift monitors catch issues before they become field escalations—so teams ship faster with controlled risk. This approach is standard in leading AI deployments, ensuring both safety and velocity at scale.
What should a vendor be able to show us?
Four concrete artifacts: (1) a design-time eval set mapped to your SKUs/policies, (2) pre-prod go/no-go scorecards, (3) live drift monitors tied to operational thresholds, and (4) a path to promote new edge cases into reusable tests inherited by all agents.
- Andreessen Horowitz: AI’s Unsung Hero: Data Labeling and Expert Evals, June 2025 ↩︎
