This article is adapted from a lecture series I gave at Deeplearn 2025: From Prototype to Production: Evaluation Strategies for Agentic Applications¹.
, which measure an AI system’s performance in use-case-specific, real-world settings, are underadopted and understudied. There is still an outsized focus in AI literature on foundation model benchmarks. Benchmarks are essential for advancing research and comparing broad, general capabilities, but they rarely translate cleanly into task-specific performance.
By contrast, task-based evaluations let us measure how systems perform on the products and features we actually want to deliver, and they enable us to do it at scale. Without that, there’s no way to know if a system is aligned with our expectations, and no way to build the trust that drives adoption. Evaluations are how we make AI accountable. They’re not just for debugging or QA; they’re the connective tissue between prototypes and production systems that people can rely on.
This article focuses on the why — why task-based evaluations matter, how they’re useful throughout the development lifecycle, and why they’re distinct from AI benchmarks.
Evaluations Build Trust
When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, … your knowledge is of a meager and unsatisfactory kind.
Lord Kelvin
Evaluations define what “good” looks like for a system. Without them, there’s no accountability — just outputs with nothing but vibes to judge whether they meet the mark. With evaluations, we can create a structure for accountability and a path to improvement. That structure is what builds trust, so that we can:
- Define appropriate behavior so teams agree on what success means.
- Create accountability by making it possible to test whether the system meets those standards.
- Drive adoption by giving users, developers, and regulators confidence that the system behaves as intended.
Each cycle of evaluation and refinement strengthens that trust, turning experimental prototypes into systems people can depend on.
Evaluations Support the Entire Lifecycle
Evaluations aren’t limited to a single stage of development. They provide value across the entire lifecycle of an AI system:
- Debugging and development: catching issues early and guiding iteration.
- Product validation and QA: confirming that features function properly under real-world conditions.
- Safety and regulatory strategy: meeting standards that demand clear, auditable evidence.
- User trust: demonstrating reliability to the people who interact with the system.
- Continuous improvement: creating the foundation for fine-tuning and continuous training/deployment loops, so systems evolve alongside new data.
In each of these phases, evaluations act as the link between intention and outcome. They ensure that what teams set out to build is what users actually experience.
Benchmarks vs. Task-Specific Evaluations
Benchmarks dominate much of the AI literature. They are broad, public, and standardized, which makes them valuable for research. They allow easy comparison across models and help drive progress in foundation model capabilities. Datasets like MMLU or HELM have become reference points for measuring general performance.
But benchmarks come with limits. They are static, slow to evolve, and intentionally difficult to help separate cutting-edge model performance, but not always in ways that accurately reflect real-world tasks. They risk encouraging leaderboard chasing rather than product alignment, and they rarely tell you how a system will perform in the messy context of an actual application.
Consider the following thought exercise: if a new foundation model performs a few percentage points better on a benchmark or a leaderboard, is that enough to justify refactoring your production system? What about 10%? And what if your existing setup already performs well with faster, cheaper, or smaller models?
Task-based evaluations serve a different purpose. They are specific, often proprietary, and tailored to the requirements of a particular use case. Instead of measuring broad capability, they measure whether a system performs well for the products and features being built. Task-based evals are designed to:
- Support the full lifecycle — from development to validation to post-market monitoring.
- Evolve as both the system and the product mature.
- Ensure that what matters to the end user is what gets measured.
Benchmarks and task-based evaluations aren’t in competition. Benchmarks move the research frontier, but task-based evals are what make products work, build trust, and ultimately drive adoption of AI features.
Closing Thoughts
Evaluations aren’t just overhead. They define what success looks like, create accountability, and provide the foundation for trust. Benchmarks have their place in advancing research, but task-based evaluations are what turn prototypes into production systems.
They support the full lifecycle, evolve with the product, and enable measuring alignment at scale. Most importantly, they ensure that what gets built is what users actually need.
This first piece has focused on the “why.” In the next article, I’ll turn to the “how” — the practical tactics for evaluating agentic AI systems, from simple assertions and heuristics to LLM judges and real-world feedback.
The views expressed within are my personal opinions and do not represent the opinions of any organizations, their affiliates, or employees.
[1] M. Derdzinski, From Prototype to Production: Evaluation Strategies for Agentic Applications (2025), DeepLearn 2025
