Image: images.contentstack.io · rights & removal

Building Agents Backwards from Evaluation

Reporting by SentinelOne BlogRead the original at sentinelone.com

Executive Summary

Evaluating security agents requires defining the work the agent must perform and establishing criteria for assessing the required evidence. A successful approach involves evaluating complete system runs alongside individual competency tests to assess both overall usefulness and pinpoint specific areas for improvement. The text contrasts end-to-end evaluations, which measure overall system performance against realistic workloads like alert burndown or disposition accuracy, with competency evaluations, which isolate single skills to determine exact failure points. End-to-end metrics show whether the system is useful, while competency evaluations reveal exactly what needs fixing. This process necessitates decomposing the agent's function into measurable competencies, such as SIEM query construction or hypothesis generation, each requiring specific evaluation sets based on real failures.

Facts Only

* Evaluating an agent begins with defining the work it should perform and required evidence.
* Successful evaluation combines complete runs and individual competency tests.
* Material changes require comparison with previous systems.
* Operational failures supply new test cases for improvements.
* Evaluation discipline is presented as an architectural decision.
* End-to-end evaluation uses metrics like alert burndown, disposition accuracy, time-to-verdict, stability, consistency, and cost per alert.
* Competency evaluation isolates one skill to test it directly with specific inputs and success definitions.
* End-to-end evaluation measures if the system delivers value; competency evaluation identifies which capability is weak.
* Query construction can be evaluated by checking query correctness against a source's schema.
* Competencies should be prioritized by impact of failure times probability of model failure times decidability of ground truth.
* Evaluation effort should prioritize observed failures over successes.

Full Take

The narrative presents a necessary evolution in how AI systems, specifically agentic systems, are developed, shifting evaluation from an emergent, post-hoc activity to a foundational design choice. The core tension lies between the seductive simplicity of end-to-end metrics—which tell a compelling story about overall performance—and the necessity of competency evaluation, which provides actionable engineering guidance. This forces a recognition that aggregate success is insufficient for safety-critical domains like cybersecurity; failures are not just outcomes but traceable deviations in reasoning and data retrieval paths. The implication is that building autonomous systems requires internalizing expert knowledge into measurable competencies, moving beyond superficial metrics to achieve true operational reliability. The push toward competency evaluation, grounded by domain expertise, challenges the prevailing trend of deferring difficult evaluation until after a proof-of-concept is established, suggesting that rigorous measurement must be an intrinsic part of the agent's architecture rather than an afterthought.
Bridge Questions: If competency evaluation dictates the design, how can organizations efficiently allocate expert time and resources to continuously maintain these granular evaluation suites as systems evolve? What mechanisms can bridge the gap between expert judgment in defining "good" for specific competencies and the automated grading of those judgments during iterative development? What happens when the structure of a real-world security scenario shifts in ways that invalidate existing competency test suites?

From the original · SentinelOne Blog

Executive Summary - Evaluating a security agent begins with defining the work it should perform and the evidence required to consider that work complete and accurate.

Read the full story at sentinelone.com

Sentinel — Human

Confidence

This text is a sophisticated analysis that expertly synthesizes complex concepts in AI evaluation into an actionable framework, displaying characteristics of expert human writing focused on domain application.

Signals Detected

Varied sentence length and complex conceptual phrasing; use of strong domain-specific vocabulary mixed with reflective, cautionary tone.

Strong, consistent argumentative flow; the text successfully weaves together abstract evaluation theory with concrete, practical examples (investigation agents) without sounding purely expository.

Effective structuring using clear headings and tables to organize complex ideas; the argument follows a logical progression from problem identification (brittle development) to proposed solution (competency-based evaluation).

References cited are specific and relevant to the topic, suggesting grounding in academic or industry discourse; the analysis synthesizes known concepts rather than asserting novel facts.

Human Indicators

The integration of named external sources (citations) into a deep argumentative structure suggests human synthesis rather than simple LLM regurgitation.

The tone exhibits critical nuance—questioning the industry's current practice while offering structured, multi-faceted solutions.