Reliable Agents Start with Smarter Testing

In recent months, as agent-based technologies continue to evolve, more and more of our life sciences clients have been asking: How do we test AI agents in a regulated environment? Whether it’s a Customer Relationship Management (CRM) system, a Regulatory Information Management (RIM) platform, or a Clinical Trial Management System (CTMS), pharmaceutical companies are increasingly challenged to validate AI tools integrated with those and ensure they meet functional, compliance, and user expectations.

Beyond regulation, European pharma companies face the added complexity of testing outputs in multiple languages. This presents unique hurdles – especially since some evaluation metrics are highly language-specific. This blog focuses on the core elements required to prepare for testing an AI agent. Future entries will dive into testing strategies and multilingual considerations.

ffectively evaluate an agent (or more broadly an AI Model), three foundational pillars are required – in addition to traditional system implementation protocols:

• Metrics
• Benchmarks
• Test Data

Let’s explore each.

Define Clear and Aligned Metrics

The starting point for any evaluation is alignment on what you’re actually measuring. Requirements often specify that the agent must provide “correct” answers – but what does that mean in practice?

This is typically interpreted as accuracy, but that can be quantified in many different ways: from straightforward accuracy scores (correct predictions over total predictions) to more advanced metrics like logarithmic loss or BLEU scores for language outputs.

More importantly, you need a clear definition of what constitutes a true positive. In many use cases, this isn’t a binary outcome. Responses may be partially correct or contextually appropriate, even if not identical to a gold standard. You may choose to evaluate outputs via semantic similarity, or even use another model (yes, an AI judging another AI) to assess quality.

Regardless of the methodology, alignment on evaluation metrics is essential before any testing begins.

Establish Relevant Benchmarks

Under the hood, AI Models expose multiple metrics such as perplexity and accuracy. Benchmarking those is critical for achieving quality.

Once you’ve defined your metric, the next step is understanding what “good” looks like. Unlike traditional system testing, AI agent evaluation often results in a score or percentage. But is 95% accuracy acceptable?

Well, it depends.

Consider a RIM agent responsible for listing required documents for a regulatory submission. In this context, 95% accuracy might be dangerously low – missing 5% of key documents could have major consequences.

Contrast that with a CRM agent suggesting content to engage a healthcare provider. In that case, a 95% accuracy rate may be more than acceptable, as the stakes and tolerances differ.

This makes benchmarking crucial. You need to determine appropriate thresholds based on the agent’s domain and risk level. Aggregating and comparing to known benchmarks can help contextualize your metrics and establish what success actually means for your use case.

Prepare High-Quality, Isolated Test Data

No test is complete without proper test data—but not just any data.

Ideally, your testing dataset should not overlap with the agent’s training data. Otherwise, you’re not really testing—you’re just measuring how well the agent memorized its homework. Unfortunately, transparency around training datasets is often limited, so identifying non-overlapping data can be a real challenge.

Additionally, testing often requires multiple rounds, meaning multiple data sets. For example, if you’re validating an agent designed to assist with clinical trial authorization, you’ll need various clinical study scenarios as test inputs. Each set should be constructed with the help of subject matter experts and carefully curated to avoid bias or unrealistic performance.

Time spent preparing robust test data is well worth it—cutting corners here will almost certainly result in unreliable testing outcomes.

Final Thoughts

This blog focused on the foundational elements required before you begin testing an AI agent in the life sciences space. In regulated environments—particularly when system validation is required—these three components (metrics, benchmarks, and test data) must be clearly defined and documented in your validation plan. Each should be treated with the same rigor as any other component of a validated system.

Future blogs will explore test execution strategies and how to handle the multi-language dimension effectively.

Because, as you’ve probably noticed by now, testing agents isn’t as simple as asking: “Does it work?”

It’s about asking: “Does it work, reliably, under scrutiny, in every language, and across every scenario that matters?”

Metrics. Benchmarks. Test data. These three pillars should be treated with the same rigour as any other component of a validated system. In highly regulated industries like pharma and biotech, there are no shortcuts—only smart testing.