Resources

A library of practical guidance for ML and AI engineering teams deploying LLM agents in production. Not tutorials for beginners. Reference material for teams who have moved past "does it work?" and are asking "is it safe to operate at scale?"

Evaluation Starting Points

Before running a single test, you need a baseline. A baseline requires knowing what you're actually measuring. For enterprise LLM deployments, the minimum viable evaluation set covers output correctness on domain-specific golden sets, latency at p95 under expected load, cost per inference at projected volume, and adversarial resilience against prompt injection. Each dimension has a measurement methodology; none are interchangeable with generic benchmark scores.

The Methodology page documents how TestML structures the full 20+ dimension evaluation pipeline. If you're building an internal framework, use it as a reference point for what enterprise-grade evaluation covers, what each dimension measures, and where generic benchmarks fall short.

Red-Teaming Reference Material

Prompt injection is the most underestimated risk in production LLM systems. A retrieval-augmented agent that passes all unit tests can still follow instructions injected into a retrieved document, bypassing the system prompt without any alert firing. It is not a corner case. It is the default failure mode for agents built without systematic adversarial testing.

David Park, our Head of Evaluation Science, built adversarial test suites for three Fortune 500 LLM rollouts before joining TestML. The methodology covers prompt injection, indirect instruction injection via RAG context, jailbreak taxonomy (capability unlocking, role confusion, boundary erosion), and hallucination exploit paths specific to regulated domains. Detailed technical write-ups on each attack vector are in the blog. For a direct engagement, see Security & Red-Teaming.

Compliance and Regulatory References

Enterprise AI deployments in the EU operate under GDPR obligations that extend beyond data storage. Article 22 covers automated decision-making and affects both what your agent can do and how its outputs must be documented. HIPAA adds constraints on PHI handling that determine whether evaluation can run on production data or requires synthetic substitutes. SOC 2 Type 2 covers operational controls around environment provisioning and access to evaluation outputs.

AI regulation is moving fast. The EU AI Act introduced risk-tier obligations for high-risk AI systems, with key enforcement provisions taking effect in August 2026. Financial services deployments face additional FCA and MiFID II constraints on automated decision-making in advisory contexts.

TestML holds SOC 2 Type 2 certification and operates from Dublin under EU jurisdiction. On-premise deployment is available for organisations where production data cannot leave the internal network. For a copy of our security documentation, contact the team.

Observability and Drift Detection

Production monitoring is not optional for regulated AI. Ewa Kowalska, our Lead ML Engineer, built the drift detection pipeline that monitors production agents continuously. The principle: capture a performance baseline at deployment, then run automated regression tests against it. When deviation exceeds the threshold, the team gets an alert before the compliance department finds out through an escalation.

Across 340+ enterprise LLM pipelines evaluated, silent performance degradation typically begins within 60 to 90 days of deployment as upstream model updates or prompt changes accumulate. Teams without continuous monitoring discover this through customer escalations or audit findings. Teams with it discover it in a dashboard.

For domain-specific evaluation suites and ongoing monitoring tailored to legal, medical, financial, or insurance workflows, book a technical review.