Evaluating Mental-Health AI Agents Through Clinical-Grade Simulation

We have had the privilege of working closely with one of a global mental-healthcare organisation building safe, evidence-based conversational AI for triage, therapy support and chronic-care management.

Across product, engineering, conversation design, and even clinical teams, we consistently saw the same challenge surface again and again:

“We can’t afford unsafe behaviours, missed risk signals, or non-compliant conversations.”

Healthcare doesn’t give you the luxury of trial and error. In mental health, especially, a single misunderstanding can have clinical consequences [1] [2].

This is why we built UserTrace, a simulation and evaluation platform designed specifically for healthcare AI agents, with deep emphasis on clinical safety, regulatory alignment, and trustworthiness.

What the Team Was Already Doing

Prior to working with us, the mental health provider relied on:

• manual creation and review of chat transcripts
• small evaluation sets based on typical conversations
• scoring individual responses rather than full interactions
• clinician review in late testing stages

These steps helped identify obvious defects, yet critical risks still emerged only once real users engaged with the system.

As one clinical AI lead recently told us:

“Evaluating a classifier is easy. Evaluating how a user feels after third or forth turns with our agent is the real problem.”

Observed Evaluation Gaps in Mental-Health AI

Every team we have either spoken or worked with describes the same pattern when it comes to testing healthcare agents:

1. Manual evaluation cannot capture real-world clinical complexity Teams typically manually review chats to build test cases, resulting in small datasets that only reflect typical or expected interactions.
2. No visibility into conversation-level risk Most evals score individual responses, while clinical harm emerges across multi-turn flows, tone drift, or missed escalation.
3. No tooling for empathy, tone, or cultural nuance Manual test sets cannot capture multilingual slang, metaphorical expressions, shame-laden phrasing, or indirect distress.
4. Failures appear only after deployment Unexpected behaviours, especially involving crisis signals or emotional dysregulation, surface only when real patients interact with the system.
5. Behavioural drift over time Model updates or prompt changes can alter behaviour in subtle ways, and teams struggle to detect when safety performance degrades.

These challenges created the need for a reliable, repeatable and clinically grounded testing methodology.

Our Approach: Clinical-Grade Simulation Through UserTrace

We reframed this evaluation problem from first principles:

If real-world user paths are infinite,
and emotional expression is unpredictable,
then the only solution is simulation, which matches the complexity of real patient communication

UserTrace automatically generates and tests thousands of persona × emotional state × scenario conversations across diverse clinical and emotional contexts before your agent ever reaches a real patient.

We focus on four core pillars of healthcare safety:

1. Realistic Simulation of Clinical Interactions

UserTrace’s simulation engine is designed to mirror the full complexity of real clinical interactions. It models:

• Diverse yet relevant personas (like adolescents navigating emotional volatility, burnout-prone professionals, elderly individuals dealing with loneliness, postpartum mothers, chronic illness patients)
• Emotional states (like anxiety, depression, irritability, guilt, fear, dissociation)
• Clinical scenarios (like therapy-intake misunderstandings, crisis-adjacent language, medication adherence concerns, relationship stress, body-image issues, sleep dysfunction, trauma disclosures and passive self-harm expressions.)
• Cultural and linguistic variations (Mix of languages, indirect expressions)
• Behaviour patterns (like avoidant, overwhelmed, crisis-adjacent)

This allows teams to see how their agent behaves under true clinical complexity, not synthetic prompts.

2. Safety & Compliance Evaluation

Every simulated conversation is evaluated for:

Safety

• Early detection of crisis language
• Proper boundary-maintaining behaviour
• No diagnoses, treatment, or medication advice
• Correct escalation pathways
• Tone appropriateness and empathy alignment

Compliance

Frameworks such as:

• FDA CDS/SaMD considerations
• HIPAA security and data privacy safeguards
• Internal clinical guidelines
• Organisational safety workflows such as suicide-risk escalation

Each conversation is assessed not just for correctness, but for clinical appropriateness and emotional safety.

3. Edge-Case Detection at Scale

Simulation exposes failure modes that almost never appear in manually created test sets:

• Indirect suicidal ideation (Example :“I don’t want to wake up tomorrow”)
• Minimising distress due to misunderstanding tone
• Misinterpretation of metaphors or sarcasm as literal statements
• Cultural nuance failures
• Boundary violations/ Adversarial behaviour
• Unintended/inaccurate clinical advice (Hallucination)
• Looping or stalled conversations

These insights help teams prioritise the real risks that matter most to patient safety.

4. Continuous Evaluation for Every Model Update

Healthcare AI systems are living systems, prompts, models, and instructions evolve.

UserTrace integrates directly into CI/CD to:

• Detect behavioural drift
• Highlight regressions
• Produce clinical review-ready summaries
• Maintain long-term safety and performance baselines

This ensures ongoing reliability as systems scale.

Key Outcomes for Mental-Health Provider

1. Safer AI From Day One: Teams can validate that agents behave safely across thousands of emotional, linguistic, and cultural variations before deployment.
2. Trust From Clinicians & Clinical Leaders: Providers gain confidence through measurable consistency, boundary adherence, correct escalation behaviour, and audit-ready evaluation outputs.
3. Stronger Patient Outcomes: By catching UX and safety failures early, you ensure patients receive stable, supportive, and clinically appropriate guidance.
4. Faster, Lower-Risk Iteration Cycles: Evaluation cycles drop from weeks to hours, enabling rapid improvement without compromising safety.

Our Commitment

We built UserTrace because we’ve seen firsthand how valuable AI can be in mental healthcare and how dangerous it becomes without rigorous evaluation.

Our thesis is simple:

Healthcare agents deserve the same safety rigor as medical devices, even when they operate through conversational AI.

If you’re building or deploying mental-health or clinical AI agents, we’d love to partner with you to make your systems safe, reliable, and clinically trustworthy at scale.

Book a demo →