The following use cases are an illustrative example per sector. Each snapshot shows what the agent evaluates before acting, what it executes autonomously when it can do so with guarantees, and what the analyst receives when it cannot.

Prescriptive Decision AI for Healthcare · Pharmacovigilance
Pharma
Pharmacovigilance and safety signal detection

Safety teams receive hundreds of individual case reports (ICSRs) each cycle. The issue is not detection itself: it is the evaluation volume on an already-flagged signal. The assessor does not know whether a drug–event combination represents a real emerging signal, whether it exists in the combination or in monotherapy, or what the exact regulatory obligation is—without manually reconstructing the analysis. The result is delayed PRAC notification, with regulatory and reputational risk. AyGLOO adds an agentic layer on top of that process with a precise decision function: minimise the risk of late notification for signals with a clear obligation, while avoiding escalation of weak signals that would not be defensible before PRAC. The agent evaluates not only whether the signal crosses a threshold, but how robustly it crosses it in the identified micro-segment — and acts accordingly. The assessor always reviews and signs: the agent removes all analysis work prior to that decision.

1
The agent automatically identifies signals with a clear regulatory obligation and prepares the complete file. The assessor applies scientific judgement only on lower-reliability signals — weak or preliminary.→ Time to PRAC notification: from weeks to hours for clear signals
2
ISA contextualises within the full cohort using ROR and PRR, and network analysis reveals whether the signal exists in a combination or in monotherapy — identifying the exact clinical micro-segment.→ True driver isolated without manual subgroup analysis
3
Simulations (What-if + CF) isolate which subpopulation still requires mandatory reporting and confirm which clinical factor is the driver — precisely defining the SmPC update proposal.→ Risk micro-segment defined before drafting the proposal
4
The regulatory narrative for PSUR/PBRER section 16.3 is generated pre-drafted in English, ready for review and signature. The agent also manages reporting deadlines without assessor intervention.→ Late-notification risk removed · audit trail ready for inspection
Today: what the safety team receives
ICSR batch #PV-2026-04471 Signal score: 0.91 · HIGH Ravaxelimab · Grade 3 hepatotoxicity · 23 reports · No additional context
With AyGLOO. Same signal, fully analysed
Ravaxelimab · Grade 3 hepatotoxicity Signal score: 0.91 · 23 ICSRs The agent acts
XAI · Twin Why this combination is escalated ISA Pattern in the full cohort Graph Signal network structure What-if · CF Causality simulations Action Agent decision PDF Pre-drafted PSUR narrative
1. Why this combination is escalated. Twin model (depth 7 · fidelity 97.3%)
100%IF ROR > 4.0 AND lower_IC > 2.0 AND n_cases ≥ 10 robustly in the identified micro-segment → Mandatory PRAC notification. Regulatory threshold consistently exceeded: co-medication with methotrexate in 19/23 cases (importance: 0.44), age >65 in 17 cases (0.28), elevated baseline ALT in 14 cases (0.21). The agent prepares the file. The assessor reviews and signs.
81%IF ROR between 2.0 and 4.0 AND n_cases ≥ 5 → Grey-zone signal: near threshold but not consistent enough. The agent avoids escalation: a premature notification would not be defensible before PRAC. Active monitoring and re-evaluation next cycle.
59%IF ROR > 2.0 without confirmed IC → Preliminary signal without statistical robustness. The agent closes with traceability. Escalating now would harm regulatory credibility without sufficient evidence.
The agent does not treat the threshold as a fixed rule: it evaluates how robustly it is exceeded in the specific micro-segment. The same signal score can justify mandatory reporting if the pattern is consistent across subgroups, or monitoring only if the signal is concentrated in a single prescriber site or driven by an unconfirmed factor. That distinction prevents premature notifications that harm credibility with PRAC.
XAITwin
2. Full-cohort contextualisation (ISA)
Signal concentrated in micro-segment: Ravaxelimab + methotrexate + age >65 · ROR: 8.4 (95% CI: 4.1–17.2) · PRR: 7.1 · Above EMA PRAC detection threshold · No detectable signal in monotherapy cohort (ROR: 1.2) · High reliability: act with confidence.
ISA is the agent’s double-check before preparing the file: it confirms the signal exists in the combination and not in monotherapy. Without that check, the agent would not prepare the file even if the score is high. This distinction defines whether the SmPC proposal affects monotherapy or co-medication only.
ISA
3. Signal network structure. Which clinical elements are connected
The 23 ICSRs are not independent: they share three statistically connected clinical factors · Central node: Ravaxelimab + methotrexate interaction (ROR 8.4) · Age >65 amplifies the signal by an additional 34% when present with elevated baseline ALT · Without methotrexate: the signal disappears (ROR 1.2) · 3 of 23 cases come from the same prescriber site with the same co-medication protocol
Network analysis shows the signal has structure: it is not noise distributed across 23 independent cases. The drug–co-medication–age profile interaction forms a defined risk micro-segment that individual reports do not reveal and manual review would take weeks to isolate.
Graph
4. Simulations. Sensitivity to subpopulations and isolation of the true driver
Excluding patients with elevated baseline ALT: ROR drops from 8.4 to 4.9, signal remains mandatory · Excluding only age >65: ROR drops to 3.1, weak signal · Excluding both factors: ROR 1.8, no signal · Removing methotrexate co-medication: signal falls to ROR 1.4, no signal · Interaction effect confirmed: the combination is the driver, not either drug alone
What-if defines for which subpopulation the signal remains mandatory — the exact micro-segment for the SmPC proposal. CF confirms the combination is the driver by removing each factor separately. Together they eliminate weeks of manual subgroup analysis.
What-ifCF
5. Agent decision
The agent prepares the notification file — the assessor reviews and signs
Regulatory threshold exceeded (ROR 8.4, confirmed CI, 23 ICSRs) · Prepares complete file for PRAC notification via EVWEB within 15 days · SmPC update proposal: contraindication warning for Ravaxelimab + methotrexate in patients >65 with elevated baseline ALT · Initiates specific follow-up in affected cohort · Generates pre-drafted PSUR 16.3 narrative in English ready for review · Manages the reporting deadline automatically
or, if the signal is near threshold but not robust enough in the micro-segment (medium reliability, 81%)
→ The agent retains and actively monitors: the signal is near threshold but not robust enough — a premature notification would not be defensible before PRAC · the agent defines how many additional ICSRs would confirm the signal · schedules automatic re-evaluation next cycle · the assessor receives a follow-up notice with quantified evidence gap, not a notification alert
or, if the signal is preliminary without confirmed CI (low reliability, 59%)
→ The agent closes with traceability: documents the signal with all computed indices · escalating now would harm regulatory credibility without sufficient evidence · automatically re-evaluates when new ICSRs arrive for the same drug–event · full audit trail for any future inspection
The real decision tension in pharmacovigilance is not notify vs not notify — it is when to notify and when to wait for more evidence. The agent optimises that tension: it minimises late-notification risk for signals with clear obligation, and actively avoids premature notifications that would not be defensible before PRAC and would harm the safety team’s credibility. The assessor is not replaced because EVWEB submission requires human regulatory accountability — what the agent removes is the full analysis work prior to that decision.
⚠ Final notification text requires safety assessor review before EVWEB submission. AyGLOO does not send documents autonomously. Aligned with GVP Module IX and ICH E2C(R2) requirements.
ActionPDF

Illustrative example. Each deployment is adapted to each organisation’s models, data, and operating procedures.

Estimated impact · pharmacovigilance teams with active signal management
Hours
Time to PRAC notification for signals above threshold
Vs weeks of manual analysis · late-notification risk removed for clear signals
0
Regulatory-obligation signals unmanaged
The agent monitors all active signals in parallel · no obligation is missed due to volume or manual prioritisation
−80%
Manual analysis time per escalated signal
The assessor reviews and signs · does not reconstruct subgroup analysis from scratch each cycle