Precision therapeutics for rare diseases as well as complex oncology cases is an area that may benefit from Agentic AI Closed-Loop (AACL) systems to enable individualized treatment optimization — a continuous process of proposing, testing, and adapting therapies for a single patient (N-of-1 trials).
N-of-1 problems are not typical for either clinicians or data systems. Type 2 diabetes in the US is more of an N-of-3.8×10^7 problem, so we’re looking at a profoundly different category of scaling. This lower number is not easier, because it implies existing treatment protocols have not been successful. N-of-1 optimization can discover effective regimens rapidly, but only with a data system that can manage dense multimodal signals (omics, time-series biosensors, lab results), provide fast model iteration, incorporate clinician-in-the-loop safety controls, and ensure rigorous provenance. We also need to consider the heavy cognitive load the clinician will be under. While traditional data analytics and machine learning algorithms will still play a key role, Agentic AI support can be invaluable.
Agentic AI Closed-Loop systems are relatively new, so let’s look at what a system designed to support this architecture would look like from the ground up.
Data Platform
First, let’s define the foundation of what we are trying to build. We need a clinical system that can deliver reproducible results with full lineage and enable safe automation to augment clinical judgement. That’s a decent overview of any clinical data system, so I feel like we’re on solid ground. I would posit that individualized treatment optimizations need a reduced iteration time from the standard, just because the smaller N means we have moved farther from the SoC, so there will likely be more experiments. Further, these experiments will need more clever validations. Siloed and fragmented data stores, disconnected data, disjoint model operationalization and heavy ETL are non-starters based on our foundational assumptions. A data lakehouse is a more appropriate architecture.
A data lakehouse is a unified data architecture that blends the low-cost, flexible storage of a data lake with the structure and management capabilities of a data warehouse. This combined approach allows organizations to store and manage both structured and unstructured data types on cost-effective cloud storage, while also providing high-performance analytics, data governance, and support for ML and AI workloads on the same data. Databricks currently has the most mature lakehouse implementation. Databricks is well known for handling multimodal data, so the variety of data is not a problem even at high volume.
Clinical processes are heavily regulated. Fortunately, Unity Catalog provides a high level of security and governance across your data, ML, and AI artifacts. Databricks provides a platform that can deliver auditable, regulatory-grade systems in a much more efficient and effective way than siloed data warehouse or other cloud data stacks. Realistically, data provenance alone is not sufficient to align the clinician’s cognitive load with the smaller N; it’s still a very hard problem. Honestly, since we have had lakehouses for some time and have not been able to reliably tackle n-of-1 at scale, the problem can’t soly be with the data system. This is where Agentic AI enters the scene.
Agentic AI
Agentic AI refers to systems of autonomous agents, modular reasoning units that plan, execute, observe, and adapt, orchestrated to complete complex workflows. Architecturally, Agentic AI running on Databricks’ Lakehouse platform uniquely enables safe, scalable N-of-1 systems by co-locating multimodal data, high-throughput model training, low-latency inference, and auditable model governance. This architecture accelerates time-to-effective therapy, reduces clinician cognitive load, and preserves regulatory-grade provenance in ways that are materially harder to deliver on siloed data warehouses or generic cloud stacks. Here are some examples of components of the Agentic AI system that might be used as a foundation for building our N-of-1 therapeutics system. There can and will be more agents, but they will likely be used to enhance or support this basic set.
- Digital Twin Agents compile the patient’s multimodal state and historic responses.
- Planner/Policy Agents propose treatment variants (dose, schedule, combination) using constrained optimization informed by transfer learning from cohort data.
- Evaluation Agents collect outcome signals (biosensors, labs, imaging), compute reward/utility, and update the digital twin.
- Safety/Compliance Agents enforce clinical constraints, route proposals for clinician review when needed, and produce provenance records
For N-of-1 therapeutics, there are distinct advantages to designing agents to form a closed loop. Let’s discuss why.
Agentic AI Closed Loop System
Agentic AI Closed Loops (AACL) enable AI systems to autonomously perceive, decide, act, and adapt within self-contained feedback cycles. The term “agentic” underscores the AI’s ability to proactively pursue goals without constant human oversight, while “closed loop” highlights its capacity to refine performance through internal feedback. This synergy empowers AACL systems to move beyond reactive processing, anticipating challenges and optimizing outcomes in real time. This is how we scale AI to realistically address clinician cognitive load within a highly regulated clinical framework.
- Perception: The AI gathers information from its from its Digital Twin among other sources.
- Reasoning and Planning: Based on its goals and perceived data of the current test iteration, the AI breaks down the objective into a sequence of actionable steps.
- Action: The AI executes its plan, often through the Planner/Policy Agents.
- Feedback and Learning: The system evaluates the outcome of its actions through the Evaluation Agents and compares them against its goals, referencing the Safety/Compliance Agents. It then learns from this feedback to refine its internal models and improve its performance in the next cycle.
AAIC systems are modular frameworks. Let’s wrap up with a proposed reference architecture or an AAIC system using Databricks.
AAIC on Databricks
We’ll start with a practical implementation of the data layer. Delta Lake provides versioned tables for EHR (FHIR-parquet), structured labs, medication history, genomics variants, and treatment metadata. Time-series data like high-cardinality biosensor streams can be ingested via Spark Structured Streaming into using time-partitioning and compaction. Databricks Lakeflow is a solid tool for this. Patient and cohort embeddings can be stored as vector columns or integrated with a co-located vector index.
The Feature and ETL Layer builds on Lakeflow’s capabilities. A declarative syntax and a UI provide for a low-code solution for building continuous pipelines to normalize clinica code and compute rolling features like time-windowed response metrics. The Databricks Feature Store patterns enable reusable feature views for inputs and predictors.
Databricks provides distributed GPU clusters for the model and agent layer as well as access to foundational and custom AI model. Lakeflow Jobs orchestrate agent execution, coordinate microservices (consent UI, clinician portal, device provisioning), and manage retries.
MLFlow manages most of the heavy lifting for serving and integration. You can serve low latency policy and summarization endpoints while supporting canary deployments and A/B testing. The integration endpoints can supply secure APIs for EHR actionability (SMART on FIHR) and clinician dashboards. You can also ensure the system meets audit and governance standards and practices using the MLFlow model registry as well as Unity Catalog for data/model access control.
Conclusion
Agentic AI closed-loop systems on a Databricks lakehouse offer an auditable, scalable foundation for rapid N-of-1 treatment optimization in precision therapeutics—especially for rare disease and complex oncology—by co-locating multimodal clinical data (omics, biosensors, labs), distributed GPU training, low-latency serving, and model governance (MLflow, Unity Catalog). Implementing Digital Twin, Planner/Policy, Evaluation, and Safety agents in a closed-loop workflow shortens iteration time, reduces clinician cognitive load, and preserves provenance for regulatory compliance, while reusable feature/ETL patterns, time-series versioning (Delta Lake), and vector indexes enable robust validation and canary deployments. Start with a strong data layer, declarative pipelines, and modular agent orchestration, then iterate with clinician oversight and governance to responsibly scale individualized N-of-1 optimizations and accelerate patient-specific outcomes.
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