From Chatbot Pilots to Governed, Production-Grade AI Agents
We build agentic AI systems that actually run in regulated environments — multi-agent orchestration, tool-use frameworks, governed memory, immutable audit trails, and HIPAA Security Rule controls for health systems, pharma, biotech, clinical labs, and genomics organizations.
2026 is the year agentic AI moves from chatbot to coworker. Most organisations are nowhere close to capturing it. Four failure patterns we see again and again.
System prompts treated as access controls
Teams instruct an agent “do not access oncology PHI” and assume that satisfies HIPAA. It does not. Only data-layer enforcement under 45 CFR § 164.312 qualifies as an audit-defensible control.
No agent identity, no audit trail
Agents inherit an engineer’s credentials, call internal APIs, and leave no per-action log. When HHS OCR or an auditor asks “who did this,” the answer is a person — not the agent that actually acted.
Single-shot LLM calls dressed as agents
A prompt that calls a tool once is not an agent. Without planning, memory, retry, and decision boundaries, the system fails the moment the workflow has more than one branch.
No validation strategy for non-deterministic systems
AI validation playbooks built for static models do not cover agents that plan and re-plan. Without scenario-based eval, drift monitoring, and red-teaming, regulators have no answers.
Frame agent maturity as four levels. Use this to scope honestly before committing to a build.
Ad-hoc
Single-purpose chatbots and task assistants in isolation. Most organisations live here. Useful, but not agentic.
Managed
Shared access controls, managed identity, structured logging. The minimum for any agent that touches PHI.
Integrated
Agents interoperate over shared context, knowledge graphs, and policy controls. Workflows span EHR, LIMS, and CRM.
Optimised
A central orchestrator dynamically prioritises tasks, coordinates agents, and resolves conflicts — replacing real process work.
Most production-ready engagements start at Level 1 and aim to reach Level 3 within 9 months. We do not promise Level 4 in a single phase. Anyone who does is selling a slide deck.
Every layer is engineered with the compliance plane wired in by default — not bolted on at audit time.
Agent Design & Decision Boundaries
What the agent is allowed to do, what it must escalate, and where the human-in-the-loop sits. Boundaries defined at the workflow level before a single line of code is written.
Multi-Agent Orchestration
Production agents are a team. We build orchestration layers using LangGraph, CrewAI, AutoGen, or custom frameworks on AWS Bedrock, Azure AI Foundry, or Vertex AI — with clear specialisation and conflict resolution.
Tool-Use Frameworks
Agents act through tools — EHR APIs (Epic, Cerner), LIMS connectors (LabWare, STARLIMS), Veeva Vault, internal microservices. MCP servers where appropriate, with typed tool contracts and tool-level audit logging.
Governed Memory & Knowledge
RAG over your verified data — clinical guidelines, SOPs, prior cases, regulatory documents — with versioned embeddings, evidence citations on every response, and re-ranking for high-stakes retrieval.
HIPAA-Aligned Compliance Plane
ABAC for granular PHI authorisation, PHI sanitisation pipelines, immutable audit trails meeting 45 CFR § 164.312(b), and per-agent identity so every action is attributable. Designed for HHS OCR examination.
Evaluation, Drift Monitoring & Red-Teaming
Scenario-based eval harnesses run continuously, not just at release. LangSmith or Langfuse observability, custom drift dashboards, and red-team suites that probe prompt injection, tool misuse, and goal drift.
Deployment, Lifecycle & Retirement
Agent registration, version pinning, controlled rollout, performance SLAs, and explicit retirement so an agent does not remain active beyond its intended purpose.
Patterns we have engineered or are actively building, mapped to NonStop's Applied AI and genomics practice. Every pattern ships with the compliance plane wired in by default.
Prior authorisation orchestration, intake triage, inbox routing, clinical documentation drafting against EHR context.
Variant pre-classification, ACMG evidence assembly for human review, VUS reclassification queues, multi-omic case prep for tumour boards.
Target identification across internal data and literature, trial site enrolment monitoring, protocol amendment impact analysis, regulatory submission drafting.
Accessioning exception handling, QC anomaly investigation, instrument failure triage, TAT root-cause analysis.
Genetic counselling support, PGx prescribing alerts, post-result clinician routing.
Every deployment pattern ships with the compliance plane wired in ABAC, PHI sanitisation, immutable audit trails, per-agent identity.
Every technology choice is selected for HIPAA-aligned architecture, clinical-grade reliability, and scale without rearchitecting.
Most engagements start with a 45-minute Architecture Review. No pitch. A clear picture of where you are and
what needs to change.
Map your current state against the maturity spectrum, identify the highest-ROI agent workflow, and scope a phased build. 45 minutes. Clear deliverable.
One workflow, end-to-end — design, build, evaluate, deploy, hand over with runbooks. The fastest path from pilot to production on a defined scope.
Orchestration layer, three to five interoperating agents, full compliance plane, observability stack, and the governance model your audit and quality teams need.
Explore the platform →Tell us the workflow you want an agent to own, the systems it needs to touch, and your compliance footprint. We’ll come back with a maturity assessment, a target architecture, and a phased delivery plan.
Schedule a CallA RAG chatbot retrieves and answers. An agent plans, calls tools, observes results, and decides what to do next - sometimes coordinating with other agents. RAG is the right answer for knowledge access. Agentic engineering is the right answer when the workflow has branches, requires action across systems, and benefits from autonomous coordination. Most production deployments combine both - RAG inside the agents.A production-ready clinical bioinformatics pipeline must be reproducible across runs, scalable for clinical sample volumes, auditable for regulatory compliance, and integrated with clinical systems such as LIMS and reporting platforms.
Three things that system prompts cannot deliver: ABAC at the data layer so PHI authorisation is enforced regardless of what the agent decides to ask for, a PHI sanitisation pipeline that detects and minimises leakage in agent inputs and outputs, and immutable audit trails that satisfy 45 CFR § 164.312(b) - including per-agent identity so every action is attributable. We engineer these as architectural defaults, not optional add-ons.
We build scenario-based evaluation harnesses that run continuously - not pass/fail at release. Coverage includes happy paths, adversarial inputs, prompt injection, tool misuse, goal drift, and PHI exposure tests. Outputs are versioned with the underlying model and prompt set, so a regulator or quality team can reconstruct exactly how an agent behaved at any point in its history.
Yes - Epic and Cerner via FHIR R4 and CDS Hooks, LIMS systems via HL7 v2 and direct APIs, Veeva Vault and SAP via their native connectors, and custom systems via typed tool contracts or MCP servers. Tool integration is treated as a first-class architectural decision, not a sprint deliverable.
Tell us the workflow you want an agent to own, the systems it needs to touch, and your compliance footprint. We’ll come back with a maturity assessment, a target architecture, and a phased delivery plan.
45-minute Architecture Review — no pitch, clear deliverable
HIPAA compliance plane engineered by default, not bolted on
Modular engagement — single agent or full multi-agent platform
Integrates with Epic, Cerner, LabWare, STARLIMS, Veeva Vault
