Domino introduces fastest, safest path to scale enterprise agentic AI systems
News | Life Sciences | 4 Mar 2026
Domino Data Lab has released a fully governed, end-to-end platform for operationalizing agentic AI systems. The winter update brings an agentic development lifecycle (ADLC) and built-in LLM hosting so life sciences teams can move from prototype to production with traceability, reproducibility, and compliance.
Agent teams have been missing what classic ML has had for years: unified tracking, evaluation, deployment, and monitoring. Without it, promising prototypes stall and trust erodes. This release closes that gap.
Why this matters for science and research
Life sciences work lives or dies on evidence, auditability, and repeatability. Agentic AI adds speed, but without lineage and oversight it won't pass internal QA or external review.
Domino's approach links every prompt, tool call, decision, and output to a shared system of record. That means you can explain how results were produced, compare alternatives, and prove that controls were in place.
What's new in Domino's winter release
- Unified ADLC: Build, Evaluate, Deploy, and Monitor agentic applications in one governed workflow with complete lineage and reproducibility.
- Universal tracing SDK: Instrument any orchestration framework to capture prompts, tool invocations, decisions, and outputs across all stages.
- Structured evaluation: Side-by-side comparisons with shared metrics and full configuration history for consistent, repeatable assessments.
- Production-ready deployment: Ship agents as governed Domino Apps with autoscaling and policy controls so thousands of users can access stable, supported applications-not fragile demos.
- Continuous evaluation: Monitor agents in production with metrics, custom evaluations, and human feedback; revisit historical decisions using captured traces.
- Governed LLM hosting: Host and serve models within your own infrastructure for performance, cost control, and data security that respects organizational and regulatory boundaries.
"Building and deploying agents in production requires both rapid experimentation and robust governance," said Nick Elprin, co-founder and CEO of Domino. "Domino's winter release gives enterprises the agility and control they need to deliver agentic systems that drive real business impact."
"Fragmented tools and ad-hoc processes are critical obstacles keeping agentic AI stuck in prototype," said Shawn Rogers, CEO of BARC US. "Enterprises need a single governed lifecycle and a unified platform that connects experimentation, evaluation, deployment, and monitoring of agents at scale. This approach gives teams the ability to iterate rapidly and move agents to production with confidence."
How the ADLC works end-to-end
- Build: Instrument agents with the tracing SDK. Every prompt, policy, and tool call is captured against versioned data and code.
- Evaluate: Run structured tests and side-by-side comparisons. Standardize metrics across teams so decisions aren't anecdotal.
- Deploy: Push to Domino Apps with autoscaling and access controls. Replace shadow APIs with governed entry points.
- Monitor: Track live metrics, user feedback, and drift. Drill into traces to understand failure modes and improve reliability.
Implications for life sciences
- Research productivity: Agents can assist with literature triage, experiment planning, and method drafting-with traceable reasoning you can audit.
- Clinical operations: Use governed agents for document QC, protocol comparisons, and query management while maintaining a clear decision trail.
- Regulatory workflows: Preserve configuration lineage and evaluation results to support internal reviews and external inspections.
- Safety and pharmacovigilance: Standardize evaluations and enable human-in-the-loop review on flagged cases for accountable automation.
Governance, hosting, and cost control
LLM hosting within your own environment gives tighter control over data pathways, inference performance, and spend. It also supports risk management practices aligned with widely referenced frameworks such as the NIST AI Risk Management Framework.
For teams working toward regulated deliverables, consistent evaluation, audit-ready traces, and change logs map cleanly to expectations for documentation and oversight. See also the FDA's perspective on good ML practices for medical device software here.
Practical checklist to move from pilot to production
- Define objective, decision-focused metrics (accuracy, latency, failure categories) and standardize them across teams.
- Instrument all agents with tracing at the prompt, tool, and decision levels; version datasets and configurations.
- Run side-by-side evaluations against baselines; document pass/fail criteria before deployment.
- Stand up human feedback loops for critical decisions; enforce role-based approvals.
- Enable monitoring with alerting and clear incident runbooks; track cost per request and throughput.
- Log every change (models, prompts, tools) and require re-evaluation before promoting to production.
Learn more
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