Domino introduces fastest, safest path to scale enterprise agentic AI systems
New platform capabilities unite experimentation, evaluation, deployment and monitoring in one governed workflow to rapidly move agentic AI applications to production with confidence
4 Mar 2026
Domino Data Lab has announced a major new platform update creating the first fully governed end-to-end platform for operationalizing agentic AI systems that life sciences innovators can now use to accelerate research and regulatory workflows while maintaining full traceability and compliance.
This latest winter release equips organizations with a new agentic development lifecycle (ADLC) experience and underlying large language model (LLM) hosting capabilities. These together pave the fastest path for enterprises to build, evaluate, deploy, and monitor agentic AI systems at scale with built-in governance, reproducibility, and control.
Agentic AI application teams lack the tracking, evaluation, and monitoring capabilities commonplace in traditional ML workflows. This creates significant challenges in moving agentic AI systems from prototype to production, and erodes enterprise trust in these applications to execute real business workflows and automate complex, high-impact decisions.
“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.”
Groundbreaking capabilities for agentic AI
With Domino's latest capabilities, the same integrated, governed platform teams rely on for traditional AI now supports the full agentic AI lifecycle. This foundation — replacing the fragmented tools and ad-hoc checks that slow AI development — now supports customers building, deploying, and monitoring agentic applications with the integration and governance they expect. Through this expansion, Domino now delivers agentic AI teams:
- A fully streamlined ADLC experience: New dedicated agentic instrumentation and evaluation capabilities extend Domino’s platform to connect all stages of the agentic AI lifecycle — Build, Evaluate, Deploy, and Monitor — within a shared system of record, and with the ability to iterate at scale across each stage. This ensures complete lineage, reproducibility, and governance across the entire agentic development lifecycle.
Domino achieves the ADLC experience by adding:
- A built-in universal tracing software development kit: Using any agentic orchestration framework, teams can trace every step of agentic AI creation — including prompts, tool calls, decisions, and output — through each ADLC stage, all within Domino.
- Structured evaluation and side-by-side comparison: Teams building agentic AI systems can visualise, evaluate, and compare applications at both summary and trace-level detail using shared metrics and complete configuration lineage supporting consistent, repeatable evaluation.
- Production-ready deployment of agentic AI applications: Teams can close the gap between experimentation and deployment of agentic applications using Domino Apps’ streamlined deployment, autoscaling, and broad policy-based governance capabilities. In this way, thousands of business users can access production agentic AI systems through governed applications, rather than fragile demos or unmanaged APIs.
- Continuous agentic AI evaluation and reproducibility: Teams can evaluate production performance of agents using metrics, custom evaluations, and human feedback, re-visiting historical agent decisions and exploring detailed traces captured in production.
- Governed agentic AI and agent hosting: Underpinning the ADLC experience, teams can now securely host, serve, and manage LLMs in their own infrastructure for high-performance inference and reduced operational costs. These capabilities allow organisations to adopt LLMs at scale while maintaining control over data, costs, and security within established regulatory boundaries.
"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.”