Taking agentic AI into production in regulated industries: what we learned across 121 sources

Agentic AI in production is a system of coordinated AI agents that plan, retrieve and act on real tasks in a live, governed environment — not a demo. In regulated industries such as pharma, “in production” carries a heavier meaning: every output must be traceable, every source auditable, and every action defensible. That is a different problem from building a clever prototype.

Why most GenAI never leaves the pilot

In 2026, at Roche, we deployed a multi-agent pharmaceutical-intelligence system built on a retrieval-augmented generation (RAG) architecture, covering 8 countries and 121 regulatory and market sources. The lesson was blunt: the model was never the hard part. The hard part was governance, source provenance, and trust — the distance between an impressive answer and a decision someone will stake their name on.

How to move agentic AI to production in a regulated environment

1. Anchor every answer to a source. In a regulated context, an unsourced claim is worthless. Design retrieval so each agent output cites the exact regulatory or market document it came from.
2. Separate the agent that plans from the agent that acts. Coordination beats a single monolithic prompt. Keep planning, retrieval and synthesis as distinct, inspectable steps.
3. Put a human control gate where the decision has consequences. Automate the retrieval and the draft; keep a named human owner on the call that matters.
4. Instrument for audit from day one. Log what each agent read, decided and produced. If you cannot reconstruct why the system said something, you cannot ship it in pharma.
5. Govern the sources, not just the model. 121 sources is a living corpus. Define who curates it, how it is refreshed, and what “authoritative” means for each domain.
6. Measure adoption, not accuracy alone. A correct answer nobody trusts is a failed project. Track whether the people who make decisions actually use it.

Frequently asked questions

What is the difference between agentic AI and a chatbot? A chatbot answers a prompt. An agentic system decomposes a goal into steps, retrieves what it needs, and coordinates multiple specialized agents to complete a task — with the ability to plan and act, not just respond.

Why is “in production” so much harder in regulated industries? Because outputs must be traceable, auditable and defensible. The bar is not “impressive,” it is “would you stake a regulatory or access decision on this?”

Does RAG replace fine-tuning? Not necessarily. RAG grounds answers in a controlled, current corpus and is often the right first choice when provenance matters; fine-tuning addresses different needs. Choose by the problem, not the trend.

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Julian Grijalba is Head of Data Insights & Competitiveness LATAM at Roche. Views expressed here are his own and do not represent Roche.