AI Agents for RA/QA: Why General AI Fails — and Why Hoodin Is Different

AI is evolving quickly, and the next phase will rely on specialised “agents” that work with structure, context, and domain-bound logic rather than open-ended chat. For RA/QA teams, this development is highly relevant — but also widely misunderstood.

A common assumption is that general tools such as ChatGPT will soon be able to handle regulatory questions directly. They will not. And they cannot.

This article explains why general AI cannot safely support regulated work, why RA/QA requires an entirely different approach, and how Hoodin is building a controlled, domain-specific agent model designed specifically for regulatory environments.

It also outlines what RA/QA teams can realistically do today to prepare — without drifting away from the core topic of AI safety and agent readiness.

Why General AI Cannot Solve RA/QA Problems

Large language models are powerful, but they are not suited for regulated environments. Regulatory work depends on:

• validated and controlled datasets

• strict source fidelity

• reproducible reasoning

• traceability between requirements and evidence

• explicit product and market attributes

• zero speculation

• stability over time

General AI cannot provide these conditions. It cannot guarantee dataset accuracy. It cannot restrict itself to validated sources. It cannot operate inside regulatory logic. It cannot prevent invented details. It cannot maintain reproducibility or defensible traceability.

In life sciences, these gaps are not minor limitations. They render general AI unsuitable for RA/QA tasks where accuracy, structure, and defensibility are non-negotiable.

Why RA/QA Requires a Different Type of AI

Regulatory work is built on structured, hierarchical models:

• frameworks with defined clauses

• dependencies between requirements

• local deviations across markets

• controlled documentation logic (Annex II/III, 21 CFR 820, ISO 13485)

• traceability between requirements, decisions, and evidence

To support this safely, an AI system must operate within that structure — not outside it.

This is where controlled, domain-specific agent models become relevant. Such agents:

• operate only on validated regulatory datasets

• never guess or assume

• remain bound to explicit product and market context

• produce structured, transparent, auditable reasoning

• never make regulatory decisions

These systems cannot be created by general AI providers. They require domain-specific data, domain-specific architecture, and domain-specific constraints.

The Foundation: Hoodin’s Validated Regulatory Dataset

Before RA/QA agents can operate safely, they require a dataset that is:

• validated

• version-controlled

• harmonised across markets

• mapped to product attributes

• linked to local deviations

• connected to update histories

• terminologically consistent

This foundation is exactly what Hoodin has built over the past decade:37 parent regulations, more than 1,500 locally implemented regulations across Europe, structured key points, deviation insights, market-level variants, update metadata, and contextualised regulatory relationships.

It is this dataset — not the AI model — that enables safe agent behaviour.

What Hoodin Is Building Next: Controlled RA/QA-Oriented Agents

Hoodin is now developing a family of RA/QA agents designed to operate entirely within a validated regulatory environment. These are not open-ended chat systems. They are controlled assistants with strict boundaries and defined scopes.

The initial agent group includes:

• Regulations Expert — structured reasoning based on validated regulatory requirements

• Guidance Expert — high-level clarification of relevant guidances and their regulatory connections

• Standards Expert — conceptual insight into standards without reproducing controlled text

• Update Expert — impact interpretation based on validated update metadata

  
  

A general RA/QA assistant will coordinate context and ensure consistency across tasks. None of these agents will classify products, select conformity routes, make decisions, or issue compliance statements. Their purpose is to strengthen clarity, traceability, and internal understanding — not to replace regulatory expertise.

What RA/QA Teams Can Do Today to Prepare for Agent-Based Support

If future RA/QA agents must rely on validated datasets, explicit attributes, controlled sources, deviation logic, and traceable reasoning, then the only meaningful preparation RA/QA teams can make today is to strengthen exactly those areas.

The preparation does not sit outside the AI problem — it is the AI problem.

In practical terms, RA/QA teams can begin by ensuring that:

• regulatory information is structured rather than narrative (agents cannot operate safely on free-text spreadsheets)

• product and market attributes are explicit, stable, and unambiguous (agents cannot infer attributes without risking inaccuracies)

• all regulatory sources are known, validated, and controlled (agents cannot work with unverified or outdated documents)

• update histories are documented and traceable (agents depend on understanding what changed and when)

• internal terminology is consistent across products and markets (agents require stable conceptual anchors to reason accurately)

  
  

These are not alternatives to agent technology. They are the preconditions that enable it.

Organisations that establish these foundations now will be the only ones positioned to benefit when controlled RA/QA agents become available.

A Practical Starting Point

AI for Applicable Requirements in Life Sciences

To support RA/QA teams in building these foundations, Hoodin offers a practical programme: AI for Applicable Requirements in Life Sciences.

Participants work with:

• applicable requirement frameworks

• justification and traceability models

• alignment and deviation structures

• update interpretation

• controlled reasoning logic

  
  

The stronger these elements are today, the greater the value controlled RA/QA agents will deliver tomorrow.

Invitation to Professional Dialogue

If you have reflections on the limitations of general AI for regulatory work, or if you see particular areas where controlled, domain-bound agents could strengthen RA/QA tasks safely, you are welcome to share your perspective. These discussions help ensure that future tools develop in alignment with regulatory expectations and operational realities.

Early Access Invitation

Hoodin will soon initiate early testing of the forthcoming RA/QA agent model. If you are interested in joining a structured early-access group — focused on evaluating boundaries, reasoning patterns, and regulatory robustness — you’re welcome to sign up below.

This early cohort will be limited to organisations with established applicable-requirement structures, as the purpose is to evaluate controlled logic rather than raw functionality.

Sign up for early access