ACTO AI Webinar Four Pillars: Agentic AI Evaluation Guide for Life Sciences

Chapter headingChapter subheadingA hands-on guide with practical decision rules for evaluating agentic AI solutions for life sciences commercialization teams.Contents

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EVALUATION GUIDE

AI Guardrails for Life Sciences

A practical guide for evaluating agentic AI solutions for commercialization teams.

Agentic AI is only ready for Life Sciences when it can operate within the realities of a regulated environment:

Approved content. Role-based permissions. Human oversight. Audit-ready records. Validated workflows.

Why This Matters Now

AI is moving fast, but it must have guardrails to scale in a regulated environment.

Life Sciences organizations are under pressure to move faster, reduce manual work, and support field teams with AI. But without the right controls, agentic AI can create new risks:

Shadow AI adoption
Unapproved content use
Unclear data access
Inconsistent outputs
Limited auditability
Compliance gaps that surface too late

The Bottom Line:

Responsible AI starts with security, governance, observability, and validation.

Do you know where and how AI is being used in your organization today?

Yes, we have a clear view across teams
Somewhat, but usage is inconsistent or informal
No, we know AI is being used, but do not have visibility
We are still early and have not assessed usage yet

The Four Readiness Pillars:

What makes agentic AI ready for Life Sciences?

Security

Protect what matters before AI acts

Agentic AI may retrieve information, recommend next steps, summarize interactions, or trigger workflows. Every action must preserve data confidentiality, privacy, and system integrity.

What to look for:

SOC 2 Type II or comparable security controls
Role-based access controls and permissions
SSO and secure authentication
Clear data boundaries across customer, patient, HCP, commercial, and medical information
Transparent data handling and storage practices

Evidence to request:
Security certifications, data flow diagrams, access-control models, incident response processes, and vendor risk documentation.

Observability

See what AI is doing and why

You cannot manage AI risk if you cannot see how AI is being used, what it is producing, its decision logic, and which sources it is using.

What to look for:

Audit trails for prompts, responses, sources, decisions, and downstream actions
Logs, metrics, and traces that support monitoring and investigation
Source references and content lineage
Content indicators for trusted-source retrieval
Admin dashboards that make usage, quality, and exceptions visible

Evidence to request:
Sample audit reports, admin dashboards, logging schemas, monitoring alerts, and data lineage.

Governance

Keep AI inside approved boundaries

In regulated environments, governance determines what an AI agent is allowed to know, say, recommend, and do.

What to look for:

Guardrails configurable by team, product, role, and use case
Human review points for high-risk
outputs and actions
Separation between medical, commercial, and training workflows where required
Alignment to SOPs, promotional review rules, and medical/legal requirements
Clear escalation paths when AI
reaches a boundary

Evidence to request:
Guardrail configuration, SOP mapping, approval workflows, escalation paths, and role-by-role permission examples.

Validation

Prove AI is safe before it scales

Agentic AI should be tested before deployment and monitored after launch. Validation should cover the agent, its workflows, its knowledge sources, and the quality of its output.

What to look for:

Role-based testing and certification
Documented accuracy, safety, and governance testing before go-live
Acceptance criteria tied to real use cases
Change control and re-validation as content, models, or workflows evolve
Ongoing monitoring for quality, accuracy, and compliance

Evidence to request:
Validation packages that include test scripts, certification criteria, release/change control processes, and performance monitoring plans.

Evaluation Scorecard

Use this as a discussion guide with vendors, IT, compliance, commercial operations, medical affairs, and learning leaders. A strong solution should be able to show proof—not just roadmap promises.

What good looks like:

Evaluation Area	What To Look For	Questions To Ask	Score
Security	Certified controls, RBAC, SSO, encryption, and clear tenant boundaries	What data is used, where is it stored, and who can access it?	1 2 3 4 5
Governance	Guardrails mapped to roles, markets, SOPs, and approved workflows	How are policies enforced, and when is human review required?	1 2 3 4 5
Observability	Logs, audit trails, source lineage, quality monitoring, and admin visibility	Can we reconstruct why an agent gave an answer or took an action?	1 2 3 4 5
Validation	Documented testing, acceptance criteria, change control, and ongoing monitoring	How are agents tested and certified to ensure responses meet compliance and accuracy requirements?	1 2 3 4 5
Integration	Connection to trusted enterprise systems and approved content repositories	Can it work with our CRM, LMS, CMS, content, and data ecosystem?	1 2 3 4 5
Testing	Testing and certification process for agent compliance and skills accuracy	Which roles and workflows are supported out of the box?	1 2 3 4 5

Red Flags to Note:

“We can add validation later.”

Validation should be planned before deployment, not retrofitted after adoption.

“The agent can access everything.”

Broad access increases risk; role-based context and permissions matter.

“The model explains itself.”

Ask for logs, citations, source lineage, and audit trails—not generic confidence language.

“It is compliant because it is AI-safe.”

Compliance depends on documented controls, workflows, testing, monitoring, and accountability.

Practical Decision Rule

Advance the vendors that can prove readiness, not just promise it.

A strong agentic AI solution should demonstrate:

Secure infrastructure
Policy-aware agent behavior
Traceable answers, actions, and decisions
Fit-for-purpose validation
Integration with trusted enterprise systems
Human oversight where it matters most

The goal is not simply to deploy AI faster. It is to deploy AI that teams in regulated environments can trust, govern, monitor, and improve.

Responsible AI Starts with Trust

Leading organizations are not only asking: Can this agentic AI work?

They are asking: Can we secure it? Govern it? Observe it? Validate it? Trust it at scale?

Agentic AI can accelerate Life Sciences' work—but only when built to meet the standards, workflows, and accountability that regulated environments require.

About ACTO

Trusted by 14 of the top 50 pharmaceutical companies, high-growth biotechs, and leading medtech organizations, ACTO is redefining how therapies reach patients after regulatory approval. We unite human intelligence with empathetic AI through our AI-powered Field Excellence Platform and AI SuperAgents to accelerate product launches, strengthen field execution, and deepen customer engagement. By elevating training and coaching while agentifying up to 30% of the work embedded in customer-facing roles, ACTO frees teams to focus on what matters most: human connection, judgment, and strategic execution. Built to meet the industry’s highest regulatory standards, ACTO is FDA 21 CFR Part 11 validated and SOC 2 Type II certified. Learn more at www.acto.com.

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