AGENTIC · SPEQ SYNTHESIS
Autonomous Quality Agent
An agentic AI that does more than answer: it plans steps, calls tools, and takes actions inside quality-system workflows such as triaging deviations, routing records, drafting CAPAs, and updating fields.
Because the agent acts rather than advises, an error is not just a bad answer a human might catch but a state change in a regulated system, which is why its action surface and permissions define its risk.
What a system class is not
An AI system class describes a shape of system, not a product and not an approval pathway. SPEQ does not qualify, validate, or endorse any implementation, and no regulator recognises these classes as a category.
Autonomous action on a patient-safety or critical-quality decision — classified critical by rule, independent of the other factors.
Computed deterministically from four Context-of-Use factors — decision consequence, model influence, data sensitivity, and change dynamics. A transparent scoping aid, not a validated risk-assessment system.
SCOPE THE ASSURANCE STRATEGY → CSA WORKBENCHDeployed inside the eQMS to triage, classify, and progress quality events, influencing how a deviation is prioritized, what CAPA is proposed, and which records move forward for human decision.
The agent can alter regulated QMS records and workflow state directly, so a wrong classification or a mis-executed action changes the official quality record and the decisions built on it.
human approval required
Because the agent changes regulated QMS records through autonomous actions, a qualified person must approve each consequential action before it commits, keeping accountability and reversibility with a human.
- A reasoning error can cascade across a multi-step plan, so one wrong intermediate decision propagates into several downstream actions before any human sees the result.
- The agent can take an incorrect or unauthorized action on a regulated record, such as misclassifying a critical deviation as minor and routing it away from proper scrutiny.
- Tool calls can execute with unintended scope or parameters, changing more records or fields than intended when the agent misinterprets a workflow or an ambiguous instruction.
- Automation bias and volume can lead reviewers to rubber-stamp agent actions, collapsing the human checkpoint that is supposed to catch a flawed decision.
- Prompt injection through the content of a deviation or complaint can hijack the agent’s plan, steering it to take actions the organization never authorized.
- Bound the agent’s action surface and permissions explicitly, and require human approval before any consequential or irreversible change commits to a regulated QMS record.
- Maintain a complete, attributable audit trail of the agent’s reasoning, tool calls, and executed actions so every state change can be reconstructed and challenged during an inspection.
- Validate the agent against representative end-to-end scenarios including adversarial and edge-case inputs, measuring not just answers but the correctness and scope of the actions it takes.
- Design safe failure and rollback so a mis-executed or interrupted action can be reversed, and constrain autonomy tiers by the consequence of each action type.
- Defend against prompt injection from record content and monitor action patterns in production to detect drift, unexpected tool use, or degrading decision quality.
Standards SPEQ maps to this class
AI-governance frameworks
The AI-specific shelf that defines “quality AI” — see Good AI Practice.
SPEQ synthesis — applied AI-assurance judgment to help you scope your own Context-of-Use assessment and validation. Not regulatory guidance, not an AI classification service, and not a substitute for your documented risk assessment.