Agentic labor is AI carrying out a defined responsibility through multiple steps, using authorized tools and information, within clear limits, and handing back a result a person can review. Veeva uses the term for its Falcon agents. For a quality team, the useful question is which parts of a job, such as preparing a deviation investigation, an agent can take on, and where qualified people stay responsible.
What agentic labor means for quality work
A deviation is opened after an unexpected equipment stoppage during manufacturing. Before anyone can reach a defensible conclusion, someone has to reconstruct what happened: find the applicable procedure, review the production and equipment records, check timestamps, follow up on missing information, and assemble the evidence.
That is the work agentic labor can take on. When an agent gathers the records and reconstructs the event, the investigator spends less time assembling evidence and more time evaluating it, which is the part that needs their judgment.
For quality teams, the opportunity starts with identifying which parts of that work an agent can perform, and defining exactly where qualified people remain responsible.
From answering questions to carrying out assignments
AI can contribute to work in several ways. It can answer a question, summarize a document, draft a response, or carry out a sequence of actions toward an assigned result.
An assignment asks more of the system. It has to work out what information is required, use the appropriate tools, evaluate what comes back, and recognize when progress needs human input.
The design underneath can combine predictable workflows with more flexible agent behavior. In Building effective agents, Anthropic groups both as agentic systems but separates workflows, where models and tools follow predefined code paths, from agents, where the model directs its own process and tool use. It recommends the simplest design that works.
| Design | Who decides the steps | Suited to |
|---|---|---|
| Workflow | The builder, in predefined code paths | Well-defined tasks that need predictability and consistency |
| Agent | The model, as it works | Tasks that need flexibility and model-driven decisions |
Both have a place, and the right design depends on the task. For a quality team, the useful starting point is a concrete assignment:
That assignment needs an owner, a scope, permitted actions, and an explicit handoff.
Why agentic labor is appearing in life sciences
Veeva uses “agentic labor” to describe Veeva Falcon, which it announced on May 27, 2026. Veeva describes Falcon as a workforce of standard agents that execute complex, multistep business processes end to end, working natively in Vault applications, following the customer’s business rules, and escalating to people only for exceptions. That makes the term relevant to how life sciences companies evaluate AI agents.
Which Falcon agents are available and which are still planned is covered in our Veeva Falcon guide.
The label alone tells a buyer relatively little about a particular implementation. The practical questions concern the work itself: which steps the system performs, which records it can access, how it handles exceptions, and what a person receives for review.
A demonstration becomes much more useful when it follows one specific assignment through those steps.
Example: preparing a deviation investigation
Consider an illustrative equipment-stoppage investigation. The initial report says an operator paused a manufacturing run after an alarm. The production record, the equipment log, and the operator’s account each hold a different piece of the event.
For a drug manufacturer, 21 CFR 211.192 requires a thorough investigation of any unexplained discrepancy, with a written record that includes the conclusions and follow-up. Within an approved implementation, an agent could be assigned to:
- Locate the applicable procedure and the relevant records.
- Build a timeline, with each event linked to its source.
- Identify conflicting timestamps or missing information.
- Draft follow-up questions for the investigator.
- Assemble the known facts, unresolved questions, and supporting evidence into a draft investigation record.
Suppose the equipment log and the operator’s account disagree about when the alarm occurred. A useful result preserves both accounts and flags the discrepancy. The investigator then resolves the conflict, assesses its significance, and decides what further work is needed.
The deliverable is a structured investigation package that makes the evidence and the remaining questions easier to inspect.
A useful result includes the unfinished work
An agent’s output should make uncertainty easy to find. A reviewer needs to see which records support a statement, which information is missing, and which conclusions still need judgment. So a draft investigation should keep four kinds of statement apart:
| Kind of statement | Illustrative example from the stoppage |
|---|---|
| A fact supported by a record | The batch record shows the run paused, with the operator’s entry. |
| A conflict between sources | The equipment log records the alarm at 09:12. The operator’s account puts it at about 09:20. Both are kept. |
| A possible explanation to investigate | The equipment clock may not be synchronized with the batch record system. |
| A question still unanswered | Was maintenance notified, and is there a work order for the alarm? |
A complete-looking narrative can hide an incomplete investigation. Clearly labeled gaps give the reviewer a better starting point.
The same applies when an agent reaches a record it cannot access, or an action outside its authority: the handoff should say what prevented progress and what is needed next.
Start with one responsibility and measure the result
A practical evaluation starts with a bounded assignment and agreed success criteria. For deviation preparation, a team could check whether the agent:
- Found the required records
- Reconstructed the timeline accurately
- Surfaced the known discrepancies
- Kept every statement tied to evidence
Measure the work left for the reviewer too: corrections, additional searches, unanswered questions, and time spent checking the output.
Historical cases make a good test set when the organization has permission to use them. Include cases with missing records or conflicting evidence, so the evaluation tests how the system handles incomplete information.
The goal is to establish whether the assignment produces useful, reviewable work within its intended boundaries. An evaluation is not validation: work that moves into regulated use still has to be validated for its intended use. See GAMP 5 and computer system validation.
How Ovada applies agentic labor
Ovada works as a digital Quality employee for manufacturing quality teams: an AI agent with a defined job, authorized access, and a qualified person to hand its work to. See how that works in deviation management.
Our view of agentic labor starts with a defined responsibility: clear inputs, authorized actions, visible evidence, and an accountable person receiving the work. That gives a quality team a concrete basis for deciding what to delegate, what to supervise, and what it must continue to own.
Sources
All pages were checked on October 21, 2026.
Questions
What is the difference between agentic AI and an AI agent?
An AI agent is a system that decides its own steps and tool use as it works toward a goal. Agentic AI is the broader term for systems that act through multiple steps, including workflows that call a model at fixed points. Anthropic groups both as agentic systems. Agentic labor describes the work such a system does when it is given a defined responsibility.
Is agentic labor the same as digital labor?
The terms overlap. Salesforce introduced Agentforce 2.0 in December 2024 as a digital labor platform, and Veeva calls its Falcon agents agentic labor. Both describe AI agents doing work rather than assisting a person. Neither label says what a particular agent does, so ask which steps it performs, what it can access, and who receives the result.
Who is accountable for work an AI agent does in a GMP quality system?
The quality unit and its designated qualified people. Under 21 CFR 211.22, the quality control unit has the authority to approve or reject, and to review production records so that errors are fully investigated. An agent can gather evidence and prepare drafts, but conclusions, approvals, and signatures stay with people.
How should a quality team evaluate an AI agent for deviation investigations?
Give it one bounded assignment with agreed success criteria, ideally on historical cases you are permitted to use, including some with missing or conflicting records. Check whether it found the required records, rebuilt the timeline accurately, surfaced discrepancies, and tied statements to evidence. Then measure the corrections and checking left for the reviewer.