Estimated reading time: 10 minutes
Key Takeaways
- As agentic AI moves from advising to acting in manufacturing, organizations must be able to explain and defend every decision.
- MES traceability is crucial for making AI accountable as it provides necessary context and ensures decisions can be explained.
- AI must generate rationales based on prior recorded data, not reconstruct justifications after the fact.
- Explainability in AI stems from its foundational architecture, influencing how decisions can be defended.
- Determinism, guardrails, and human oversight are essential to maintain accountability with AI in manufacturing.
- Ultimately, leaders must decide the level of trust in AI decisions, as accountability cannot shift from humans to AI.
Can You Delegate a Decision to AI?
As agentic AI moves from advising to acting on the shop floor, the question is no longer whether it’s smart enough. It’s whether someone can explain what it just did.
Yes, I know it sounds scary to many, but even if you resist the idea today, at some point soon you will let AI act on your line.
Take a moment to consider what that could look like dispatching a lot, adjusting a setpoint, routing material to rework, or placing a batch on hold. The technology is ready, and the pressure to use it is undeniable.
The problem is that, some time after that decision, someone with authority will ask why it did what it did. It could be a regulator during an inspection. Or an auditor reconstructing an event. It could be your own management after a scrap incident. Or a customer’s quality team before they sign.
And in that room, “the model was highly confident” is not an answer. It is the beginning of a very bad day.
Why a Human-in-the-Loop isn’t Enough to Make AI Accountable?
The question that decides whether you can delegate a decision to AI is not how intelligent the system is. It is whether the system can explain itself to someone who holds you accountable.
And explainability, it turns out, is not something you add to AI after the fact. It is a property of what the AI was built on.
If this seems hypothetical or far-fetched, think again. There was a first FDA warning letter to call out AI in pharmaceutical manufacturing. Investigators found the firm had not conducted process validation before distributing its drug products, as required under 21 CFR 211.100. The firm’s reply, in its own words, was that it had not been aware of the requirement, because the AI agent it used “never told [you] it was required.”
Bear in mind that the FDA’s position was not that AI had been used. It was that AI outputs must be reviewed and cleared by a person accountable for CGMP compliance, and that using AI to help create documents does not remove the obligation to verify they are accurate and compliant. The agent’s silence is not a defense.
Why MES Traceability is the Foundation for Accountable AI
There are two ways an AI system can answer the question “why did you do that.”
The first is to generate a plausible rationale after the fact. When the reasoning was never recorded anywhere, the model reconstructs a story that fits the outcome.
The second is to trace the decision back through the structure that produced it. The system took this action because a specific rule applied to this step in this version of the recipe, for this product, based on the material’s state and the quality result recorded at that moment.
That is an explanation because the reasoning was captured before the decision was made. You are not asking the AI to justify itself after the fact; you are following a path that already exists.
To see why that distinction matters, step back from AI for a moment and consider a single number on the shop floor.
How MES Traceability Creates the Context AI Needs
A measurement means nothing on its own. It is just noise until you can place it in context: which product it describes, which equipment produced it, which recipe version was running, which operator performed the work and whether their certification was current, which material lot was used, and which order tied it all together.
Add those relationships, and the number becomes a fact. Remove them, and it is only a value without meaning.
That context must be assembled at one of two points:
– Late
Events land in separate places, and when a question is asked, the system re-links everything on the spot. It can work, but you pay the stitching cost on every question, and a system reconstructing relationships it did not witness is guessing.
Sometimes the guess is wrong and no one can tell.
– Early
At the instant the step completes, the execution system already knows the product, the equipment, the recipe version, the operator, the lot, the order. Not because it looked them up, but because it enforced them a moment earlier. It checked the certification before allowing the step. It confirmed the equipment was qualified and it bound the lot to the order. The context was the act itself.
AI can only explain a decision if the reasoning was captured at the moment it happened and the MES is what captures it, by enforcing rules (certifications, equipment qualification, lot binding) as each step executes, not by reconstructing them afterward.
What is interesting is that this is not a new standard invented for AI. It is the one that more regulated manufacturing environments already live by.
Data-integrity principles require records to be attributable and captured at the time an event occurs. Regulators also expect electronic records to reconstruct the event, not simply summarize it.
The reason is straightforward. If you cannot trace a decision back to the moment it was made, you can hardly defend it. AI does not change this requirement; it raises the stakes, because the actor asking to be trusted is no longer a person.
Read more: The clock is ticking: why manufacturers can’t afford to wait on their data
A Well-Built MES is What Makes Tracing Possible
The reason a decision can be traced at all is that a well-built MES does not just record what happened. Whether explicitly or not, every execution creates a graph of meaning. The material, lot, equipment, operator, and recipe step become its nodes, while the transactions between them form the edges.
Every Production Decision Creates a Traceable Chain of Context
MES traceability captures these relationships as they happen, creating a connected record of how production actually unfolded.
That graph is what makes an explanation possible. When an auditor asks “why,” the answer is a traceable path through the graph: from the decision, back to the rule that triggered it, the step it governed, and the material, quality state, and person involved.
The explanation doesn’t have to be reconstructed on demand. It already exists because the system captured these relationships as the factory ran.
To be fair about the alternative, a contextualization layer built above execution can reconstruct much of this after the fact. In many asset-centric and process environments, it can do so well enough to be useful.
But reconstruction is limited by what the execution system actually captured. It can approximate lineage, but it cannot recover meaning that was never recorded. That gap between approximate and defensible is widest where it matters most.
In high-mix, regulated, discrete manufacturing, where genealogy, deviations, and rework aren’t edge cases, they are fundamental to how the operation runs.
Explainability vs. Accountability: They Are Not the Same
This is why you should be careful with vendors who say they have “solved explainability.” Two different things are sold under that word.
One is understanding how a model reasons internally, which the field has not solved and does not yet have an agreed way to even judge.
The other is an audit trail showing which inputs led to a decision which is what most explainability tools actually provide.
That’s why the foundation matters when it comes to agentic AI. Without the right foundation, AI can’t deliver reliable answers.
Determinism, Guardrails and Human Oversight Keep AI Accountable
Some topics are always present when we discuss Agentic AI decisions. These are important, although do not necessarily relate to the explainability topic.
Determinism. This is very important, although it simply prevents the use of generative AI in critical runtime execution functions. In the case of regulated environments or risk-averse organizations, determinism, where the same input conditions always generate the same output decisions, is a must.
In these cases, generative AI belongs at design time, not execution time. Whether through vibe coding, or more intelligently through vibe metadating (AI based configurations that are used by the platform) companies can accelerate their business rules and processes; but only deterministic behavior reaches runtime execution.
Guardrails. Again critical, but not enough. With guardrails you’re choosing the space where AI operates, and the limits are themselves rules that need to be explainable. But they won’t explain the decisions that happen inside those limits.
Human check. Human-in-the-loop is invoked constantly as the answer to all of this. It is not sufficient on its own. A human approving a decision they cannot interrogate is not oversight. It is liability with a signature.
Still, the Decision is Yours
So back to explainability and decisions. None of the solutions mentioned removes the hardest call. Even a system that can explain perfectly does not tell you how much confidence is enough to act on. That threshold, the line between assist and autonomy for any given decision, is a judgment someone accountable has to own. The platform can make the threshold visible, auditable, and adjustable. But it cannot set it for you.
That is precisely why this stops being an IT question and becomes a leadership one. A leader can decide how much trust to grant. But no leader can defend a decision the system cannot explain.
Governing Agentic AI in Manufacturing
In regulated environments, the direction is already visible: the European Commission’s draft Annex 22 draws the boundary explicitly: generative AI and large language models are not covered and should not be used in critical GMP applications, and where AI is used in non-critical ones, qualified personnel remain responsible for ensuring the output is suitable, under a human-in-the-loop approach.
The principle underneath the drafting is that intelligence does not transfer accountability. So before the debate about how autonomous your AI should be, there is a prior question, and it is architectural before it is strategic. When your AI acts, and someone with authority asks why, does your factory answer with a rationalization, or with a path it already wrote down?
You can’t delegate a decision your system can’t explain. And your system can only explain what it was built to capture in the first place.
FAQ
Not in regulated or risk-averse environments. Determinism is required at runtime, so generative AI belongs at design time, not execution time.
Only if the system can explain itself to whoever holds you accountable. Intelligence doesn’t transfer accountability, and an agent’s silence isn’t a defense.
That AI outputs are reviewed and cleared by an accountable person. Using AI doesn’t remove the obligation to verify accuracy and compliance, as shown in the FDA’s first AI-related warning letter.
AI can only explain a decision if the reasoning was captured at the moment it happened and MES is what captures it, by enforcing rules (certifications, equipment qualification, lot binding) as each step executes, not by reconstructing them afterward.
Explainability is an audit trail showing which inputs led to a decision. Accountability is a person owning that decision and its consequences, one doesn’t create the other.
It’s the connected record a MES creates as production happens linking material, equipment, operator, and recipe step so a decision can be traced back to why it was made.
Context captured at the moment of execution is enforced and verified. Context reconstructed later is a guess and sometimes the guess is wrong with no way to tell.
