The black box problem: Solving for agentic AI transparency
There’s a specific phase in every company’s AI journey where things don’t break. They just get… mysterious.
The agent works. The workflow runs. The dashboard is green. Everyone’s LinkedIn posts say “🚀 leveraging AI at scale.” And yet, somewhere deep in your organization, a compliance officer is staring at a decision the system made and thinking: Cool. Now explain it.
You know the moment. You’re in a meeting. Engineering is walking through how the new agent “reasons dynamically based on learned patterns and environmental context” (a sentence that means everything and nothing at the same time). There’s a tidy diagram. Arrows. Boxes. A comforting sense of inevitability.
Then someone asks the wrong question.
“So… when the regulator wants to know why this transaction was flagged and that one wasn’t, what do we show them?”
And just like that, the flowchart stops being reassuring.
This is the agentic AI explainability problem. And it’s quietly becoming the most dangerous gap in modern AI governance.
Not because the technology is broken but because it works in ways that are famously allergic to footnotes.
In this article, we'll break down why non-deterministic agents make regulators nervous, what Explainable AI (XAI) actually offers, how techniques like SHAP and LIME help decode decisions, and how platforms like Moxo use structured interaction logs to turn autonomous workflows into audit-ready systems.
Key takeaways
Agentic AI's opacity creates regulatory exposure. When your system can't explain its reasoning, neither can you, and that's a problem when auditors come calling.
Explainable AI (XAI) bridges capability and accountability. It's not about dumbing down the model; it's about surfacing logic in ways humans can verify.
Techniques like SHAP and LIME offer feature-level attribution. They won't fully "open" the black box, but they illuminate which inputs drove which outputs.
Audit trails require more than model probes. Persistent interaction logs capturing inputs, reasoning steps, and decision timestamps are what satisfy governance teams. This is where Moxo's workflow orchestration becomes essential.
Why non-deterministic agents make regulators nervous
Traditional software is predictable. Input A produces Output B. You can trace the logic, reproduce the result, and explain it to anyone with a whiteboard and fifteen minutes.
Agentic AI doesn't work that way.
These systems reason across multiple steps, adapt to environmental changes, and produce outputs that can vary even with identical inputs. They're powerful precisely because they're flexible. But that flexibility is why your CISO has started asking questions you can't answer.
Unpredictability is the first concern. Slight variations in context can produce meaningfully different outcomes. Good luck explaining that in a regulatory filing.
Traceability is the second. When a decision involves multi-step reasoning, tool invocations, and learned patterns, there's no single line of code to point to. The "why" is distributed across the model's architecture.
Accountability is the third, and the one that keeps legal teams up at night. If the system harms a stakeholder or violates policy, someone has to explain what happened. "The model learned it" is not a satisfying answer.
The EU AI Act and similar frameworks increasingly demand explanations for automated decisions that materially impact people.
This is precisely why organizations need platforms like Moxo that build accountability into the workflow itself, capturing every decision point and routing exceptions to human reviewers before they become compliance problems.
A decision you can't explain is a decision you can't defend.
What explainable AI (XAI) actually offers
Explainable AI isn't a single technology. It's a discipline: a set of techniques designed to make AI decisions interpretable to humans who weren't involved in training the model.
The goal is threefold: transparency (opening up parts of the model logic), interpretability (mapping decisions to features humans understand), and explanation (providing rationales for outputs).
For passive AI systems like classification models or recommendation engines, XAI techniques are well-established. But agentic AI introduces complications. Agents don't just classify. They plan, reason, use tools, and adapt mid-process. The decision isn't a single inference; it's a sequence of inferences, each influenced by the last.
This is where Moxo's Human + AI approach becomes critical. Rather than treating explainability as an afterthought, Moxo separates AI execution from human judgment. AI agents handle coordination, validation, and routing. Humans handle the decisions that require accountability. Every handoff is documented.
Agentic AI requires explainability that operates across the entire reasoning chain, not just the final output.
Decoding decisions: SHAP, LIME, and their limits
If you've spent any time in the XAI literature, you've encountered SHAP and LIME. They're the workhorses of model interpretability.
SHAP (SHapley Additive exPlanations) uses game theory to assign contribution values to each input feature. It answers the question: "How much did each variable influence this specific decision?" For tabular data and structured inputs, it's remarkably useful.
LIME (Local Interpretable Model-agnostic Explanations) takes a different approach. It approximates the model's behavior locally, around a single prediction, using a simpler interpretable model. You lose global understanding but gain clarity on individual decisions.
Both techniques are powerful. Neither is sufficient for agentic AI on its own.
Here's why: SHAP and LIME assume the decision is a single event. But agentic reasoning is sequential. The agent reviewed the context, flagged an anomaly, invoked a validation tool, received a result, and then made its recommendation. Feature attribution at the final step misses everything that happened before.
To truly decode agentic decisions, you need techniques that capture the chain, not just the conclusion. This is exactly what Moxo's interaction logs provide: a complete record of every step, every input, and every decision point in the workflow.
Best practices for agentic AI explainability
If you're deploying agents in regulated environments (and increasingly, every environment is regulated), here's what actually works:
Chain-of-thought logging captures intermediate reasoning, not just final outputs. When the agent "thinks through" a problem, that thinking should be preserved in a format auditors can review.
Feature attribution across stages applies SHAP or LIME at multiple decision points, not just the endpoint. You want to know which inputs mattered at each step of the reasoning chain.
Human-in-the-loop checkpoints create natural intervention points where humans can verify logic before the process continues. This isn't about slowing things down; it's about creating defensible decision points.
Structured interaction logs record everything: inputs, timestamps, tool calls, intermediate outputs, and final decisions. This is your audit trail.
Moxo embeds all four practices into its workflow orchestration. As one G2 reviewer noted: "Moxo provides a complete audit trail of all client interactions and document exchanges. This has been invaluable for compliance and gives us confidence during regulatory reviews."
Creating audit-ready reasoning trails
Here's the part most explainability discussions skip: model interpretability tools are useful, but they don't produce audit artifacts.
Regulators don't want a SHAP plot. They want documentation. They want to see when a decision was made, what data informed it, which steps the agent executed, and who (or what) was accountable at each stage.
According to IBM's AI governance research, organizations with comprehensive audit trails resolve compliance inquiries faster.
This requires platform-level support, not just model-level techniques.
Here's what that looks like with Moxo: An AI agent reviews incoming documents, validates completeness, and flags exceptions. The reasoning is logged. The flagged exception routes to a compliance officer with full context attached. The officer makes a judgment call: approve, escalate, or reject.
That decision is timestamped and recorded. The workflow continues, and every step is preserved.
No side emails. No missing context. No "I think it was approved, but I'd have to check."
Another G2 reviewer captures it well: "The visibility into every step of our workflows has transformed how we handle audits. What used to take days of pulling together documentation now takes minutes."
If your explainability strategy doesn't produce artifacts a regulator can read, it's not a strategy; it's a hope.
Solving for agentic AI transparency
The black box problem isn’t going away. Agentic AI is too useful, too flexible, and too embedded in real operations to roll back. Complexity is the feature.
But the organizations that deploy it responsibly will be the ones that invest in explainability infrastructure, not just interpretability tools.That means using SHAP and LIME where they help. Logging reasoning chains. Keeping humans in the loop where accountability lives. And, above all, maintaining persistent interaction records that turn autonomous behavior into documented process execution.
Moxo provides this by default. AI agents handle the coordination. Humans handle the judgment. Every interaction is logged. Every decision is defensible.
Explainability isn’t a technical checkbox. It’s the foundation of trust between your systems, your regulators, and the people affected by automated decisions.
See how Moxo enables transparent, auditable AI workflows.
FAQs
What exactly is agentic AI explainability?
It's the practice of making autonomous AI decisions understandable to humans, particularly why an agent made a specific decision and what inputs influenced its reasoning. This goes beyond model interpretability to include logging, documentation, and audit artifacts that platforms like Moxo generate automatically.
Why do regulators care so much about AI explainability?
Because automated decisions can affect people's rights, finances, and opportunities. When something goes wrong, regulators need to understand what happened and why. An unexplainable decision is an unaccountable decision, and accountability is what regulatory frameworks exist to enforce.
Can SHAP and LIME fully explain agentic AI reasoning?
Not on their own. These techniques provide valuable feature-level attribution, but agentic AI involves sequential reasoning, tool use, and multi-step logic. Full explainability requires combining attribution techniques with chain-of-thought logging and structured interaction records.
What's the difference between explainability and interpretability?
Interpretability refers to how easily humans can understand a model's mechanics. Explainability focuses on why a specific decision was made, in terms that non-technical stakeholders can evaluate. Both matter for governance.
How do I start building audit-ready AI workflows?
Begin with logging. Ensure every agent action (inputs, reasoning steps, tool calls, outputs, timestamps) is captured persistently. Then add human checkpoints at high-stakes decision points. Moxo's workflow platform provides this infrastructure by default, making audit readiness inherent to how processes run rather than a separate compliance effort.
