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From "Why This Word?" to "Why This Action?": A New Era for AI Explainability

For years, “opening the black box” of artificial intelligence meant one thing: identifying which specific features—like a pixel in a photo or a word in a sentence—triggered a model’s decision. If an AI flagged an email as spam, an explanation tool might highlight the word “enlarge” as the culprit.

But as AI evolves from passive predictors into “agentic” systems—autonomous assistants that navigate websites, use software tools, and execute multi-step plans—these traditional explanations are becoming obsolete.

A new paper from researchers at the Vector Institute and the Mayo Clinic, titled From Features to Actions, argues that our current methods for explaining AI are fundamentally mismatched for the era of AI agents. To truly understand why an autonomous agent fails, we must stop looking at “features” and start looking at “trajectories.”

The “Static” vs. “Agentic” Gap

To understand the shift, consider two different AI tasks.

In a static task, such as a job-posting classifier, the AI looks at a block of text and outputs a category. Traditional Explainable AI (XAI) tools like SHAP or LIME work beautifully here. If the AI labels a posting as “IT,” these tools might show that the word “software” had a high “attribution score,” giving the user a clear intuition of the model’s logic.

In an agentic task, however, the AI acts as a travel assistant. To book a flight, it must search a database, filter by price, check a user’s calendar, and finally execute a booking. Success or failure isn’t determined by a single word, but by a “trajectory”—a long sequence of observations and decisions.

The researchers found that while traditional tools can tell you which words the travel agent liked, they cannot tell you why the agent suddenly abandoned a search or why it hallucinated a flight that didn’t exist.

Diagnosing the “Slow Failure”

The study’s most striking finding involves “state tracking inconsistency.” In complex tasks, agents often suffer from a “slow failure” pattern. An agent might start correctly but gradually lose track of the user’s constraints—for example, “remembering” a budget of $500 in step one but acting as if the budget is unlimited by step ten.

The researchers found that this specific type of breakdown is 2.7 times more prevalent in failed runs. Crucially, traditional feature-highlighting tools completely missed these errors. Instead, the team utilized “trace-grounded rubrics”—essentially a set of behavioral checks (like “did the agent use the right tool?” and “did it stay consistent?”)—to pinpoint exactly where the logic curdled.

The Minimal Explanation Packet

To bridge this gap, the authors propose a new framework: the Minimal Explanation Packet (MEP).

Think of the MEP as a “black box flight recorder” for AI agents. Rather than just a single heatmap of important words, an MEP bundles three things:

  1. The Artifact: A human-readable summary of the agent’s reasoning steps.
  2. The Context: The full “trace” of every tool called and every observation made.
  3. Verification Signals: “Rubric flags” that alert a human if the agent violated a core behavior, such as using a tool incorrectly.

Why It Matters

As AI agents begin handling sensitive tasks in healthcare triage and financial operations, “trusting” the AI isn’t enough; we need to be able to audit its journey.

The research suggests a paradigm shift: we must move away from asking “what features mattered?” and toward asking “where did the plan go wrong?” By focusing on the trajectory of decisions, we can finally move toward AI systems that are not just powerful, but truly accountable.