Why AI Agents Need an ‘Actionable’ Explainability Layer to Achieve True Accuracy

Imagine running a critical business intelligence report based on an AI-generated SQL query, only to discover that essential constraints were missing—leading to misleading revenue figures and costly business decisions. Would you trust AI again? After witnessing the unreliability of AI, probably not.  

According to the 2024 McKinsey report, inaccuracy and lack of explainability in agentic AI are among the top three barriers to widespread adoption. Dr. Cynthia Rudin of Duke University emphasizes that we should use interpretable models instead of black-box models in critical applications​.

Why Explainability and Accuracy Are Inseparable

Research consistently shows that true accuracy in AI Outputs can be achieved through a human-in-the-loop (HITL) approach. A recent study published in PMCID demonstrated that human oversight and explainable AI assistance improved human task performance by 7.7 percentage points compared to fully automated approaches. 

However, for HITL to be effective, humans must understand the AI's decision-making process—a challenge that shallow reasoning AI agent (RAG based) implementations struggle to address. This "black box" problem isn't just a philosophical concern—it's a practical barrier to achieving reliable accuracy in real-world applications.

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The Explainability Challenge In AI Agents Due To LLM’s Blackbox

Trust is the cornerstone of AI adoption—if stakeholders or managers can’t see how AI arrives at its answers, they simply won’t use it. Real confidence in AI comes from understanding its outputs and the high-level steps that produce them. Yet when large language models generate queries, they routinely obscure crucial decision points—concealing assumptions, metric definitions, and execution logic. This hidden reasoning not only undermines user confidence but also poses serious threats in analytics-driven settings, where unchecked errors or opaque shortcuts can drive costly business missteps.

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The Human-in-the-Loop Paradox

The paradox is clear: human oversight is essential for accurate AI, but the black-box nature of shallow reasoning AI agents makes effective oversight impossible at scale. This validation bottleneck stems from several factors:

1. Cognitive Load: Understanding complex SQL requires mental tracing of sub-queries, data flows, joins, and transformations—a process that becomes exponentially more difficult as query size increases.
   
   
2. Contextual Knowledge: Domain-specific business rules and data relationships are often implicit, requiring analysts to cross-reference multiple knowledge sources.
   
   
3. Error Cascades: A small misinterpretation early in query generation can propagate through the analysis, creating errors that are difficult to detect.
   
   

Introducing Actionable Explainability: The Missing Link

To address these challenges, it’s crucial to distinguish clearly between Explainability (understanding why an AI makes specific decisions) and Actionability (knowing what steps to take next). While explainability alone offers transparency, without actionability, explanations become static and do not directly lead to improved outcomes. Such a layer should cater to non-technical and technical users, both. 

Explainability: Clarifying the 'Why'

Explainability reveals the AI agent’s decision-making process, enabling users to trust, verify, and interpret AI outputs. This visibility is crucial for effective human oversight. For example, when an AI tool generates a query, showing the step-by-step reasoning allows data analysts to assess whether the logic aligns with business rules and objectives.

Actionability: Guiding the 'What Next'

Actionability goes beyond understanding the decision-making process. It equips users with practical, proactive suggestions to improve the accuracy and effectiveness of AI outputs. By clearly recommending next steps, actionability transforms passive understanding into active improvement, bridging the gap between AI insights and human expertise.

Together, these two pillars create a true partnership between AI and humans, enhancing trust, accuracy, and collaboration. Studies indicate that this combined approach yields significant advantages, including faster error detection and notably higher accuracy compared to shallow reasoning, purely explanatory methods (MIT HCAI Lab, 2024).

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Core Components of Actionable Explainability

1. Intent Decomposition 

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Complex business questions can be ambiguous or multi-faceted. Before generating SQL, an AI should first break these questions down into clear sub-intents and explain dependencies (1:1 or 1:many). This decomposition verifies that the AI correctly interprets each aspect of the user's request and aligns it to the main goal.

Example from TPC-H dataset:

"I need a breakdown of our orders. How many orders were placed in each country and region? I’d also like to see the total revenue from those orders, grouped by month. On top of that, can we include the most frequently ordered part for each country-region combo? Let’s sort everything so the most recent months show up first."

The explainability layer explicitly decomposes this into clear intents:

• Intent 1: Pricing Comparison
  
  • Sub-Intent A: Count the number of orders placed, grouped by country and region.
    
  • Sub-Intent B: Calculate the total revenue from orders, grouped by month and sorted by most recent months first.
  • Sub-Intent C: Determine the most frequently ordered part for each country-region combination.

This decomposition allows analysts to verify that the AI correctly understood each component of the request before proceeding.

2. Context Retrieval Transparency (e.g. Lineage, Metadata)

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AI agents make multiple decisions on data sourcing, metrics selection, and joining tables when generating analytical queries. Without transparency in these choices, analysts may overlook vital contexts or miss relevant datasets. Context selection transparency explicitly explains why particular data sources, tables, or metrics were chosen, and proactively suggests viable alternatives, enhancing analyst confidence and ensuring a more comprehensive and accurate analysis.

Example from TPC-H Dataset:

Case A: Selecting Appropriate Tables for Supplier Performance Analysis

• Context Used:
  "Joined supplier, lineitem, and orders tables to compute average delivery times."
  
• Actionable Recommendation:
  "Consider also joining the partsupp table to capture supplier-specific lead times. This additional context can clarify if observed delays originate from initial supply constraints rather than downstream shipping processes alone."
  

This proactive recommendation ensures analysts can leverage deeper insights, incorporating business domain expertise that might not have been explicitly mentioned in the initial user query.

Case B: Deciding Between Saved Metrics or Creating Metrics from Scratch

When analyzing financial performance, analysts frequently encounter the decision to either reuse predefined, business-approved metrics (saved metrics) or create custom calculations from raw data.

• Context Used:
  "Calculated quarterly revenue using field values: SUM(l_extendedprice * (1 - l_discount))."
  
• Actionable Recommendation:
  "Your database includes an existing saved metric, quarterly_net_revenue, which already incorporates relevant taxes and adjustments. Consider utilizing this saved metric for consistency and compliance with your organization's financial reporting standards."
  

By explicitly highlighting the existence and suitability of saved metrics versus the creation of new calculations, the AI empowers analysts to align their queries with established business practices, thereby improving accuracy, saving time, and reducing potential inconsistencies in reporting.

3. Semantic Layer Knowledge

Detailed explanations of the underlying logic in metrics and table relationships used in a query help analysts understand the specific reasoning behind each calculation and data transformation.

When explainability exposes how these elements are used—or misused—it becomes possible to catch inconsistencies that may otherwise lead to incorrect analysis, decision-making errors, and loss of trust in data products.

‍Example from TPC-H dataset:

For market share analysis, the AI-generated query might indicate clearly:

• Query Decision:
  "Used SUM(l_extendedprice * (1 - l_discount)) to calculate supplier revenue."
  
  
• Actionable Recommendation:
  "You might consider also including l_tax in your calculation, as this impacts recognized revenue. Additionally, explicitly exclude canceled orders by adding a condition: WHERE o_orderstatus <> 'CANCELED'."

This detailed breakdown ensures analysts don't just know "what" the query is doing but also understand precisely "why" specific calculations and transformations were chosen, and how these choices align with their business rules.

4. Confidence Scoring at Granular level

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Accurate decision-making depends not just on explanations, but on knowing how much trust to place in each part of the AI’s output. A truly explainable system should provide confidence scores at a fine-grained level, along with clear justifications for those scores. This allows analysts to focus their attention on the parts of a query that are most uncertain or potentially problematic.

Rather than assigning broad confidence levels to an entire query, granular scoring highlights confidence for each sub-intent, table join, metric calculation, or filter condition—backed by transparent reasoning.

5. Error Understanding and Debugging Transparency

Even with well-constructed queries, execution failures can occur due to syntax errors, schema mismatches, missing fields, or unforeseen edge cases. Shallow reasoning AI agents often return only vague or technical error codes, leaving analysts to debug the root cause manually.

Actionable Explainability addresses this gap by explicitly surfacing 'Why did the error happen?'.

‍Example from TPC-H Dataset:

Question: "List the top 10 suppliers by total revenue in 2024."

The AI agent generates this SQL:

1SELECT s_suppkey, SUM(l_extendedprice * (1 - l_discount)) AS total_revenue
2FROM supplier
3JOIN lineitem ON s_suppkey = l_suppkey
4WHERE o_orderdate BETWEEN '2024-01-01' AND '2024-12-31'
5GROUP BY s_suppkey
6ORDER BY total_revenue DESC
7LIMIT 10;

However, query execution fails with an error:

ERROR: Column 'o_orderdate' does not exist.

Actionable Explainability in Response:

• Error Detected: o_orderdate is referenced, but the orders table was not joined, leading to a missing field error.
• Root Cause Diagnosis: Missing JOIN between lineitem and orders table. o_orderdate exists in orders, not lineitem.
• Recommended One-click Actions: The orders table contains the o_orderdate field needed for time-based filtering. Add the join to resolve the missing column errors and correct revenue aggregation for 2024.

This improves analysts’ ability to resolve issues quickly, minimizes downtime, and enhances trust in the AI system’s reliability.

User Experience (UX) for Explainability

Effectively communicating complex explanations, especially to non-technical users, requires thoughtful UX design. The interface should incorporate several principles from cognitive science research on information processing and decision-making:

Interactive Visualization Interface

The explanation interface must use a multi-layered approach to manage cognitive load:

1. Intent Visualization Layer: An interactive graph visualization shows relationships between query components, allowing users to focus on specific parts of complex queries.

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2. Semantic Layer: The interface breaks down complex SQL into conceptual building blocks (filters, aggregations, joins) with expandable details, allowing users to zoom in on areas of interest.

3. Version Control: When suggesting changes, the system shows side-by-side comparisons of the original and proposed changes by the human with highlighted differences.
   

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4. Progressive Disclosure: Explanations follow a progressive disclosure model, starting with a high-level summary and allowing users to drill into specific aspects.

Feedback Integration and Learning Loop

The UX must incorporate explicit feedback mechanisms:

1. In-Context Feedback: Users can provide feedback on specific explanation components (e.g., "This join recommendation was helpful" or "This intent was misunderstood").
   
   
2. Suggestion Acceptance: One-click implementation of suggestions with automatic query updates in versioning.

This feedback feeds into a learning loop that continuously improves the quality and relevance of explanations over time, improving the Accuracy of AI Agents.

Handling Ambiguity in Query Interpretation

AI-generated queries sometimes face inherent ambiguity, where a question or intent can be interpreted in multiple valid ways based on database structure or domain definitions. In these scenarios, providing a single, definitive explanation may not always be feasible.

For example, consider the query: "Show sales for high-value parts."
Here, "high-value" could mean parts with high retail prices or those with high total sales volume. Without clear user specification, the AI faces ambiguity.

To effectively handle such edge cases, the explainability system should transparently present both interpretations, clearly outlining the reasoning behind each:

• Interpretation A:
  High-value defined by retail price (parts.p_retailprice > 1000).
  
  
• Interpretation B:
  High-value defined by sales volume (total order value from lineitem).

Each interpretation would be accompanied by actionable recommendations on how to proceed or refine further. This dual-explanation approach empowers analysts to make informed decisions aligned closely with their specific business context and goals, significantly reducing potential misunderstandings and ensuring analytical accuracy.

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Connecty AI: Pioneering Actionable Explainability

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Connecty AI is at the forefront of implementing Actionable Explainability in Agentic AI data workflows. Their AI-powered analytics Agents go beyond generating SQL to provide a comprehensive explainability layer that makes complex queries transparent and improvable with Petabyte-Scale Performance.

Connecty AI's Approach

The system provides a comprehensive explanation interface with:

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1. Intent Decomposition:
   
   • Leverages the interactive graph to isolate individual query node.
   • Drills into specific nodes to uncover hidden decision paths, then tweak parameters or rerun sub-queries for instant, transparent feedback.
     
     
2. Context Retrieval with Recommendations:
   
   • Explains the “why” behind each data source, then swap in suggested alternatives with one click to broaden your analysis and eliminate blind spots.
   • Uses self-refining steps to surface overlooked tables or fields—empowering users to enrich results without manual trial-and-error prompting.
     
     
3. Dynamic Semantic Layer Explanation and Validation:
   
   • Expand each metric(filters, joins, aggregations, relationships) to see the exact formulas and logic used, enabling you to validate or adjust calculations before they impact downstream computation.
   • Suggested Changes with live SQL snippets to experiment with metric definitions with version control, ensuring every transformation aligns with your business logic.
     
     
4. Confidence Assessment on Context with Reasoning:
   
   • Confidence scores alongside concise, human-readable rationale.
   • Act on low-confidence flags by digging into the supporting reasoning, then refine data selections or adjust thresholds to boost overall query reliability.
     
     

Most importantly, Connecty AI provides actionable recommendations at every step—from context selection to metric extraction—enabling analysts to leverage Agentic AI capabilities while maintaining full control over quality.

Competitive Landscape: Positioning Connecty AI's Actionable Explainability

The market for Agentic AI deep Analysis tools is increasingly crowded, with several shallow reasoning AI Agents offering varying levels of explainability. Here's how Connecty AI's Actionable Explainability approach differentiates itself:

Comparison

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With this level of transparency and actionable self-refining steps, analysts can make informed decisions about how to refine the query, effectively combining their domain expertise with the AI's computational capabilities.

Conclusion: The Path Forward

As AI agents become increasingly integrated into enterprise decision-making, explainability isn't just a technical consideration—it's the foundation of trust, accuracy, and effective human-AI collaboration. By implementing Actionable Explainability frameworks, organizations can overcome the limitations of black-box AI and unlock the full potential of these powerful tools.

The research is clear: human oversight remains essential for AI accuracy, but that oversight is only possible when AI systems provide transparent, actionable explanations for their reasoning. By embracing this paradigm, we can move beyond the false dichotomy of human versus machine intelligence toward a truly collaborative future where each amplifies the strengths of the other. Organizations that embrace this approach will not only achieve greater accuracy in their analytics but will also build stronger data cultures where AI serves as a trusted partner rather than an inscrutable oracle.

Take Action

Book a demo to experience how your organization can leverage Actionable Explainability with Connecty AI to increase efficiency, accuracy, and trust in your AI-powered data analytics.