As artificial intelligence systems gain unprecedented autonomy, a fundamental challenge emerges: how do we understand what these agents are thinking and why they make specific decisions? The rise of agentic AI has brought this question into sharp focus, transforming explainability from a nice-to-have feature into a business-critical requirement.
Recent developments in autonomous AI agents have created what experts are calling an “explainability crisis.” Unlike traditional AI systems that perform single, bounded tasks, agentic AI systems can perceive, reason, act, and iterate autonomously across complex workflows. This leap in capability comes with an equally dramatic increase in opacity, making it harder than ever to understand how these systems arrive at their conclusions.
The implications of this challenge extend far beyond academic curiosity. In enterprise environments, where agentic AI is increasingly being deployed for mission-critical tasks, the inability to understand agent decision-making creates significant risks for compliance, governance, and trust.
Consider a wealth management firm using AI agents to prepare personalized investment reports for hundreds of clients. These agents pull client data from multiple systems, craft investment strategies based on market trends, generate compliance-ready communications, and route them for approval, all without human intervention. When regulators ask why a particular investment recommendation was made, the traditional “black box” response is no longer acceptable.
“The trust and adaptability you have in the data or predictions from an agentic AI model is much better when you understand what’s happening behind the scenes,” explains Saradha Nagarajan, Senior Data Engineer at Agilent Technologies, speaking at a recent industry panel on AI transparency.
Traditional machine learning models, while often complex, typically operate within well-defined parameters. A credit scoring model evaluates specific data points and produces a risk assessment. Even when the internal workings are opaque, the inputs and outputs are clear and bounded.
Agentic AI systems operate differently. They can:
This complexity multiplies the explainability challenge exponentially. When an agent makes a decision after a multi-hop reasoning process involving several tool calls and data sources, tracing the decision back to its origins becomes extraordinarily difficult.
“When agents are interacting with each other, you can get outputs that were never anticipated,” warns Nagarajan. “That’s the danger of emergent behavior.”
The challenge becomes even more pronounced in multi-agent systems, where several AI agents work in concert. In these environments, agents may:
Dan Chernoff, Data Scientist at Parallaxis, describes the fundamental problem: “We haven’t really created guardrails for when the system hits something it can’t interpret. That’s where observability becomes critical, so we can trap those issues and evolve the system accordingly.”
Forward-thinking organizations are already grappling with these challenges, developing new approaches to make agentic AI more transparent and auditable.
Pankaj Agrawal, Staff Software Engineer at LinkedIn, advocates for what he calls a “glass box” approach: “It’s about whether your system is a black box, where you can’t see inside, or a glass box, where the internal decision-making is fully visible and traceable.”
This shift requires fundamental changes in how agentic systems are designed. Instead of treating explainability as an afterthought, developers are building transparency into the core architecture of their agents.
One emerging best practice involves tracking every tool invocation and data access made by an agent during its decision-making process. As Agrawal notes: “As a developer or system designer, I need to know what tools are called. Should a calculator be used for a simple question like one plus one? Absolutely not. But if it is, I want to see that, and understand why.”
This granular visibility enables developers to:
Some organizations are implementing dedicated audit agents whose sole purpose is to monitor and explain the behavior of other agents in the system. Keshavan Seshadri, Senior Machine Learning Engineer at Prudential Financial, describes this approach: “You can have an agent that audits everything, from input to prompts and to responses, creating a rich audit trail.”
These supervisory systems provide multiple benefits:
Several technical approaches are emerging to address the explainability challenge in agentic AI:
Advanced agentic systems are implementing detailed logging of their reasoning processes, creating human-readable explanations of their decision-making steps. This includes:
Some organizations are adopting hybrid architectures that clearly separate deterministic operations (like database queries or calculations) from generative processes (like text creation or creative synthesis). This separation makes it easier to validate the accuracy of factual components while still allowing for creative AI capabilities.
Rather than generic explanations, advanced systems are developing domain-specific explanation frameworks. A financial services agent explains its decisions in terms familiar to compliance officers, while a manufacturing agent uses the language of quality control and production optimization.
The explainability imperative is being reinforced by regulatory developments worldwide. The EU AI Act explicitly requires explainability for high-risk AI applications, while financial regulators in multiple jurisdictions are demanding transparency in algorithmic decision-making.
“Europe has always been the front-runner on regulation,” notes Seshadri. “The EU AI Act tells us what counts as acceptable risk, low risk, high risk, and what’s completely unacceptable.”
This regulatory environment is pushing organizations to proactively address explainability rather than waiting for compliance requirements to force their hand.
Creating transparent agentic AI systems requires a multi-faceted approach combining technical innovation with organizational commitment:
Organizations must prioritize explainability from the earliest stages of agent development. This includes:
Robust evaluation frameworks are essential for ensuring agent reliability and explainability. As Agrawal emphasizes: “You need a solid eval set to ground your agents, especially when LLMs, prompts, or other variables change underneath you.”
These frameworks should test not just accuracy but also the quality and comprehensibility of explanations provided by the agents.
Building explainable AI requires expertise from multiple domains. Technical teams must work closely with:
25 September 2025
