As AI agents gain unprecedented autonomy in enterprise environments, a critical question emerges: How do we build trust in systems that can make decisions without human oversight? The answer lies in explainability and transparency, two foundational principles that separate production-ready AI from experimental prototypes.
In a recent expert panel discussion, four seasoned AI practitioners shared insights that every enterprise should understand before deploying autonomous agents. Their combined experience across finance, healthcare, technology, and data engineering reveals both the immense potential and sobering challenges of building trustworthy agentic systems.
“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.
This insight captures the essence of why explainability has moved from a nice-to-have feature to a business-critical requirement. When AI systems operate autonomously, particularly in regulated industries, the black box approach simply doesn’t work.
Pankaj Agrawal, Staff Software Engineer at LinkedIn, reinforces this point from an operational perspective: “Even with agentic AI, you need to ensure the agent has taken the steps it was supposed to. It shouldn’t deviate from the graph it’s meant to follow.”
The implications are stark. In highly regulated environments, unexplainable AI decisions can lead to compliance violations, financial penalties, and erosion of stakeholder confidence. More fundamentally, they prevent organizations from learning and improving their systems.
While discussions about AI often center on ethical considerations, Dan Chernoff, Data Scientist at Parallaxis, offers a more pragmatic perspective: “I don’t think it’s necessarily about ethics. It’s about governance and how your systems align with the rules that apply in your environment.”
This governance-first approach provides a clearer framework for enterprise decision-making. Organizations need systems that can:
Keshavan Seshadri, Senior Machine Learning Engineer at Prudential Financial, adds a global regulatory perspective: “The EU AI Act tells us what counts as acceptable risk, low risk, high risk, and what’s completely unacceptable.”
Building transparent AI systems requires breaking down traditional organizational silos. As Dan Chernoff explains, “It has to be a coalition of people. Not just to define what we’re building, but to ensure it helps both the customer and the business, and that it’s safe and observable.”
LinkedIn has operationalized this collaborative approach by creating playgrounds where business users can interact with AI models before deployment. “We created a playground for business users to play with prompts,” Pankaj Agrawal shares. “That way, they can see what the model produces and what its limitations are.”
This hands-on approach serves multiple purposes:
The complexity of modern enterprises demands tailored approaches to AI governance. Saradha Nagarajan emphasizes this point: “If you’re solving a problem in healthcare, which is high-risk and highly regulated, your guardrails have to reflect the domain-specific needs.”
Even general-purpose AI systems require domain-aware safeguards. The key is building governance frameworks that can adapt to different risk profiles while maintaining consistent standards for transparency and accountability.
As AI systems become more sophisticated, they increasingly involve multiple agents working together. This introduces new challenges for explainability. “When agents are interacting with each other, you can get outputs that were never anticipated,” warns Saradha Nagarajan. “That’s the danger of emergent behavior.”
These emergent behaviors aren’t just theoretical concerns. In dynamic environments, interacting agents may:
The solution involves implementing structural guardrails that maintain system behavior within acceptable bounds, even when individual agents operate with considerable autonomy.
The panelists outlined several concrete approaches for building explainable AI systems:
Architectural Transparency: Design systems as “glass boxes” rather than “black boxes,” where internal decision-making processes are visible and traceable from the start.
Tool-Level Monitoring: Track which specific tools and functions agents invoke in response to prompts. This granular visibility helps identify both appropriate and inappropriate system behaviors.
Audit Trail Implementation: Maintain comprehensive logs of agent interactions, decisions, and outcomes. As Dan Chernoff notes, “You need an audit trail, because when questions come up, and they will, you need a way to trace what happened.”
Evaluation Frameworks: Rather than focusing solely on prompt optimization, build rigorous testing and validation systems. “The key differentiator now is evaluations,” according to Pankaj Agrawal.
Despite advances in autonomous capabilities, today’s most successful AI implementations maintain meaningful human involvement. “Think of it like a chatbot for finance,” suggests Saradha Nagarajan. “The agent might sift through your financial documents and answer questions like ‘What were earnings in Q1 2024?’, but a human is still in the loop, making the judgment call.”
This partnership model recognizes both the strengths and limitations of current AI technology. Humans provide contextual judgment, ethical oversight, and strategic direction, while AI systems handle data processing, pattern recognition, and routine decision execution.
The path to trustworthy autonomous AI requires balancing innovation with responsibility. Organizations that succeed will likely:
As autonomous AI systems become more powerful and pervasive, explainability and transparency aren’t just technical nice-to-haves. They’re business imperatives that determine whether AI initiatives deliver sustainable value or become expensive experiments.
The organizations that master these principles will gain a significant competitive advantage: AI systems that stakeholders trust, regulators approve, and businesses can confidently scale. Those that don’t may find their most promising AI investments stalled by questions they can’t answer about systems they can’t explain.
The future belongs to transparent AI. The question isn’t whether explainability matters, but whether your organization is prepared to build the governance frameworks, collaborative processes, and technical capabilities needed to make it a reality.
25 September 2025
