Building Trust in AI Agents: Transparency and Explainability

AI agents can deliver remarkable results, but only if people trust them. Without trust, users second-guess every decision, stakeholders demand excessive oversight, and the promised efficiency gains evaporate.

Transparency and explainability are the keys to building that trust. Here is how to get them right.

Why trust matters more for agents

Traditional software is predictable. Given the same inputs, it produces the same outputs. Users learn its behaviour and trust follows from experience.

AI agents are different. They reason, adapt and sometimes surprise. This flexibility is what makes them powerful - but it also makes them harder to trust. People cannot simply memorise what the agent will do; they need to understand how it thinks.

Trust is not optional. Without it, adoption stalls, workarounds emerge, and the investment in agentic workflows fails to pay off.

Transparency: showing what the agent does

Transparency means making the agent’s actions visible. Users and supervisors should be able to see:

• What the agent decided to do.
• What data it used to make that decision.
• What systems it interacted with.
• What the outcome was.

This does not require exposing every technical detail. A well-designed audit trail, accessible through a simple interface, is usually enough. The goal is to answer the question: “What did the agent do and why?”

Practical approaches include:

• Activity logs that summarise actions in plain language.
• Dashboards showing key metrics and recent decisions.
• Alerts when the agent takes unusual or high-impact actions.

Explainability: helping people understand

Explainability goes a step further. It is not enough to show what the agent did; people need to understand the reasoning behind it.

This is harder than it sounds. AI reasoning can be complex, probabilistic and non-linear. Dumping a technical explanation on a business user does not help.

Effective explainability focuses on the audience:

• For end users: Simple, action-oriented explanations. “I routed this ticket to the billing team because it mentions an invoice query.”
• For supervisors: Summaries of patterns and exceptions. “This week, 12 cases were escalated because the agent was uncertain about the customer segment.”
• For auditors: Detailed logs with timestamps, data sources and decision criteria.

Different audiences need different levels of detail. Design your explainability layer accordingly.

The right level of transparency

More transparency is not always better. Flooding users with information creates noise and undermines the efficiency gains you were seeking.

The right level of transparency depends on:

• Risk: High-stakes decisions warrant more visibility.
• Familiarity: New agents need more explanation; mature ones can fade into the background.
• Regulation: Some industries require detailed audit trails regardless of risk.

Start with more transparency than you think you need, then dial it back as trust builds.

Building explainability into your agents

Explainability should not be an afterthought. It needs to be designed in from the start.

When defining your agentic workflows, ask:

• What decisions will users want explained?
• What data and reasoning should be captured at each step?
• How will explanations be surfaced - inline, on demand, or in a report?

Many modern AI platforms offer built-in tracing and logging features. Use them. They make explainability far easier than trying to reconstruct agent behaviour after the fact.

Handling uncertainty and mistakes

Agents will sometimes be uncertain. They will occasionally make mistakes. How you handle these moments has a huge impact on trust.

For uncertainty, consider:

• Surfacing confidence levels alongside decisions.
• Escalating to humans when confidence is low.
• Explaining what additional information would help.

For mistakes:

• Acknowledge them quickly and clearly.
• Explain what went wrong and what has been done to prevent recurrence.
• Make it easy for users to correct the agent’s output.

People are surprisingly forgiving of errors - as long as they understand what happened and see that the system is improving.

The regulatory dimension

Regulators are increasingly interested in AI transparency. Depending on your industry, you may face specific requirements around explainability, auditability and human oversight.

Treat these requirements as a floor, not a ceiling. Compliance is necessary, but genuine trust requires going further - making transparency and explainability part of the culture, not just the audit file.

Trust as a competitive advantage

Organisations that get transparency and explainability right will move faster. Their people will adopt agentic workflows with confidence. Their customers will accept AI-driven interactions. Their regulators will have fewer concerns.

In a world where AI agents are becoming ubiquitous, trust is the differentiator. Build it deliberately, and it will pay dividends.