AI agents are fundamentally different from traditional software. According to Ushur, treating AI agents like standard software—something to buy, configure, and “set and forget”—can create challenges, especially in regulated industries such as healthcare, financial services, and insurance. Unlike deterministic software, AI agents interpret, reason, and act within environments, making them function more like digital co-workers than hard-wired applications.
Traditional enterprise software follows fixed business rules and predictable workflows. The outputs are consistent, which made governance straightforward.
AI agents, on the other hand, operate differently: they reason across context, interpret language, and make decisions that may vary depending on the situation. Introducing an AI agent is similar to bringing a new team member into a workflow—it requires clear guardrails, thoughtful oversight, and trust structures.
Treating AI agents exactly like traditional software can be risky. Their behavior is probabilistic, dynamic, and context-sensitive.
Responses may differ based on inputs or evolving models, creating potential compliance challenges if governance is not adapted to these fluid decision patterns. Key considerations include opaque reasoning, dynamic behaviour, regulatory exposure, and context sensitivity, all of which require oversight beyond static rules.
AI agents as digital co-workers
The most effective way to think about AI agents is as digital co-workers. They interpret context, make judgements, and act autonomously, requiring structures similar to those provided to human employees. Enterprises should:
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Define roles, responsibilities, and limits
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Provide training, context, and examples
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Establish supervision and feedback loops
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Create escalation paths for uncertainty
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Apply guardrails to maintain predictable, explainable behaviour
This mindset shift—from installing software to onboarding a co-worker—builds trusted enterprise AI and ensures decisions align with organisational goals.
Building trust at every level
Trust in AI agents is not universal; it must be cultivated for each stakeholder group:
Executives need confidence that agents deliver measurable outcomes, align with strategy, and operate responsibly. Key enablers include KPIs, governance visibility, and clear ownership of agent performance.
Operators need observability and control. Transparent logs, configuration-based management, safe testing environments, and policy controls are critical to trust.
Internal users require reliability and clear escalation mechanisms. They benefit when agents handle routine tasks accurately and pause when human intervention is needed.
End customers seek clarity, accuracy, and security. AI agents must deliver timely, respectful, and compliant interactions that protect data privacy.
Context, permission, and predictability
For AI agents to operate effectively as digital co-workers, they need three key elements:
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Context: Access to customer history, business rules, and regulatory details ensures informed decision-making.
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Permission: Defined boundaries prevent unsafe actions, while still enabling automation.
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Predictability: Stable prompts, policies, testing, and monitoring build confidence in consistent performance.
These elements collectively make AI agents reliable contributors to workflows, improving both operational efficiency and customer experience.
How Ushur operationalises trustworthy AI agents
Ushur’s AI-powered Customer Experience Automation™ platform helps enterprises introduce AI agents as governed digital co-workers. The platform ensures that AI agents have clear roles, contextual awareness, human-in-the-loop oversight, and observable behaviour, while allowing rapid no-code configuration. Key benefits include:
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Role definition and guardrails for each agent
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Context-rich, policy-aligned reasoning
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Transparent logs and behavioral insights
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Safe, operator-controlled deployment
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Compliance and operational risk reduction
By treating AI agents as digital co-workers rather than software, enterprises gain automation benefits without compromising trust or regulatory alignment.
Read the full blog from Ushur here.
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