Agentic AI: Part 5— Trust in Agentic AI
The Cornerstone of Adoption
Why Trust Is the Cornerstone of Agentic AI Adoption?
Trust is the invisible currency of every industry.
Customers trust that services will be delivered when they need them most.
Partners trust that organisations will respond quickly and fairly.
Regulators trust that businesses operate ethically and transparently.
As we embrace Agentic AI, autonomous agents that reason, decide, and collaborate, the role of trust shifts from important to absolutely foundational. These agents don’t just suggest; they act. They extract data, assess risks, make decisions, and even escalate issues to compliance checkpoints.
But here’s the real question:
Would you trust an AI agent to make a high-stakes decision without human intervention?
In insurance, for example, this might mean trusting an AI agent to underwrite a multimillion-dollar property policy without manual review.
Why Trust Matters More in Agentic AI
In the past, most AI operated within safe boundaries. Predictive models provided insights, dashboards offered advice, and humans made the final call. AI was a tool, not a decision-maker.
Agentic AI changes that dynamic.
These systems don’t just analyse, they act. Agents can autonomously initiate workflows, trigger approvals, interact with customers, and collaborate with other agents. This autonomy delivers speed, scale, and consistency that humans alone cannot match. Yet, without trust, the very efficiency of Agentic AI becomes irrelevant. Organisations will hesitate to adopt, users will resist relying on outcomes, and regulators will scrutinise every decision.
Insurance provides a clear illustration.
- Underwriters need confidence that AI recommendations reflect sound reasoning, not just opaque calculations.
- Brokers must feel assured that fast, automated responses are also accurate and fair.
- Customers expect decisions that are transparent and unbiased, especially when policies affect their livelihoods.
- Regulators demand traceability and proof that automated processes comply with industry standards.
Trust, then, is not a nice-to-have anymore.
It is the keystone that holds the entire Agentic AI ecosystem together.
The Five Dimensions of Trust in Agentic AI
Transparency
Transparency means that AI agents must not operate as “black boxes.” Users need to understand how a decision was reached, what data was used, and which reasoning steps contributed to the outcome.
In insurance, instead of an underwriting agent simply returning “declined,” the system should provide the reasoning:
“Declined due to flood exposure in Zone A and incomplete fire safety measures.”
In healthcare, a diagnostic AI should highlight the specific test results or image patterns that triggered an alert, not just deliver the alert itself.
Accountability
Accountability ensures every action taken by an AI agent can be traced to an identifiable entity with a defined role and authority scope. Without accountability, responsibility becomes blurred in multi-agent ecosystems.
In insurance, if a pricing decision is challenged, the insurer should be able to pinpoint: which agent generated the price, which data sources were used, and which business rules were applied.
In logistics, accountability might identify whether a Routing Agent or Weather Enrichment Agent caused a shipment delay.
Security
Agentic AI systems are highly interconnected, which increases the surface area for risks like unauthorised access, privilege escalation, or impersonation. Security must be proactive, extending zero-trust principles to every agent interaction.
An Underwriting Agent requesting customer financial records should not automatically gain access. The system must check both identity and authority for that specific task.
Similarly, in banking, a Trading Agent must not bypass approval limits even if it sits within the firm’s own environment.
Consistency
Trust requires that similar situations lead to similar outcomes. Inconsistent results create perceptions of unfairness and unreliability, undermining adoption.
Two brokers submit nearly identical property risks. If one is quoted at $10,000 and another at $14,000 with no clear explanation, trust erodes instantly.
Outside insurance, a customer service bot that approves refunds for some customers but denies them for others with identical claims will face backlash.
Ethics & Fairness
Agentic AI must align not just with technical accuracy but with broader societal expectations of fairness, privacy, and non-discrimination.
In insurance, an underwriting agent should not disproportionately penalise certain postcodes or demographics because of biased training data.
In HR, an AI hiring agent must avoid amplifying historical discrimination against certain groups.
Trust Breakdowns: What Can Go Wrong
- Opaque Decisions: Regulators and brokers can’t explain outcomes.
- Accountability Gaps: No clear owner when things go wrong.
- Security Risks: Rogue or compromised agents gaining access.
- Unpredictability: Agents deviating from intended purpose.
- Reputational Risk: Customers perceiving unfair or biased treatment.
Each breakdown not only threatens adoption — it threatens brand reputation and customer loyalty.
Engineering Trust into Agentic AI
Building trust is not about a single control but a set of principles woven into the design of every system. Here are six foundational ways to embed trust into multi-agent ecosystems:
Key principles:
Identity Anchoring: Knowing Who’s Who
Every agent must carry a verifiable identity, just as every employee has a secure login. This prevents impersonation and ensures each decision can be traced back to a legitimate source.
In insurance, a Pricing Agent should have its own digital identity, so that when regulators ask who generated a premium, the answer is precise — not “the system,” but “this agent at this point in time.”
In a hospital, a diagnostic AI could be tied to a unique identity so that its outputs can be separated from other support systems.
Context-Aware Authorisation: Right Access at the Right Time
Instead of blanket permissions, agents should only access the data and tools relevant to their role and the context they’re operating in.
An Underwriting Agent may be allowed to view building safety data but not detailed customer credit history, unless the risk level explicitly requires it.
Similarly, in finance, a Trading Agent might execute trades up to a certain threshold, but anything beyond that requires human approval.
Immutable Audit Trails: Leaving No Gaps
Every action an agent takes must be recorded in an unchangeable log. This creates a decision history that can be reconstructed, audited, and explained.
If a broker challenges why a quote was unusually high, the insurer can replay the entire decision path, data sources, risk enrichment steps, and final calculation.
In healthcare, audit trails can show why a diagnostic AI flagged a patient for urgent care, ensuring accountability to both clinicians and regulators.
Human-in-the-Loop: Balancing Autonomy with Oversight
Not every decision should be fully automated. Critical, ambiguous, or high-stakes cases must be escalated to human experts.
A property policy worth a few thousand dollars might be auto-approved, but a multimillion-dollar commercial submission should still land on an underwriter’s desk.
In aviation, an autonomous scheduling system might optimise crew rosters automatically but escalate exceptions like safety regulation conflicts to human managers.
Dynamic Trust Scoring: Continuous Performance Check
Trust is not static; it must be monitored and recalibrated. Agents should be scored dynamically based on accuracy, fairness, and compliance, with scores influencing how much autonomy they’re granted.
If a Data Extraction Agent consistently misreads fields in broker submissions, its trust score falls, and its outputs require human verification.
In contrast, an agent with a strong track record of accuracy and fairness may be allowed to handle more cases automatically.
Resilience & Containment: Assuming Failure, Designing for Safety
Systems should be designed on the assumption that failures will happen. If one agent misbehaves or is compromised, its access and permissions should be revoked instantly without bringing down the entire ecosystem.
In insurance, if a Pricing Agent suddenly begins producing unusually high premiums due to a faulty data feed, containment mechanisms should immediately suspend its outputs while other agents such as data extraction or triage should continue to function normally. This prevents flawed quotes from reaching brokers and damaging trust.
In cybersecurity, if one threat-monitoring agent is breached, its access should be cut off immediately to prevent a ripple effect across the network.
The Business Payoff of Trust
Trust is more than risk mitigation. It is a competitive advantage.
In insurance, trusted Agentic AI delivers:
- Faster quotes → stronger broker loyalty.
- Transparent rationale → regulator confidence.
- Consistent outputs → improved conversion rates.
- Fair decisions → higher customer satisfaction.
In a market where responsiveness and fairness are differentiators, trust is a business strategy, not a compliance checkbox.
Conclusion: Trust as the First Pillar
Trust is not just an abstract principle. It is the bedrock on which Agentic AI adoption stands. Without it, insurers risk building fragile systems that brokers hesitate to use, customers fail to believe in, and regulators challenge at every turn.
With it, however, Agentic AI becomes a true differentiator: accelerating decisions, strengthening relationships, and creating fairness at scale.
But trust alone is not enough. To sustain it, insurers must be able to see and prove how their AI agents are making decisions.
Transparency at the design level must be matched by transparency in real-time operations.
This is where End-to-End Observability comes in, the scaffolding that makes trust durable, measurable, and actionable.
In the next blog, we’ll explore how observability unlocks continuous assurance in Agentic AI.
Together, these dimensions ensure insurers can answer the critical question:
“Do we truly know what our AI agents are doing and why?”
