Trust Serves as the Foundational Backbone of AI

Co-authored by Manish Sood and Venkat Venkatraman

Every enterprise discussion about agentic AI inevitably raises the question of whether the model is good enough—an incorrect premise. Model performance has advanced faster than many experts have predicted.1 What remains unsolved is trust: confidence in the data an agent reasons over, confidence in the context it uses to make decisions, and confidence that its reasoning can be inspected and, if necessary, reversed.2

Coagency: A New Kind of Decision-Maker

In *Agentic Intelligence*, we introduce the concept of “coagency,” meaning that deploying an agent capable of acting—not merely recommending—creates a new type of joint decision‑maker. The agent is not a tool awaiting a human’s trigger pull; it is a participant. Like any participant in a relationship, human or otherwise, it earns authority through demonstrable reliability, not through specifications. Coagency requires that both the human and the agent can observe sufficient of each other’s reasoning to uphold their responsibilities. This is fundamentally a design challenge, not a model challenge. It cannot be added after the agent is in production; it must be embedded from the outset, capturing the origin of each claim, the basis for its weighting, and the protocol for handling disagreement.

Lineage and Provenance Aren’t Compliance Checkboxes

Enterprises often err by purchasing “agentic AI” as a feature rather than implementing it as a capability. After two decades of watching organizations consolidate customer, product, and operational data, a consistent pattern emerges: the failing technology is not the one with the weakest algorithm, but the one that lacks sufficient trust to be acted upon. If a sales representative receives an agent recommending a discount, their immediate question is not “how intelligent is this model” but “where did this figure originate, and can I see the account history that supports it?” A black‑box response will halt adoption regardless of the recommendation’s accuracy.

Contestability: The Right to Push Back

The second element is contestability. Trust requires not only insight into a decision’s origin but also a genuine mechanism for challenging it. A system that cannot be questioned is merely tolerated, and such systems are eventually circumvented and abandoned. Contestability ensures that an agent’s reasoning is transparent, allowing a human to request “explain this” and receive a response grounded in the original logic, rather than a post‑hoc justification crafted to appear plausible.

Override Rights: Designing for the Undo

That’s why override rights are as crucial as automation itself. Organizations deriving genuine value from agentic AI are not those that relinquish the most control; they are those that implement clean, efficient, well‑defined procedures for a human to intervene, pause, or reverse actions. Override is not a tolerated failure mode; it is a deliberate feature, essential for granting real authority to the system. No one surrenders control without the ability to reclaim it.

Trust Is the Architecture

Agentic AI will be won not by the entity with the most powerful model, but by the one that constructs systems with visible lineage, verifiable provenance, contestable decisions, and genuine human override rights—where coagency is the foundational architecture, not an afterthought. Trust forms the underlying infrastructure for all else; without it, even the most capable agent will remain idle.

1. Forecasting Research Institute, Near-Term AI Forecasting Accuracy (2025), which found that AI experts and superforecasters systematically underestimated progress across multiple AI benchmarks; Stanford Institute for Human-Centered Artificial Intelligence, AI Index Report 2026, which documents rapid gains in frontier model performance across software engineering, mathematics, science, and multimodal reasoning.

2. McKinsey & Company, Building the Foundations for Agentic AI at Scale (2026), which argues that reliable agentic systems depend on high-quality, interoperable data with strong lineage, governance, access controls, and auditability; Deloitte, Trust Is Main Barrier to Agentic AI Adoption in Finance and Accounting (2026), reporting that trust in the accuracy and reliability of AI agents is the leading barrier to adoption and that only 2.7% of surveyed professionals were comfortable allowing agents to make autonomous judgment calls.

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