Building Trust in Agentic AI: The Case for Model Supply Chain Transparency

Introduction

As AI systems transition from standalone models to autonomous, agentic systems, the need for trust, transparency, and risk-aware design has never been more critical. These intelligent agents, powered by Large Language Models (LLMs) and multi-agent orchestration, are increasingly making decisions that impact businesses, individuals, and society at large. However, trust in these systems cannot be assumed: it must be designed, measured, and continuously reinforced at the system level, not just at the model level.

One of the key enablers of AI trustworthiness is model supply chain transparency—a framework that allows organizations to assess and verify the provenance, safety, and alignment of AI components used in complex systems. Without clear insights into how AI models are built, trained, and deployed, it becomes nearly impossible to conduct a risk-based analysis of system requirements. This blog explores why model supply chain transparency is essential, how it supports risk alignment in agentic AI, and best practices for designing trustworthy AI ecosystems.

The Growing Complexity of AI Supply Chains

Modern AI systems are increasingly no longer monolithic; they are composed of multiple interconnected models, APIs, and components including external data sources and tools. This complexity introduces new risk factors, including:

• Data provenance uncertainty: Where did the training data come from? Was it biased or incomplete?
• Third-party model risks: Are external AI models compliant with ethical and regulatory standards?
• API dependencies: What happens if a function call retrieves unreliable or unsafe outputs?
• Automated decision opacity: Can stakeholders audit and intervene in AI-driven decisions?

These challenges underscore why model supply chain transparency is crucial. This is why it is important for our industry to standardise AI supply chain visibility, ensuring that models are built with accountability and risk alignment in mind.

Why Risk-Based Analysis is Critical for Agentic AI

Unlike traditional AI models that provide outputs on request, Agentic AI systems act autonomously based on high-level goals. This shift from reactive to proactive AI necessitates a new approach to risk assessment. Organisations deploying multi-agent orchestration and function calling frameworks must evaluate:

1. Predictability & Reliability – Can the system produce consistent, explainable results?
2. Human-in-the-Loop Controls – Are there mechanisms for human intervention and alignment?
3. Ethical Guardrails – How does the system align with human values and policies?
4. Adaptive Risk Mitigation – Can the AI adjust its behaviour based on evolving risks?

A risk-aligned AI system does not simply execute functions—it understands its limitations, communicates uncertainty, and allows for human oversight when necessary.

Best Practices for Enhancing AI System Trust

To ensure AI systems are trustworthy, organisations must embed safety measures at every stage of the AI lifecycle. The following best practices can help:

1. Model Lineage and Explainability: Model lineage ensures transparency by tracking an AI model’s entire lifecycle—from data sources to deployment—allowing for bias detection and accountability. Explainability provides clear, interpretable insights into AI decisions, helping users understand and trust the system’s outputs.
2. Risk-Aware Agentic Orchestration: To prevent unintended behaviors, agentic AI systems must include safeguards like prompt mediation (validating inputs), output mediation (filtering responses), and task grounding (ensuring AI stays within scope). These mechanisms help align AI actions with human expectations and safety standards.
3. Human-in-the-Loop Governance: Even in autonomous AI, human oversight is essential to prevent errors and unintended consequences. Implementing real-time intervention controls and fail-safe mechanisms ensures that AI actions can be monitored, corrected, or overridden when necessary.
4. Transparent AI Supply Chains: AI systems should be built on verifiable, auditable components to ensure trust and accountability. Organisations must track model provenance, assess third-party AI risks, and use open-source frameworks to enhance transparency in AI development and deployment.

By integrating these practices, organisations can proactively design for trust rather than retrofit safety features after deployment. Looking at this in terms of established implementation patterns such as the Emerging Patterns in Building GenAI Products by Martin Fowler and Bharani Subramaniam from ThoughtWorks, embedding elements and best practice for trust by design is going to be increasingly important in successful deployment of AI at Enterprise scale in the years to come.

Conclusion: Trust as a System-Level Imperative

As AI transitions from models to systems, organizations must adopt a holistic approach to trust and transparency. This requires:

• Model supply chain transparency to assess and verify AI components.
• Risk-based system analysis to anticipate failures and mitigate biases.
• Proactive design patterns that enforce safety, fairness, and accountability.

Ultimately, trust is not a feature, it is a foundation. To ensure AI systems are safe, effective, and aligned with human values, we must design for trust at every level—from data and models to decisions and deployments.