The re-emergence of agentic AI—intelligent agents capable of autonomously planning, making decisions, taking actions, and continuously adapting to their environment—marks a significant shift in AI. Yet, with this shift comes a crucial challenge: how do we ensure that these autonomous agents are truly trustworthy and reliably aligned with organisational goals?

At Rainbird, we’ve developed a distinct type of AI agent that complements probabilistic agents in these existing systems. Our agents are built for deterministic reasoning, delivering precise and explainable decisions that are guaranteed to be justifiable. As organisations deploy various forms of AI agents, Rainbird’s deterministic agents provide a critical capability: the ability to make decisions where accuracy and transparency cannot be compromised.

The Crisis of Trust in AI Systems

Most sectors are facing unprecedented change due to AI, and nowhere is this more prominent than in professional services where the risks are considered by many to be existential. For centuries, the professional services business model has rested on twin pillars: the billable hour and unwavering trust. While AI promises to dramatically reduce the cost and time needed for complex tasks—from legal document review to financial audits—its probabilistic and non-deterministic nature simultaneously threatens the foundation of trust these firms have spent generations building. As AI drives the cost of time-based work towards zero, firms must confront an uncomfortable reality: their future value will depend almost entirely on their ability to be trusted advisors.

Yet here lies a critical contradiction for professional services firms: while embracing probabilistic AI models is necessary to remain competitive, this approach risks introducing subtle errors that can silently propagate through their work. Such risks are not hypothetical—an incident where fabricated legal authorities generated by an AI system, such as ChatGPT, were presented in a tax tribunal highlights the dangers of relying on unverified AI outputs. The internet is full of reports of such problems. When errors slip past both AI and human checks, the damage extends beyond financial losses; it undermines the client relationship and strikes at the heart of the profession’s centuries-old commitment to accuracy and reliability.

The Dangerous Fallacy of Human Oversight

The common defence that ‘humans will verify AI outputs’ overlooks a crucial paradox in human psychology. Research into automation bias reveals a troubling pattern: the more reliable an AI system appears to be, the less likely humans are to thoroughly check its outputs. This creates a dangerous feedback loop where increased accuracy actually amplifies risk. As AI systems become more sophisticated and generate consistently reliable results, human operators naturally develop a sense of trust and complacency.

This erosion of vigilance means that when errors do occur—particularly in edge cases or novel situations—they’re more likely to slip through unnoticed. The irony is stark: the very success that makes AI systems valuable in reducing human workload simultaneously increases the potential impact of any undetected errors. This psychological blind spot makes human oversight an unreliable safeguard, particularly in high-stakes domains where a single missed error could have significant consequences.

Beyond Language Models: The Critical Gap in AI Decision Making

Recent advances in large language model (LLM) tooling have enabled AI agents to plan, interact with users, orchestrate tasks, and respond to complex queries. Yet even as these agentic systems become more sophisticated, a reliance on probabilistic outputs remains a significant shortcoming. Without a layer of deterministic reasoning, agents struggle to validate their conclusions, explain their actions, or demonstrate a causal logic chain from inputs to decisions.

Rainbird provides this missing link. Instead of simply generating predicted outputs, our platform empowers AI agents to operate over a structured model of the world, captured in knowledge graphs. This transforms agentic AI from a ‘guess and hope’ approach to one in which every outcome can be traced back to its logical foundations.

Why Deterministic Reasoning Matters for Agentic AI

To understand why deterministic reasoning is indispensable, we must examine the nature of agentic AI itself. Intelligent agents do more than answer questions: they take actions that may have real-world consequences. Whether it is automating complex workflows, researching tax treaties, or issuing financial recommendations, the stakes are high. In such scenarios, “good enough” may not be enough. Certainly, the market needs decision-making processes that can be fully understood, vetted, and trusted. At the very least, an agentic approach should allow developers to use “good enough” agents where appropriate and precise and deterministic agents when it really matters. 

By building decisions from explicitly modelled knowledge, Rainbird ensures that agents do not merely predict outcomes but instead derive logically rigorous conclusions from authoritative world models that were deliberately designed and approved. Whereas probabilistic systems will produce results with no clear justification—in most cases influenced by training that is based on the public internet—Rainbird shows exactly why each conclusion follows from the knowledge at hand. For enterprises that must meet rigorous compliance or regulatory standards, this is not just beneficial, it’s essential.

Mastering Contextual Complexity

Real-world applications often involve intricate interactions between multiple variables. Take healthcare scenarios evaluating treatment eligibility or insurance underwriting tasks that must consider risk factors, eligibility criteria, and multi-jurisdictional compliance; these challenges require sophisticated navigation of interconnected rules and relationships.

Rainbird’s knowledge graph approach captures these complexities explicitly, enabling agents to:

• Reason causally about interconnected conditions
• Understand how changes in one variable affect others
• Provide detailed justifications for decisions
• Maintain consistency across complex rule sets
• Process natural language inputs while maintaining deterministic outputs
• Transform human-readable policies into machine-executable logic
• Eliminate hallucinations through grounded reasoning
• Bridge the gap between human understanding and machine inference

Introducing Noesis: Accelerating Deterministic AI Development

The challenge of building deterministic AI agents has traditionally been the time and expertise required to create comprehensive knowledge graphs. Our new Noesis platform transforms this process, automatically converting organisational documentation and expertise into executable knowledge graphs in minutes rather than weeks. 

Noesis represents a step-change in the sophistication of deterministic AI agents, and how quickly organisations can deploy them. It automatically processes policy documents, procedures, and regulatory texts, transforming them into precise knowledge graphs while maintaining the rigorous logical structure that deterministic reasoning demands. This automated conversion of knowledge—from written and unstructured form to computable deterministic model—preserves the critical relationships and rules within that documentation while eliminating the manual effort traditionally required for knowledge graph creation.

The same technology is being used to automatically design interviews with domain experts, to elicit and encode layers of tacit knowledge into graphs that already understand a base level of regulation and policy.

Key capabilities include:

• Automated extraction of decision logic from existing documentation
• Built-in validation to ensure knowledge graph consistency
• Developer-friendly APIs and SDKs for seamless integration
• Comprehensive audit trails and explanation facilities
• Enterprise-grade security and compliance features

For developers, this means being able to rapidly create deterministic AI agents that can be trusted with critical decisions. 

Building the Future of Trusted AI

As AI agents become increasingly autonomous, the focus must shift from controlling unpredictable AI outputs to building inherently reliable AI agents. Rainbird’s decade-long experience in delivering deterministic AI reasoning to major enterprises demonstrates that trust and transparency aren’t optional extras—they’re fundamental requirements.

The future of AI lies not in probabilistic models constrained by guardrails, but in systems that think clearly and consistently from the outset. Through deterministic logic, comprehensive knowledge modelling, and explainable reasoning chains, Rainbird is making this future a reality.