Amazon recently announced that they have extended their automated reasoning checks in Bedrock Guardrails. This acknowledges what we at Rainbird have long advocated — the critical importance of moving beyond pure probabilistic AI to achieve true reliability and trust in AI systems.
AWS’s move demonstrates the increasing market demands for more rigorous AI validation, although it’s crucial to understand the distinction between implementing guardrails and achieving truly deterministic AI reasoning.
Let’s explore why this all matters.
Understanding the Evolution: AWS’s Approach vs True Reasoning
AWS’s approach focuses on validating LLM outputs against predefined rules and policies through what they call “Automated Reasoning policies.” These policies can be extracted from a simple policy document and can operate on a set of variables and logical rules that are translated into natural language for accessibility.
However, there are several key distinctions between AWS’s validation approach and true deterministic reasoning:
Static Validation vs Dynamic Reasoning
AWS Bedrock Guardrails implements an “allow-all” approach with selective validation. This means the system attempts to “catch” hallucinations post LLM where explicit policies are defined.
In contrast, Rainbird reasons over a world model that you get to design and control, captured in a knowledge graph. Decisions are explicitly derived from the graph, not via a probabilistic LLM, so no predicted outputs, just reasoned answers.
This amounts to a ‘deny all’ approach, with only verifiable decisions being created – a fundamental difference is crucial for applications where determinism, precision and completeness are non-negotiable.
Limited vs Complete Reasoning Chain
Whilst AWS’s solution can validate final outputs, it fundamentally operates as a validation layer on top of probabilistic LLM outputs. An LLM predicts an answer based on a distribution of probabilities, and then a rule checks if that prediction matches predefined criteria. This approach can catch obvious errors, but it cannot provide insight into how conclusions were reached or validate the reasoning process itself.
In contrast, Rainbird’s knowledge graphs enable true causal reasoning from first principles. Rather than validating probabilistic outputs after the fact, our system builds deterministic logic chains that can explain not just what decision was made, but precisely how and why each step in the reasoning process was taken. This delivers complete visibility and validation of the entire decision-making process, not just its conclusion.
This distinction becomes particularly important in complex decision-making scenarios where multiple policies or rules interact.
While a validation approach can check individual rules, it cannot understand or reason about their relationships and dependencies. For example, when evaluating eligibility for a financial product, multiple qualifying conditions might interact in subtle ways — income thresholds might vary based on employment status, which in turn could be affected by residency requirements.
A validation-only system can check each rule in isolation but struggles to handle these interconnected relationships.
In contrast, a true reasoning system built on knowledge graphs can navigate these complex relationships naturally. Because the knowledge is explicitly modelled as an interconnected graph rather than a list of validation rules, the system can trace multiple paths through the knowledge, understand how different conditions influence each other, and arrive at conclusions that respect the full complexity of the domain.
This means not just more accurate decisions, but also the ability to explain precisely how different factors influenced the outcome and why alternative paths weren’t taken.
The Rainbird Difference: Beyond Validation to True Reasoning
Whilst guardrails can help prevent errors that can be pre-determined, Rainbird’s approach fundamentally transforms how AI makes decisions. Our technology doesn’t just validate outputs – it represents rules as weighted knowledge graphs that unlock true causal reasoning. This means:
- Deterministic by Design: Rather than attempting to constrain probabilistic outputs, our systems are inherently deterministic. Every decision follows explicit, traceable logic paths.
- Complete Causal Chains: Instead of simply checking if outputs match predefined rules, Rainbird provides complete causal proof for every decision, showing exactly how each conclusion was reached.
- Knowledge-First Architecture: Our approach begins with structured knowledge representation, enabling organisations to represent regulations, policy and operating procedures as precise models that can reason, rather than attempting to retrofit precision onto probabilistic models.
The Power of Neurosymbolic AI
What truly sets Rainbird apart is a neurosymbolic approach, combining the best of symbolic reasoning with modern machine learning. This hybrid architecture:
- Enables precise reasoning over complex knowledge domains
- Provides complete auditability of decision processes
- Maintains deterministic outputs whilst handling natural language inputs
- Eliminates hallucinations through structured knowledge representation
Looking Forward: Noesis and the Future of Trusted AI
With our new Noesis platform, we’re taking this proven approach even further. Noesis will enable developers to:
- Automatically convert unstructured documents into executable knowledge graphs
- Deploy deterministic reasoning capabilities through simple API calls
- Integrate trusted AI capabilities into existing ML pipelines
- Generate complete audit trails for every decision
The Broader Implications
AWS’s move into automated reasoning validation demonstrates growing market recognition of what Rainbird has been delivering for years: the need for more reliable, explainable AI in enterprise settings.
However, true trust in AI requires more than guardrails – it demands systems built on deterministic reasoning from the ground up.
As organisations increasingly rely on AI for critical decisions, the ability to provide not just guardrails but complete causal reasoning is becoming more critical. This is where Rainbird’s decade of experience in delivering deterministic AI solutions to major enterprises has proven invaluable.
Whilst we welcome AWS’s recognition of the importance of automated reasoning in AI systems, the future demands more than validation layers. It requires AI systems that are inherently precise, deterministic, and explainable. This is the foundation Rainbird has built upon, and with Noesis, we’re making these capabilities accessible to developers everywhere.
The race to trustworthy AI isn’t just about constraining what AI can do wrong – it’s about building systems that do things right from the start.
That’s the Rainbird difference.
Want to learn more about how Rainbird can bring deterministic AI to your organisation? Register for early access to Noesis and see the future of trusted AI in action.