# Automated reasoning Automated reasoning is the use of computing systems and algorithms to mechanize logical deduction and prove theorems, with applications including formal verification of software and hardware. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Securing the agentic era: Introducing formal verification for CEL DevFeed: [Securing the agentic era: Introducing formal verification for CEL]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-08-18T18:30:00Z Content type: release Language: en Sources: [Google Open Source Blog]() Topics: [Formal verification](), [Automated reasoning](), [Testing](), [Artificial Intelligence]() Tags: [agent](), [ai](), [ai-agent](), [automated-reasoning](), [cel](), [common-expression-language](), [formal-verification](), [open-source](), [security](), [testing](), [verification](), [z3]() ### AI overview Google's Common Expression Language (CEL) Formal Verification Framework is available for proving the correctness of CEL expressions and policies. Powered by the Z3 theorem prover, it complements heuristic testing by using mathematical proofs to identify logic flaws, equivalence issues, and potentially unsafe policy behavior. ### Source excerpt by Sean Huh, Common Expression Language Team We are rapidly entering an era where AI agents can autonomously draft, refactor, and deploy policies that protect our users and our systems. But this velocity introduces a vital question: How do we trust AI-generated policies? Unit tests may fail to cover the infinite set of possible inputs that occur in production; thus, an AI agent that overfits its policy to existing tests may fail spectacularly in production. To secure automated policy authoring, we must combine heuristic testing with mathematical proofs. We are thrilled to announce the Common Expression Language (CEL) Formal Verification Framework is now available. Powered by the Z3 theorem prover, this framework allows you to prove the correctness of your CEL expressions and policies, serving as the ultimate safety net for the agentic policy. Automated reasoning definitively answers questions like: "Is there any combination of inputs that allows an unapproved request into production?" "Are we absolutely certain this AI-refactored policy matches the original behavior?" "Can a bad actor manipulate this rule to force an evaluation error?" Formal verification establishes mathematical certainty across the infinite spectrum of inputs. Proven policies protect your users and system while giving auditors clear proof of compliance. To see these capabilities in action, watch our video demonstrating how the CEL Verifier REPL catches subtle logic flaws in seconds: Proving rules from the ground up Getting started with formal verification doesn't require learning complex architectures right away. You can evaluate simple standalone CEL expressions to catch edge cases that tests easily miss. (Note: The examples below use our interactive REPL syntax--check out the REPL documentation to follow along!) 1. Catching logic bugs in simple expressions (Equivalence) How do you guarantee a refactored rule behaves identically to the original? Suppose we have a policy that allows ports 80 or 443 ## A decade of mathematical certainty: Reflections on the Automated Reasoning Group DevFeed: [A decade of mathematical certainty: Reflections on the Automated Reasoning Group]() Original publisher: [Read original article]() Author: Byron Cook Published: 2026-08-11T16:22:19Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Automated reasoning](), [Formal verification](), [Math and Logic](), [systems](), [Amazon Web Services](), [VPC](), [network security](), [Security](), [AWS IAM](), [Amazon S3](), [Cloud](), [amazon]() Tags: [agentic-ai](), [amazon](), [amazon-web-services-aws](), [automated-reasoning](), [aws](), [compliance](), [formal-verification](), [network-security](), [security](), [security-privacy-and-abuse-prevention](), [vpc](), [vulnerabilities]() ### AI overview Amazon's Automated Reasoning Group reflects on a decade of applying mathematical logic, formal verification, and program analysis to AWS security and reliability. The article describes how research projects became production systems, including Tiros for VPC and network analysis and Zelkova for analyzing policies, S3 Block Public Access, and IAM Access Analyzer. ### Source excerpt Ten years after we founded the Automated Reasoning Group, mathematical logic has moved from academic research into production services that secure millions of customer workloads -- demonstrating that systems can be provably correct, not just probably correct. ## Proofs and Essays Are Paths: An LLM ↔ Prover Loop for Falsifying Hallucinations DevFeed: [Proofs and Essays Are Paths: An LLM ↔ Prover Loop for Falsifying Hallucinations]() Original publisher: [Read original article]() Published: 2026-04-30T00:00:00Z Content type: opinion Language: en Sources: [Alex Korbonits]() Topics: [Large Language Model](), [Automated reasoning](), [Graphs](), [Artificial Intelligence](), [Math and Logic]() Tags: [grounding](), [hallucinations](), [knowledge-graph](), [llm](), [reasoning](), [smt](), [verification](), [verify]() ### AI overview This essay proposes a bidirectional loop between a large language model and a theorem prover. LLM outputs become propositions and graph connections that the prover checks, while verification results feed back into the model. In this framework, hallucinations appear as knowledge-graph paths with edges that fail formal verification. ### Source excerpt If a proof is a path through a formal space and an essay is a path through a semantic one, can we close the loop -- translate LLM outputs into a theorem prover, build a knowledge graph of verifiable propositions, and use the gaps as a signal for hallucination? Notebook ideas from March 2024 expanding on the SMT-grounding piece. ## The Philosophy of Machine Learning, or: What Comes After Hegel? DevFeed: [The Philosophy of Machine Learning, or: What Comes After Hegel?]() Original publisher: [Read original article]() Published: 2026-04-09T00:00:00Z Content type: opinion Language: en Sources: [Alex Korbonits]() Topics: [Automated reasoning](), [Artificial Intelligence](), [LLMs](), [Math and Logic](), [pattern matching]() Tags: [ai](), [automated-reasoning](), [conditional-probability](), [llms](), [logic](), [machine-learning](), [pattern-matching](), [reasoning]() ### AI overview This opinion essay compares large language models with Kant's distinction between a priori structures and a posteriori experience. It argues that LLMs learn patterns from human text but lack reliable logical scaffolding, and presents automated reasoning systems such as SMT solvers and theorem provers as a possible complement. ### Source excerpt A notebook entry that maps AI paradigms onto the history of Western philosophy -- from scholasticism to Hegel -- and asks what comes next when the current moment exhausts itself. ## Using LLMs with SMT Solvers for Automated Reasoning and Formal Verification DevFeed: [Using LLMs with SMT Solvers for Automated Reasoning and Formal Verification]() Original publisher: [Read original article]() Published: 2026-04-02T00:00:00Z Content type: opinion Language: en Sources: [Alex Korbonits]() Topics: [Automated reasoning](), [Large Language Model](), [Formal verification](), [Prompt Engineering](), [Claude](), [graph theory](), [Machine learning](), [Artificial Intelligence]() Tags: [automated-reasoning](), [claude](), [formal-verification](), [graph-theory](), [llm](), [prompt-engineering]() ### AI overview The author revisits a November 2023 idea about using Claude and prompt engineering to translate problems into propositions for an SMT solver, satisfy proofs, eliminate unknowns and quantifiers, and return results to an agent. The essay connects this idea to the author's background in formal logic, graph theory and machine learning, while noting that the concept was not novel in isolation. ### Source excerpt A November 2023 notebook entry about grounding LLMs with SMT solvers -- why formal verification might be the right fix for hallucination, and what it took to finally say it out loud. ## A chat with Byron Cook on automated reasoning and trust in AI systems DevFeed: [A chat with Byron Cook on automated reasoning and trust in AI systems]() Original publisher: [Read original article]() Author: werner@allthingsdistributed.com (Dr. Werner Vogels) Published: 2026-02-17T14:00:00Z Content type: article Language: en Sources: [All Things Distributed]() Topics: [Automated reasoning](), [Artificial Intelligence](), [AI Agent](), [Lean](), [large-language-models](), [Authorization](), [Architecture & Design]() Tags: [agentic-ai](), [ai](), [ai-agent](), [authorization](), [automated-reasoning](), [cryptographic](), [deployment](), [financial](), [generate](), [language-models](), [large-language-models](), [neural-networks](), [posts](), [reasoning](), [regulatory](), [safety](), [security](), [security-vulnerabilities]() ### AI overview An interview with Byron Cook examines how automated reasoning can establish trust in AI systems. It discusses applying formal verification techniques to agentic systems, generated code, authorization, cryptographic implementations, and virtualization, with neurosymbolic AI presented as a way to combine neural networks with mathematically rigorous symbolic procedures. ### Source excerpt Over the past decade, Byron's team has proven the correctness of our authorization engine, our cryptographic implementations, and our virtualization layer. Now they're taking those same techniques and applying them to agentic systems. ## Cedar Joins CNCF as a Sandbox Project DevFeed: [Cedar Joins CNCF as a Sandbox Project]() Original publisher: [Read original article]() Author: Lara Langdon Published: 2025-12-15T18:21:10Z Content type: article Language: en Sources: [AWS Open Source Blog]() Topics: [Access Control](), [Open Source](), [Authorization](), [Cloud Native Ecosystem](), [SDKs](), [Automated reasoning](), [Lean](), [Rust](), [Testing]() Tags: [authorization](), [automated-reasoning](), [cloud-native](), [cncf](), [open-source](), [rust](), [sdk](), [security-identity-compliance](), [testing](), [verification]() ### AI overview Cedar, an open source authorization policy language and SDK, has joined the Cloud Native Computing Foundation as a Sandbox project. The article explains Cedar's purpose-built approach to fine-grained access control, its support for RBAC, ABAC, and ReBAC, and its focus on performance, analyzability, and safety through formal verification and differential testing. It also describes adoption by organizations including Cloudflare, MongoDB, StrongDM, Cloudinary, and AWS services. ### Source excerpt Cedar, an open source authorization policy language and SDK, has joined the Cloud Native Computing Foundation (CNCF) as a Sandbox project. CNCF provides a neutral home for early stage and developing open source projects. Cedar fulfills the need for a fast, safe, and analyzable authorization policy language in cloud-native environments by allowing developers to define, [...] ## LLMs as Parts of Systems DevFeed: [LLMs as Parts of Systems]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2025-08-12T00:00:00Z Content type: opinion Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Large Language Model](), [systems](), [AI Development](), [Automated reasoning](), [Artificial Intelligence](), [Kiro](), [Amazon Bedrock]() Tags: [ai](), [ai-agent](), [amazon-bedrock](), [automated-reasoning](), [kiro](), [llms](), [reasoning](), [systems]() ### AI overview The article argues that LLMs are most powerful when combined with other system components, such as code interpreters, databases, browsers, algorithms, and SMT solvers. These combinations can solve problems that LLMs alone cannot and can deliver substantially better speed and cost efficiency. Amazon Bedrock's Automated Reasoning Checks illustrate this approach by using LLMs to extract rules and facts, then applying an SMT solver to verify logical consistency. ### Source excerpt LLMs as Parts of Systems Towers of Hanoi is a boring game, anyway. Over on the Kiro blog, I wrote a post about Kiro and the future of AI spec-driven software development, looking at where I think the space of AI-agent-powered development tools is going. In that post, I made a bit of cheeky oblique reference to a topic I think is super important. I asked Kiro to build a Towers of Hanoi game. It's an oblique reference to Apple's The Illusion of Thinking paper, and the discourse that followed it. The question of whether LLMs can scalably play Towers of Hanoi is an interesting theoretically and scientifically, but not the most important question. The more important one is can systems built with LLMs play these games?. By picking me Towers of Hanoi in that other post, I was pointing out that the answer is clearly yes. And has been for several LLM generations. As a system builder, I'm much more interested in what systems of LLMs and tools can do together. LLMs and code interpreters. LLMs and databases. LLMs and browsers. LLMs and SMT solvers. These systems can do things, today, that LLMs alone simply can't, and will never be able to do. More importantly, they can do things today orders of magnitude more cheaply and quickly than LLMs can, even in the case where they can do the same things. You know, this kind of thing: > Generate a python snippet that counts the number of rs in a string. def count_rs(input_string): return input_string.lower().count('r') Trivial? Yes. But I've now created a system that that can solve problems that this LLM can't. A better LLM can, but at about six orders of magnitude higher cost per example. Systems, fundamentally, are more than the sum of their components. A good system can do things that no component can do alone. The trivial example is trivial, but you can imagine how that power could extend to being able to use decades of progress in algorithms. And not only count, but much more powerful things like SMT solvers, or ILP approximation, or