AWS Strengthens Amazon Bedrock with Industry-First AI Safeguard, New Agent Capability, and Model Customization
AWS announced new capabilities for Amazon Bedrock, its fully managed service for building generative AI applications. The key enhancements include: Automated Reasoning checks, the first AI safeguard preventing factual errors from hallucinations; multi-agent collaboration for orchestrating multiple AI agents in complex tasks; and Model Distillation for creating smaller, task-specific models.
The new Model Distillation feature enables up to 500% faster performance and 75% cost reduction while maintaining accuracy. Amazon Bedrock has seen its customer base grow 4.7x in the last year, with notable clients including Moody's, PwC, and Robin AI implementing these new capabilities.
Automated Reasoning checks helps validate factual responses and produces auditable outputs, particularly beneficial for regulated industries. The multi-agent collaboration feature allows companies to create specialized AI agents for specific tasks, while Model Distillation helps optimize models for the best combination of capabilities, accuracy, latency, and cost.
AWS ha annunciato nuove funzionalità per Amazon Bedrock, il suo servizio completamente gestito per la creazione di applicazioni di intelligenza artificiale generativa. I principali miglioramenti includono: Verifiche di Ragionamento Automatizzato, il primo sistema di sicurezza AI che impedisce errori fattuali da allucinazioni; collaborazione tra più agenti per orchestrare più agenti AI in compiti complessi; e Distorzione del Modello per creare modelli più piccoli e specifici per il compito.
La nuova funzionalità di Distorzione del Modello consente prestazioni fino al 500% più veloci e una riduzione dei costi del 75% mantenendo la precisione. Amazon Bedrock ha visto crescere la sua base clienti di 4,7 volte nell'ultimo anno, con clienti notevoli come Moody's, PwC e Robin AI che implementano queste nuove funzionalità.
Le verifiche di Ragionamento Automatizzato aiutano a convalidare risposte fattuali e producono output auditabili, particolarmente utili per le industrie regolamentate. La funzionalità di collaborazione tra più agenti consente alle aziende di creare agenti AI specializzati per compiti specifici, mentre la Distorzione del Modello aiuta a ottimizzare i modelli per la migliore combinazione di capacità, precisione, latenza e costo.
AWS anunció nuevas capacidades para Amazon Bedrock, su servicio completamente gestionado para construir aplicaciones de IA generativa. Las mejoras clave incluyen: Verificaciones de Razonamiento Automatizado, la primera salvaguarda de IA que previene errores fácticos por alucinaciones; colaboración entre múltiples agentes para orquestar varios agentes de IA en tareas complejas; y Destilación de Modelos para crear modelos más pequeños y específicos para tareas.
La nueva función de Destilación de Modelos permite un rendimiento hasta un 500% más rápido y una reducción de costos del 75% manteniendo la precisión. Amazon Bedrock ha visto crecer su base de clientes 4.7 veces en el último año, con clientes notables como Moody's, PwC y Robin AI implementando estas nuevas capacidades.
Las verificaciones de Razonamiento Automatizado ayudan a validar respuestas fácticas y producen salidas auditables, particularmente beneficiosas para las industrias reguladas. La función de colaboración entre múltiples agentes permite a las empresas crear agentes de IA especializados para tareas específicas, mientras que la Destilación de Modelos ayuda a optimizar modelos para la mejor combinación de capacidades, precisión, latencia y costo.
AWS는 생성적 AI 애플리케이션 구축을 위한 완전 관리 서비스인 Amazon Bedrock의 새로운 기능을 발표했습니다. 주요 개선 사항에는 자동 추론 검사가 포함되어 있으며, 이는 환각으로 인한 사실 오류를 방지하는 첫 번째 AI 안전 장치입니다; 다중 에이전트 협업은 복잡한 작업에서 여러 AI 에이전트를 조정하기 위한 것입니다; 모델 증류는 더 작고 특정 작업에 맞는 모델을 만드는 기능입니다.
새로운 모델 증류 기능은 성능을 최대 500% 더 빠르게 하고 정확성을 유지하면서 비용을 75% 줄일 수 있습니다. Amazon Bedrock는 지난 1년 동안 고객 기반이 4.7배 성장했으며, Moody's, PwC, Robin AI와 같은 주목할 만한 고객들이 이러한 새로운 기능을 도입했습니다.
자동 추론 검사는 사실 응답을 검증하고 감사 가능한 출력을 생산하는 데 도움을 주며, 특히 규제 산업에 유리합니다. 다중 에이전트 협업 기능은 기업이 특정 작업을 위해 특화된 AI 에이전트를 생성할 수 있게 하며, 모델 증류는 기능, 정확성, 대기 시간 및 비용의 최상의 조합을 위해 모델을 최적화하는 데 도움을 줍니다.
AWS a annoncé de nouvelles fonctionnalités pour Amazon Bedrock, son service entièrement géré pour la création d'applications d'IA génératives. Les améliorations clés incluent : Contrôles de Raisonnement Automatisés, le premier garde-fou d'IA empêchant les erreurs factuelles dues aux hallucinations ; collaboration multi-agents pour orchestrer plusieurs agents d'IA dans des tâches complexes ; et Distillation de Modèle pour créer des modèles plus petits et spécifiques à une tâche.
La nouvelle fonctionnalité de Distillation de Modèle permet d'obtenir des performances jusqu'à 500 % plus rapides et une réduction des coûts de 75 % tout en maintenant la précision. Amazon Bedrock a vu sa base de clients croître de 4,7 fois au cours de la dernière année, avec des clients notables tels que Moody's, PwC et Robin AI qui mettent en œuvre ces nouvelles fonctionnalités.
Les contrôles de Raisonnement Automatisés aident à valider les réponses factuelles et produisent des résultats auditables, particulièrement bénéfiques pour les industries réglementées. La fonctionnalité de collaboration multi-agents permet aux entreprises de créer des agents d'IA spécialisés pour des tâches spécifiques, tandis que la Distillation de Modèle aide à optimiser les modèles pour la meilleure combinaison de capacités, précision, latence et coût.
AWS hat neue Funktionen für Amazon Bedrock angekündigt, seinen vollständig verwalteten Dienst zum Erstellen generativer KI-Anwendungen. Zu den wichtigsten Verbesserungen gehören: Automatisierte Einsichtsprüfungen, die erste KI-Schutzmaßnahme zur Verhinderung von Faktenfehlern durch Halluzinationen; Multi-Agenten-Kollaboration zur Orchestrierung mehrerer KI-Agenten bei komplexen Aufgaben; und Modell-Destillation zur Erstellung kleinerer, auf bestimmte Aufgaben abgestimmter Modelle.
Die neue Modell-Destillationsfunktion ermöglicht bis zu 500% schnellere Leistung und 75% Kostensenkung bei gleichbleibender Genauigkeit. Amazon Bedrock hat im letzten Jahr ein Wachstum von 4,7x bei der Kundenbasis verzeichnet, wobei bemerkenswerte Kunden wie Moody's, PwC und Robin AI diese neuen Funktionen implementieren.
Automatisierte Einsichtsprüfungen helfen, faktische Antworten zu validieren und erstellbare Ausgaben zu produzieren, was insbesondere für regulierte Branchen von Vorteil ist. Die Multi-Agenten-Kollaboration ermöglicht es Unternehmen, spezialisierte KI-Agenten für spezifische Aufgaben zu erstellen, während die Modell-Destillation hilft, Modelle für die beste Kombination von Fähigkeiten, Genauigkeit, Latenz und Kosten zu optimieren.
- Customer base growth of 4.7x in the last year
- Model Distillation enables up to 500% faster performance
- Cost reduction of up to 75% through Model Distillation
- First-to-market AI safeguard against hallucinations
- Model Distillation may result in up to 2% accuracy loss
Insights
Amazon's strategic enhancements to Bedrock represent a significant leap in enterprise AI capabilities. The Automated Reasoning checks address a critical market need by reducing AI hallucinations - a major barrier for regulated industries adopting AI. The multi-agent collaboration feature enables complex business process automation, while Model Distillation could reduce operational costs by up to
These improvements directly target enterprise pain points: reliability, scalability and cost-effectiveness. The 4.7x customer base growth demonstrates strong market adoption. With major clients like Moody's and PwC implementing these solutions, AWS is strengthening its position in the enterprise AI market. The ability to create smaller, efficient models while maintaining accuracy addresses the critical balance between performance and cost that many businesses struggle with.
This development strengthens Amazon's competitive position in the lucrative enterprise AI market. The cost reduction potential through Model Distillation (
The 4.7x growth in Bedrock's customer base indicates strong market penetration and potential recurring revenue growth. With enterprise AI spending projected to surge, these enhancements position AWS to capture a larger share of this expanding market. The endorsement from major firms like Moody's and PwC validates the technology's enterprise readiness and could accelerate adoption among other large organizations.
Automated Reasoning checks, multi-agent collaboration, and Model Distillation build on the strong foundation of enterprise-grade capabilities available on Amazon Bedrock to help customers go from proof of concept to production-ready generative AI faster
- Automated Reasoning checks is the first and only generative AI safeguard that helps prevent factual errors due to model hallucinations, opening up new generative AI use cases that demand the highest levels of precision.
- Customers can use multi-agent collaboration to easily build and orchestrate multiple AI agents to solve problems together, expanding the ways customers can apply generative AI to address their most complex use cases.
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Model Distillation empowers customers to transfer specific knowledge from a large, highly capable model to a smaller, more efficient one that can be up to
500% faster and75% less expensive to run. - Tens of thousands of customers use Amazon Bedrock today, with Moody’s, PwC, and Robin AI among those leveraging these new capabilities to cost-effectively scale inference and push the limits of generative AI innovation.
“With a broad selection of models, leading capabilities that make it easier for developers to incorporate generative AI into their applications, and a commitment to security and privacy, Amazon Bedrock has become essential for customers who want to make generative AI a core part of their applications and businesses,” said Dr. Swami Sivasubramanian, vice president of AI and Data at AWS. “That is why we have seen Amazon Bedrock grow its customer base by 4.7x in the last year alone. Over time, as generative AI transforms more companies and customer experiences, inference will become a core part of every application. With the launch of these new capabilities, we are innovating on behalf of customers to solve some of the top challenges, like hallucinations and cost, that the entire industry is facing when moving generative AI applications to production.”
Automated Reasoning checks prevent factual errors due to hallucinations
While models continue to advance, even the most capable ones can hallucinate, providing incorrect or misleading responses. Hallucinations remain a fundamental challenge across the industry, limiting the trust companies can place in generative AI. This is especially true for regulated industries, like healthcare, financial services, and government agencies, where accuracy is critical, and organizations need to audit to make sure models are responding appropriately. Automated Reasoning checks is the first and only generative AI safeguard that helps prevent factual errors due to hallucinations using logically accurate and verifiable reasoning. By increasing the trust that customers can place in model responses, Automated Reasoning checks opens generative AI up to new use cases where accuracy is paramount.
Automated reasoning is a branch of AI that uses math to prove something is correct. It excels when dealing with problems where users need precise answers to a topic that is large and complex, and that has a well-defined set of rules or collection of knowledge about the subject. AWS has a team of world-class automated reasoning experts who have used this technology over the last decade to improve experiences across AWS, like proving that permissions and access controls are implemented correctly to enhance security or checking millions of scenarios across Amazon Simple Storage Service (S3) before deployment to ensure availability and durability remain protected.
Amazon Bedrock Guardrails makes it easy for customers to apply safety and responsible AI checks to generative AI applications, allowing customers to guide models to only talk about relevant topics. Accessible through Amazon Bedrock Guardrails, Automated Reasoning checks now allows Amazon Bedrock to validate factual responses for accuracy, produce auditable outputs, and show customers exactly why a model arrived at an outcome. This increases transparency and ensures that model responses are in line with a customer’s rules and policies. For example, a health insurance provider that needs to ensure its generative AI-powered customer service application responds correctly to customer questions about policies could benefit from Automated Reasoning checks. To apply them, the provider uploads their policy information, and Amazon Bedrock automatically develops the necessary rules, guiding the customers to iteratively test it to ensure the model is tuned to the right response—no automated reasoning expertise required. The insurance provider then applies the check, and as the model generates responses, Amazon Bedrock verifies them. If a response is incorrect, like getting the deductible wrong or flagging a procedure that is not covered, Amazon Bedrock suggests the correct response using information from the Automated Reasoning check.
PwC, a global professional services firm, is using Automated Reasoning checks to create AI assistants and agents that are highly accurate, trustworthy, and useful to drive its clients’ businesses to the leading edge. PwC incorporates Automated Reasoning checks into industry-specific solutions for clients in financial services, healthcare, and life sciences, including applications that verify AI-generated compliance content with Food and Drug Administration (FDA) and other regulatory standards. Internally, PwC employs Automated Reasoning checks to ensure that responses generated by generative AI assistants and agents are accurate and compliant with internal policies.
Easily build and coordinate multiple agents to execute complex workflows
As companies make generative AI a core part of their applications, they want to do more than just summarize content and power chat experiences. They also want their applications to take action. AI-powered agents can help customers’ applications accomplish these actions by using a model’s reasoning capabilities to break down a task, like helping with an order return or analyzing customer retention data, into a series of steps that the model can execute. Amazon Bedrock Agents makes it easy for customers to build these agents to work across a company’s systems and data sources. While a single agent can be useful, more complex tasks, like performing financial analysis across hundreds or thousands of different variables, may require a large number of agents with their own specializations. However, creating a system that can coordinate multiple agents, share context across them, and dynamically route different tasks to the right agent requires specialized tools and generative AI expertise that many companies do not have available. That is why AWS is expanding Amazon Bedrock Agents to support multi-agent collaboration, empowering customers to easily build and coordinate specialized agents to execute complex workflows.
Using multi-agent collaboration in Amazon Bedrock, customers can get more accurate results by creating and assigning specialized agents for specific steps of a project and accelerate tasks by orchestrating multiple agents working in parallel. For example, a financial institution could use Amazon Bedrock Agents to help carry out due diligence on a company before investing. First, the customer uses Amazon Bedrock Agents to create a series of specialized agents focused on specific tasks, like analyzing global economic factors, assessing industry trends, and reviewing the company’s historical financials. After they have created all of their specialized agents, they create a supervisor agent to manage the project. The supervisor then handles the coordination, like breaking up and routing tasks to the right agents, giving specific agents access to the information they need to complete their work, and determining what actions can be processed in parallel and which need details from other tasks before the agent can move forward. Once all of the specialized agents complete their inputs, the supervisor agent pulls the information together, synthesizes the results, and develops an overall risk profile.
Moody’s, a global leader in credit ratings and financial insights, has chosen Amazon Bedrock multi-agent collaboration to enhance its risk analysis workflows. Moody's is leveraging Amazon Bedrock to create agents that are each assigned a specific task and given access to tailored datasets to perform its role. For example, one agent might analyze macroeconomic trends, while another evaluates company-specific risks using proprietary financial data, and a third benchmarks competitive positioning. These agents collaborate seamlessly, synthesizing their outputs into precise, actionable insights. This innovative approach enables Moody’s to deliver faster, more accurate risk assessments, solidifying its reputation as a trusted authority in financial decision-making.
Create smaller, faster, more cost-effective models with Model Distillation
Customers today are experimenting with a wide variety of models to find the one best suited to the unique needs of their business. However, even with all the models available today, it is challenging to find one with the right mix of specific knowledge, cost, and latency. Larger models are more knowledgeable, but they take longer to respond and cost more, while small models are faster and cheaper to run, but are not as capable. Model distillation is a technique that transfers the knowledge from a large model to a small model, while retaining the small model’s performance characteristics. However, doing this requires specialized machine learning (ML) expertise to work with training data, manually fine-tune the model, and adjust model weights without compromising the performance characteristics that led the customer to choose the smaller model in the first place. With Amazon Bedrock Model Distillation, any customer can now distill their own model that can be up to
With Amazon Bedrock Model Distillation, customers simply select the best model for a given use case and a smaller model from the same model family that delivers the latency their application requires at the right cost. After the customer provides sample prompts, Amazon Bedrock will do all the work to generate responses and fine-tune the smaller model, and it can even create more sample data, if needed, to complete the distillation process. This gives customers a model with the relevant knowledge and accuracy of the large model, but the speed and cost of the smaller model, making it ideal for production use cases, like real-time chat interactions. Model Distillation works with models from Anthropic, Meta, and the newly announced Amazon Nova Models.
Robin AI, which provides an AI-powered assistant that helps make complex legal processes quicker, cheaper, and more accessible, is using Model Distillation to help power high-quality legal Q&A across millions of contract clauses. Model Distillation helps Robin AI get the accuracy they need at a fraction of the cost, while faster responses provide a more fluid interaction between their customers and the assistant.
Automated Reasoning checks, multi-agent collaboration, and Model Distillation are all available in preview.
To learn more, visit:
- The AWS Blog for details on today’s announcements: Automated Reasoning checks, multi-agent collaboration, and Model Distillation.
- The Amazon Bedrock page to learn more about the capabilities.
- The Amazon Bedrock customer page to learn how companies are using Amazon Bedrock.
- The AWS re:Invent page for more details on everything happening at AWS re:Invent.
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