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
At AWS re:Invent, Amazon Web Services, Inc. (AWS), an
Amazon.com, Inc. company (NASDAQ: AMZN), today announced new
capabilities for Amazon Bedrock, a fully managed service for
building and scaling generative artificial intelligence (AI)
applications with high-performing foundation models. Today’s
announcements help customers prevent factual errors due to
hallucinations, orchestrate multiple AI-powered agents for complex
tasks, and create smaller, task-specific models that can perform
similarly to a large model at a fraction of the cost and
latency.
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- 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.
- 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 and 75% 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 500% faster and 75% less expensive to run than original
models, with less than 2% accuracy loss for use cases like
retrieval augmented generation (RAG). Now, customers can optimize
to achieve the best combination of capabilities, accuracy, latency,
and cost for their use case—no ML expertise required.
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.
About Amazon Web Services
Since 2006, Amazon Web Services has been the world’s most
comprehensive and broadly adopted cloud. AWS has been continually
expanding its services to support virtually any workload, and it
now has more than 240 fully featured services for compute, storage,
databases, networking, analytics, machine learning and artificial
intelligence (AI), Internet of Things (IoT), mobile, security,
hybrid, media, and application development, deployment, and
management from 108 Availability Zones within 34 geographic
regions, with announced plans for 18 more Availability Zones and
six more AWS Regions in Mexico, New Zealand, the Kingdom of Saudi
Arabia, Taiwan, Thailand, and the AWS European Sovereign Cloud.
Millions of customers—including the fastest-growing startups,
largest enterprises, and leading government agencies—trust AWS to
power their infrastructure, become more agile, and lower costs. To
learn more about AWS, visit aws.amazon.com.
About Amazon
Amazon is guided by four principles: customer obsession rather
than competitor focus, passion for invention, commitment to
operational excellence, and long-term thinking. Amazon strives to
be Earth’s Most Customer-Centric Company, Earth’s Best Employer,
and Earth’s Safest Place to Work. Customer reviews, 1-Click
shopping, personalized recommendations, Prime, Fulfillment by
Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire
tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology,
Amazon Studios, and The Climate Pledge are some of the things
pioneered by Amazon. For more information, visit amazon.com/about
and follow @AmazonNews.
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