AWS is trying to revive its lineup of agentic AI offerings while releasing new capabilities across Amazon Quick, Bedrock, AgentCore, and new security products.
While its AI products and services may be perceived as behind competitors such as Google and Microsoft and AI labs such as OpenAI and Anthropic, they illustrate a trend in which AI vendors are no longer focusing solely on building new tools but on addressing enterprises’ concerns about building, deploying and securing agentic AI.
At its AWS AI Summit New York City conference on Wednesday, AWS introduced new capabilities across platforms such as Amazon Bedrock AgentCore, its platform for building, connecting and optimizing AI agents; Amazon Quick, its AI-powered assistant; Autonomous Agents; and new integrations with applications such as Adobe, Shopify, Smartsheet and Snowflake. The cloud giant also previewed a new service that helps agents map relationships across existing data into a knowledge graph. Beyond introducing new agentic capabilities, AWS focused on securing AI agents.
“They are enhancing their [products] to be ready for the proliferation of AI agents,” said Lian Jye Su, an analyst at Omdia, a division of Informa TechTarget.
Already Available
However, many of the releases are not new to the market.
“These capabilities are already available from other hyperscalers and frontier model vendors, but only a hyperscaler like AWS can launch so many enhancements at the same time,” Su said.
For example, the new Amazon Bedrock Managed Knowledge Base offers features similar to Google Cloud’s Vertex AI Search and Vertex AI Grounding capabilities. Knowledge Base connects unstructured data sources across applications such as SharePoint, Google Drive and Confluence. It features an agentic retriever that plans queries, connects concepts across documents and reranks results. While Knowledge Base is like Vertex AI Search in its use of retrieval augmented generation, AWS offers a wider range of third-party AI models than Google.
Another new feature is Web Search on AgentCore, which provides real time web information while keeping data within AWS security boundaries. AWS also previewed AgentCore Payments, which enables agents to discover, access and pay for premium content and services.
Along with Knowledge Base, AWS introduced AWS Context. The new service maps relationships across existing data into a knowledge graph, allowing AI agents to access governed data relationships, business rules and domain knowledge.
“With just a few clicks in the managed console, your agents now have context,” said vice president of a Swami Sivasubramanian, VP, AWS agentic AI, during a keynote presentation on Wednesday.
Movement in the Market
Knowledge Base and AWS Context show that AI trends have has shifted from the need to innovate constantly to the need to focus on developers’ challenges and the obstacles that keep customers from effectively pursuing AI initiatives. Some of those challenges are about ensuring AI agents have access to the right knowledge and context, so they don’t hallucinate when asked to perform a task.
“It’s not about frontier models anymore,” said Torsten Volk, an Omdia analyst. “It’s about context engineering. The big difference between a failed AI project and an AI project that gets across the finish line is that the agent can say, ‘No, sorry. I don’t know the response to this question,’ instead of just making something up.’”
He added that another barrier for many enterprises to succeed with AI is trust in the technology, he added.
AWS addresses this issue with AWS Continuum. Continuum is a new security tool that addresses vulnerabilities in coding agents. AWS said Continuum provides active, outcome-driven security with elements such as reasoning, actions, telemetry and context. The tool can ingest the existing vulnerability backlog, generate evidence-backed priority lists, identify false positives, assess existing defenses and controls, and provide corrective measure using policy updates or code patches. The tool uses multiple frontier models depending on the application.
A Need for Differentiation
While AWS is trailing competitors in some of these areas, the new releases ought to be helpful for enterprises looking to build agents and show the vendor is listening to its customer base, Su said.
“It shows AWS is getting a lot of demands and requests for supporting agentic AI capabilities,” he said. However, the vendor needs more than just its own set of similar capabilities to be successful in the agentic AI era, he added.
“They will need to differentiate themselves better,” Su said. “AWS needs to continuously make its cloud agent-ready by adding connectors for ease of access, integrating functions as skills, preparing the right hardware foundation … and introducing more AgentOps capabilities in terms of FinOps, observability and governance.”
