Amazon Bedrock AgentCore is a new AWS toolkit with multiple services (runtime, memory, identity, etc.) to help developers build and deploy AI agents at scale.
Amazon Web Services (AWS) is betting big on autonomous AI agents with its newly unveiled Amazon Bedrock AgentCore toolkit and a fresh $100 million investment in generative AI. Announced at the AWS Summit in New York 2025, AWS AgentCore is a suite of services designed to help companies securely deploy and operate AI agents at enterprise scale. In simple terms, AWS is providing the tools to turn experimental AI “agents” into production-ready applications – all while maintaining the security and reliability that enterprises demand. This move, along with an AI Agents Marketplace featuring hundreds of pre-built solutions, marks a major industry push toward what AWS calls “agentic AI,” where software agents can act autonomously to perform complex tasks with minimal human oversight.
What Are AI Agents (Agentic AI)?
AI agents are essentially autonomous software programs powered by AI models (like advanced foundation models or large language models) that can reason, plan, and act to achieve goals set by users. Unlike a simple chatbot that only responds to prompts, an AI agent can carry out multi-step tasks and make decisions to fulfill a user-defined objective. For example, an AI agent might not only draft an email response but also look up customer data, schedule follow-up tasks, or execute transactions – all on its own. AWS’s Swami Sivasubramanian (VP of Agentic AI) describes these agents as “autonomous software systems that leverage AI to reason, plan and adapt to complete tasks,” which can “dramatically accelerate innovation and improve productivity across every industry”. This emerging “agentic AI” trend represents a shift from AI simply generating content to AI taking action in software systems. Even if you’re a beginner, you can think of agentic AI as building smart digital assistants that don’t just chat, but can actually get things done on your behalf.
AWS AgentCore Bedrock– Toolkit for Autonomous Agents
To make autonomous AI agents easier to build and reliable enough for real-world use, AWS introduced Amazon Bedrock AgentCore. This toolkit (currently in preview) provides a comprehensive set of building blocks so developers don’t have to reinvent them from scratch. In other words, AgentCore handles the “heavy lifting” infrastructure and safety aspects, letting teams focus on an agent’s unique logic or purpose. Key components of AWS AgentCore include:
- AgentCore Runtime: A secure, managed environment to run AI agents. It’s a low-latency serverless runtime with complete session isolation, meaning each user’s agent session runs separately for security. It even supports long-running tasks (up to 8 hours) for complex workflows.
- AgentCore Memory: A built-in memory system that gives agents both short-term and long-term memory. This helps an agent remember context from past interactions and learn over time, without developers building a database from scratch.
- AgentCore Identity: An identity and access management layer for agents. It lets AI agents securely connect to tools, databases, or other services (AWS or third-party like GitHub, Salesforce, etc.) with proper authentication and permissions. This ensures agents only do what they’re allowed to, crucial for enterprise security.
- AgentCore Gateway: A gateway to connect agents with external tools and APIs. It can turn existing APIs or AWS Lambda functions into “agent-ready” tools. Essentially, this makes it easier for an AI agent to discover and use various services or data sources as it works on tasks.
- AgentCore Code Interpreter: A secure code execution sandbox for agents. Many advanced AI agents can generate or use code to solve problems (for example, writing a Python script to analyze data). This component lets an agent run such code in an isolated, safe environment.
- AgentCore Browser Tool: A managed web browser that an agent can control. This allows AI agents to browse websites or interact with web apps automatically – useful for tasks like scraping info, submitting forms, or performing online actions on behalf of a user.
- AgentCore Observability: Monitoring and debugging tools for agents. It provides step-by-step visibility into what the AI agent is doing, logs its actions, and helps developers trace and fix issues. In production, this is vital for trust and compliance, since you need to understand an agent’s decisions and ensure it’s behaving as expected.
All these components are modular – you can use one or use them all together – and they work with any AI framework or model (not just AWS’s). For example, if you built a prototype agent using an open-source framework like LangChain or a custom solution, you could plug in AgentCore services to handle things like memory or identity without rewriting your whole project. The goal is to help teams move from prototype to production much faster by solving common challenges (security, scaling, integration) for you. By eliminating tedious infrastructure work, AgentCore lets developers focus on the core functionality of their AI agents.
AI Agents Marketplace – 800+ Ready-Made Solutions
Alongside AgentCore, AWS launched a new “AI Agents and Tools” category in the AWS Marketplace to jump-start the ecosystem. Think of it as an app store for AI agents and related tools. At launch, this marketplace features over 800 pre-built AI agents, tools, and datasets from various AWS partners. In other words, instead of building every AI agent from scratch, companies can browse this catalog to find solutions that are ready to use or adapt.
Screenshot of the AWS Marketplace showing the new AI Agents & Tools category, where companies can find 800+ pre-built agent solutions.
These offerings come from well-known AI innovators and enterprise software providers – for example, you’ll find agents or tools from Anthropic, IBM, Salesforce, Automation Anywhere, Perplexity.ai, PwC, and many others. The marketplace provides a one-stop shop where customers can discover, buy, and deploy AI agents suited to their use case. AWS even added a clever AI-powered search feature: you can describe in natural language what problem you’re trying to solve, and the marketplace’s AI will recommend relevant agent solutions. This makes it easier to sift through the options and find the right tool for your needs.
For businesses, the AI Agents Marketplace can significantly speed up AI projects. Instead of spending months assembling all the pieces, you might find a pre-made agent that handles, say, customer support emails or data analysis, which you can then customize. AWS also notes this marketplace is a big opportunity for partners – those who develop AI agents can list them here and reach AWS’s enterprise customer base. In fact, AWS’s Brian Bohan calls it “a one-stop shop” that can accelerate customers’ “agentic journey” by making third-party tools and even expert consulting services easily accessible in one place.
$100 Million Investment to Spur “Agentic AI” Adoption
To further underline its commitment, AWS is doubling down on support for generative AI and agentic systems with a $100 million investment in the AWS Generative AI Innovation Center. This center (launched in 2023) pairs AWS experts with companies to co-develop AI solutions and overcome practical adoption challenges. The new $100 million funding (which doubles AWS’s investment in the program) is earmarked to help companies build “autonomous, agentic AI systems” that can transform their businesses. In plain terms, AWS is putting more resources into guiding enterprises on how to use AI agents effectively and responsibly.
Why does this matter? It shows that AWS isn’t just releasing a product and leaving customers to figure it out – they’re also investing in education, best practices, and hands-on help to ensure these AI agents deliver real value. According to AWS, the Innovation Center’s team of AI scientists and engineers has already helped thousands of customers in various industries (from finance to healthcare) bring AI solutions from idea to production. With the rise of agentic AI, they’ll focus on new use cases where AI agents can automate complex tasks. The additional funding will likely sponsor more pilot projects, workshops, and collaborations to accelerate learning in this nascent field. AWS cited examples of early successes – like a sports broadcaster using AI agents to automate live stats research, or an automaker using AI to diagnose vehicle issues – to illustrate the transformative potential of these technologies.
Opportunities and Challenges in the Era of Agentic AI
For the Ossels.ai audience and the AI developer community, AWS’s AgentCore and related initiatives signal exciting opportunities – as well as new challenges. On one hand, robust platforms like AgentCore lower the barrier to building the next generation of AI-driven applications. Developers can combine powerful AI models with enterprise-grade infrastructure, allowing even small teams to create sophisticated autonomous agents that can operate at scale. Businesses, too, stand to gain – these agents can automate time-consuming processes (from handling customer queries to processing loans) and potentially boost productivity across industries. It’s telling that Gartner predicts 33% of enterprise software will include agentic AI by 2028 (up from under 1% in 2024). In short, many believe that AI agents could become as common as web or mobile apps in the coming years, representing a big shift in how we build software.
However, embracing agentic AI isn’t without challenges. Autonomous agents introduce new considerations around security, control, and trust. By design, these agents can make decisions and take actions – so companies must ensure they don’t go off-script or access sensitive data improperly. AWS AgentCore addresses some of these concerns (for example, sandboxing agents to prevent data leaks, and fine-grained identity controls to restrict what an agent can do). Similarly, the observability tools in AgentCore are crucial for transparency: developers and auditors need to see why an AI agent made a decision, especially if it’s acting on important business processes. There’s also the issue of reliability – current AI models can sometimes produce incorrect or inconsistent results (a phenomenon often called “hallucination” in AI). In fact, industry research suggests many early AI agent projects might stall or get canceled due to lack of reliable results. AWS’s approach (and the industry at large) is working to mitigate this by incorporating guardrails, continuous monitoring, and improvement of model accuracy.
Another challenge is ethical and responsible AI use. When agents are acting autonomously, how do we ensure they align with human values and company policies? This requires careful design and governance. AWS emphasizes principles like accountability and explainability in deploying agentic AI, and it’s an active area of discussion in the AI community. For developers, this means that beyond technical skills, there’s a growing need to understand AI ethics, testing, and policy compliance when building agents.
Despite these challenges, the overall trajectory is clear: autonomous AI agents are moving from hype to reality, and big players like AWS are pouring resources into making them enterprise-ready. This opens up a world of possibilities for developers, startups, and businesses to create new AI-driven products and services. Imagine virtual assistants that can not only answer questions but also perform tasks across your apps, or AI systems that proactively handle operations in finance, healthcare, or IT without constant human instruction. Those are the kinds of innovations agentic AI promises.
Conclusion: AWS’s Vision for the Future of AI Applications
AWS’s launch of the Bedrock AgentCore toolkit, the AI Agents Marketplace, and the $100 million fund all highlight one message: Amazon is betting big on autonomous agents as the next evolution of AI. By providing the infrastructure, pre-built solutions, and investment support, AWS aims to spur the development of AI agents that are secure, scalable, and effective for real-world use. As Swami Sivasubramanian noted, this shift “upends the way software is built” and “changes how we interact with software”. For developers and enterprises, it’s an exciting opportunity to reimagine applications that can do more on their own.
That said, moving to an agentic AI future will be a journey. It requires not just new tools, but also new mindsets about software autonomy, oversight, and collaboration between humans and AI. AWS AgentCore is one of the first major platforms tackling this at scale, and it will likely be followed by others in the industry. For beginners and experts alike, now is a great time to start learning about AI agents – how they work, what they can do, and how to use frameworks like AgentCore to build them responsibly. The era of autonomous agents is just beginning, and with the right tools and guidance, even small teams will be able to create powerful AI-driven applications that were once the stuff of science fiction. Amazon’s big bet is that these agents will become invaluable co-workers in our digital world, and with AgentCore, they’re providing the toolbox to make it happen.
Sources: AWS News Blog, AWS press release, CRN tech news, The Register. (All links accessed July 2025)
