Unlocking the full potential of AI agents is no longer just an ambition—it's a necessity, but many organizations still hesitate due to concerns about safety, control, and quality assurance. But here’s where it gets controversial: As AI agents become more autonomous, ensuring they act within trusted boundaries without stifling their usefulness is a complex challenge that today's new features aim to solve. And this is the part most people miss—building reliable, compliant AI agents at scale isn’t about eliminating autonomy but managing it wisely.
Amazon Web Services has just introduced major enhancements to Amazon Bedrock’s AgentCore platform, designed specifically to address these hurdles. These enhancements add sophisticated policies and evaluation capabilities that enable organizations to confidently deploy high-performing, trustworthy AI agents across diverse industries. Since its preview just five months ago, the AgentCore SDK has been downloaded over two million times—highlighting rapid adoption and trust from the community, including innovative organizations like PGA TOUR, Workday, and Grupo Elfa.
For example, the PGA TOUR, a leader in sports innovation, utilizes a multi-agent content creation system built on AgentCore. This allows them to generate comprehensive articles for their digital outlets, boosting content creation speed by an astonishing 1,000 percent while slashing costs by 95 percent. Similarly, Workday, an independent software vendor, leverages AgentCore’s Code Interpreter to give its financial planning tools secure data handling and natural language querying, reducing routine analysis time by 30 percent and saving about 100 hours each month.
Brazilian distributor Grupo Elfa also benefits from AgentCore's observability features, which offer real-time metrics and full audit trail of their agents’ decisions. This transforms reactive processes into proactive ones, reducing problem resolution time by half while maintaining complete transparency of agent actions.
As companies expand their AI agent fleets, they often grapple with setting effective boundaries—ensuring agents do not access inappropriate data, make unauthorized modifications, or take unexpected actions. While autonomy makes agents powerful, balancing that freedom with control and quality assurance is crucial. Developers need tools that give them confidence that these autonomous systems will behave predictably and responsibly.
The latest updates empower organizations with tools to take the guesswork out of deploying trustworthy AI agents:
- Policy in AgentCore (Preview): This feature lets you define explicit boundaries for agent actions, intercepting calls to tools before execution using permissions with fine detail. It acts as a gatekeeper, verifying whether an agent’s intended action aligns with predefined rules, and is integrated into the AgentCore Gateway for swift operation.
- AgentCore Evaluations (Preview): These allow continuous, real-world monitoring of agent performance through built-in evaluators assessing parameters like correctness, helpfulness, safety, and more. Custom evaluators can also be created to reflect specific business metrics, providing ongoing insights into agent behavior.
But that’s not all—new functionalities expand what agents are capable of:
- Episodic Memory for Long-Term Learning: Agents can now learn from past experiences and adapt their behavior. Think of booking travel: over time, an agent can recognize your preferences, such as resorting to flexible flight times for business trips, and proactively suggest options—just like a seasoned assistant.
- Bidirectional Streaming for Natural Conversations: This feature supports real-time, flowing dialogue where both users and agents can speak simultaneously. It allows voice interactions to feel more like natural human conversations, enabling interruptions and mid-response adjustments without breaking the flow.
Focusing on control, the introduction of policies is a game-changer. Policies set the rules for what an agent can or cannot do, intercepting tool calls before they happen. You can create these policies using plain language or a specialized open-source language called Cedar, making it accessible even to non-experts. Cedar allows you to specify permissions at a granular level, such as allowing only a secure role to process refunds under certain amounts, and makes auditing easy.
Setting policies is straightforward: create a policy engine in the AgentCore console, link it to a gateway, and define rules either in natural language or Cedar code. For example, a rule might specify that only users with a 'refund-agent' role can process refunds under $200, providing clear boundaries that keep agents compliant.
The platform also introduces AgentCore Evaluations for ongoing quality assurance. Using customizable evaluators, you can monitor agent responses based on criteria like accuracy, helpfulness, safety, and bias. These are visualized in Amazon CloudWatch, allowing easy setup of alerts and automated responses when agent quality drops below desired levels—so issues are detected and addressed promptly.
The evaluation system works during testing phases—preventing faulty agents from reaching users—and can be integrated into real-time operations for continuous improvement. For example, if customer satisfaction scores fall or if politeness scores decline, the system triggers alerts, enabling swift corrective action.
Building custom evaluators further tailors quality metrics to specific organizational needs. You specify which model will judge responses, configure scoring parameters, and select whether evaluation runs per interaction or session. This flexibility allows businesses to create nuanced, context-sensitive assessment tools.
The new episodic memory approach enhances long-term learning, capturing structured episodes of past interactions, including context, decisions, and outcomes. Over time, agents recall these lessons to improve consistency and decision-making efficiency—much like an experienced assistant learns your preferences.
For conversational interactions, bidirectional streaming allows agents to listen and respond dynamically, supporting interruptions and context switches seamlessly. This results in more natural, human-like conversations, reducing the engineering effort required to build such fluid interactions.
And yes, these powerful new features are available now in multiple AWS regions worldwide, with pricing that adapts to your usage—no upfront commitments required. They also integrate smoothly with popular open-source frameworks like LangChain, CrewAI, and LlamaIndex, giving you extensive flexibility.
So, is total autonomy in AI agents a risk or an opportunity? And how far should control go before it stifles creativity? Share your thoughts below—this is a discussion that’s just beginning, and your insights could shape the future of responsible AI development.
