In an enterprise setting, AI agents are software systems that can reason, plan, and act to accomplish goals with limited human involvement.

They differ from traditional tools in several ways:

• Beyond fixed rules: Rule-based RPA follows predefined scripts. AI agents, by contrast, use memory, reasoning, and planning to adapt to changing inputs and conditions.
• From single prompts to workflows: Basic generative AI responds to isolated prompts. AI agents can execute multistep workflows, evaluate results, and adjust their approach until they reach a defined goal.
• Goal-oriented behavior: Agents can work toward higher-level objectives (for example, managing an entire claims process or performing code reviews), not just individual tasks.
• Autonomy spectrum: Organizations can choose the level of autonomy—from assistive agents that help humans complete tasks, to goal-driven agents that manage processes, up to fully autonomous agentic systems that can set and achieve goals with minimal oversight.

Analysts expect this shift to be significant. Gartner predicts that by 2028:

• More than 33% of enterprise applications will employ AI agents.
• A meaningful share of day-to-day work decisions will be made autonomously by AI agents, up from essentially 0% in 2024.

In practice, this means AI agents don’t just answer questions—they reimagine how work gets done by continuously working toward business outcomes such as processing claims, managing enterprise applications, or accelerating software development.

Organizations are already using agentic AI to create measurable value across four main areas:

1. Boosting workplace productivity

AI agents integrate with existing tools and workflows to automate routine tasks and decisions.

• The NFL used AWS business agents to cut new-hire training time by 67% and deployed a generative AI assistant so fans can ask a wide range of questions and get instant answers.

2. Driving research and innovation

Agents can analyze large data sets, identify patterns, test hypotheses, and generate insights at scale, helping teams move from idea to outcome faster. For example, organizations are using AI-powered workspaces like Amazon Quick Suite to compress weeks of research into same-day insights with tools such as Quick Research.

3. Optimizing business workflows

AI agents understand context and autonomously manage end-to-end processes.

• Syngenta used agentic AI to provide personalized seed recommendations and crop growth modeling, leading to a 5% increase in crop yields.
• Cognizant automated mortgage compliance workflows and achieved over 50% productivity improvements.
• Formula 1 built an intelligent root cause analysis agent with Amazon Bedrock, reducing issue resolution time by 86% and cutting triage time from days to minutes.

4. Accelerating software development

AI agents can generate code, review it, detect bugs, and run tests.

• Thomson Reuters used AWS Transform to modernize legacy code, reducing costs by 30% and increasing transformation speed by 4x compared to manual efforts.
• Druva built DruAI Agents on Amazon Bedrock AgentCore, moving from simple query-based AI to fully agentic systems. Early results show 63% of issues resolved automatically and 58% faster resolution for escalations, with a target of 70% reduction in investigation time and 90% automation of routine data protection.

These examples show that agentic AI is not just experimental. According to recent data, over 50% of companies already have AI agents in production, and nearly 80% have agents in development, many of them on AWS.

There are two main paths to getting started with agentic AI, and both can be supported on AWS:

1. Deploy specialized agents for quick wins

Specialized agents are pre-built for specific functions such as customer service, IT operations, or finance workflows. They offer:

• Faster time to value by plugging into existing processes.
• Lower implementation risk, since they’re designed around common enterprise use cases.

Examples include AWS solutions like Amazon Quick Suite (for AI-powered workspaces, sales productivity, and research) and other domain-focused agents that streamline operations.

2. Build custom agents for tailored needs

For organizations with unique requirements or existing open source investments, custom agents provide more flexibility:

• Use Amazon Bedrock and Strands Agents to build agents on top of enterprise-grade foundation models.
• Deploy and operate them with Amazon Bedrock AgentCore, which supports multiple frameworks and models.

Critical foundations for enterprise readiness

Regardless of the path, three areas are key:

1. Data management
   AI agents need seamless access to structured, unstructured, and multimodal data. AWS offers a comprehensive set of services for the end-to-end data journey, helping you organize, secure, and optimize data for agentic AI workloads.
2. Secure and responsible AI
   As agents gain more autonomy, they must operate within clear security, ethical, and regulatory boundaries. AWS is the first major cloud provider to receive ISO 42001 certification for AI management and provides automated reasoning checks that can help minimize hallucinations and reach up to 99% verification accuracy, improving reliability and safety.
3. Multi-agent environments
   As use cases grow, single agents can hit limits. Amazon Bedrock AgentCore supports multi-agent environments where specialized agents coordinate using protocols like Model Context Protocol and Agent-to-Agent. This allows agents to share context, orchestrate tasks across domains, and reduce integration friction.

With these foundations in place—data, security, and multi-agent orchestration—AWS provides the models, tools, infrastructure, and expertise needed to build, deploy, and scale trusted AI agents that can reshape how your organization works.