3 Proven Paths for Enterprises to Get Your Agentic AI Workloads into Production
The past two years have seen artificial intelligence change the way enterprises operate. But since generative AI’s iPhone moment, the pressure is mounting on enterprises to show a tangible ROI from their AI investments.
For the early adopters, the results have been great: 92% of them say their gen AI initiatives have paid for themselves, according to a recent Enterprise Strategy Group survey. But many organizations are still in the planning, piloting or pivoting phases of their gen AI projects.
Technology & Capability Lead for Accenture’s AWS Business Group Chris Wegmann explains. “Our team has garnered experience working on thousands of AI projects. We have learned that when scaling gen AI in particular, 28% of C-suite leaders claim that limitations with data or technology infrastructure are the top issues preventing their organizations from properly scaling. This includes bad data, outdated systems and lack of tested blueprints or governance models. With the adoption of multi-agent systems expected to accelerate rapidly, this also brings new challenges as companies move from experimenting with AI to scaling across the enterprise.”
“We believe to be ready for the AI era, organizations should start by identifying the business areas where AI can drive real impact; invest in preparing data and knowledge assets so AI can reason and act with confidence; and pilot on trusted priority workflows, then expand, orchestrate, and scale across functions,” he elaborates. “By starting in this way, organizations can build AI capabilities that can grow with every wave of innovation.”
Meanwhile, though the technology is powerful, it also raises some concerns. Organizations must ensure AI agents operate within intended boundaries and maintain human control over critical decisions, and conduct regular risk assessment for new potential risks. Organizations also need to understand and manage more sophisticated systems and their potential interactions to ensure the agents can perform complex, multi-step tasks, while ensuring employees have the right training and can adapt to the organizational changes agentic AI brings compared to traditional software tools.
My colleague Maulie Dass, who leads cloud alliances in the Americas, explains four key challenges to introducing AI within enterprises during a recent conversation with Harvard Business Review.
To help our customers navigate this transition and build holistic, integrated agentic solutions, Snowflake collaborates with ecosystem leaders such as Accenture, Amazon Web Services (AWS), and Anthropic.
By offering a suite of deeply integrated features and capabilities from leading global system integrators, cloud, and LLM partners—along with trusted expertise to help you identify use cases and implement the right tools for the job—we’re enabling customers to build and deploy agentic AI initiatives in ways that reduce complexity while maintaining flexibility. We connect a range of AI models and tools to where their data already lives, all without the headache of building complex data pipelines or the security risk of moving or copying datasets, which delay progress and measurable outcomes.
In this blog post, I’ll offer three strategies for building production-ready agents, highlighting the combined power of a connected ecosystem. For more detail, explore our ebook, “Deploying AI Agents at Scale: 3 Patterns to Move Beyond POCs.”
The foundation for AI agents in production
Before diving into agent types, it's important to understand what’s necessary for any successful agentic AI deployment. A solid data foundation is the backbone of any AI initiative. Agents need access to unified, governed, trusted, and high-quality structured and unstructured data. This modern, AI-ready data estate is crucial for agents to function effectively. At Snowflake, we always say: There is no AI strategy without a data strategy.
Then there are security and governance controls to help you keep your data in a secure cloud environment. Both are nonnegotiable. In collaboration with partners like Anthropic and AWS, we bring leading LLMs to run in Snowflake where the data resides. We’ve invested heavily in building native integrations and implementation patterns with AWS services like Amazon Bedrock Agentcore, Amazon SageMaker Studio and Canvas, and Amazon Q for Business, making it easy for our customers to benefit from both providers without impacting security or ease of use. Finally, we’ve mutually committed to open standards like Model Context Protocol (MCP) and Agent-2-Agent (A2A) for agentic interoperability, so that organizations can tailor their AI agents and tools to specific use cases without hindering collaboration between agents and tools from different providers.
Underlying everything is a flexible, reliable and scalable cloud-based infrastructure, which provides high-performance compute today alongside a versatile foundation that scales instantly for business needs of tomorrow.
Three proven paths for enterprises to deploy agentic AI today
I’ve seen a pattern emerge among the enterprises that have deployed agents into production. Their agents tend to fall into three categories, which we'll explore in order of complexity, starting with patterns that are relatively easy to deploy and manage and ending with patterns that require more configuration.
Data agents: They focus on hyperaccurate, data-grounded insights from both structured and unstructured data. AWS services complement Snowflake Intelligence, which calls on tools like Snowflake Cortex Agents (Analyst and Search) to enable comprehensive data access and processing, whether the data resides in Snowflake or S3. This integration is key to unlocking secure, unified data insights across cloud boundaries.
Conversational agents: These agents emphasize conversational interfaces embedded directly into familiar business workflows. Amazon Q seamlessly integrates with Snowflake Cortex Agents, enabling enterprises to deploy intelligent chatbots within familiar tools like email, chat tools or content repositories. This partnership delivers AI assistants where users already work.
Multi-agent systems: These agentic systems orchestrate multiple specialized agents for complex, multistep tasks. Strands Agents, as the SDK, build out this workflow, and the A2A protocol enables agents within this workflow to communicate with ease and security. Amazon Bedrock Agents, as the orchestrator, leverage LLMs to direct multistep workflows intelligently. This integration allows customers to mix and match specialized agents from both Snowflake and AWS, providing unmatched flexibility and power.
Your path to production-ready AI agents starts here
Agentic AI offers a powerful path for enterprises to move beyond experimentation to unlock the value of AI. Our product teams at Snowflake, AWS and Anthropic work hard to build turnkey integrations so you can use the tools and services required to drive meaningful business outcomes with AI agents, without the complexity.
But that doesn’t mean the journey will be a walk in the park—finding the right technology is just one piece of the puzzle. In working with partners like Accenture, customers gain deep expertise across multiple providers so they can apply the right technology to solve big problems and drive transformational outcomes.
“According to our research, AI can potentially impact 44% of all working hours across industries in the US,” Wegmann shares. “While 92% of C-suite leaders believe people in their organizations have been trained to use AI efficiently, only 72% of employees agree with this and suggest there is a lack of resources for training support or difficulties integrating AI into their daily work. Moreover, 55% of the employees feel that more comprehensive training is required and 45% believe that clear guidelines on responsible usage would encourage them to use gen AI tools.”
The bottom line? You’re not alone. Lean on your ecosystem to navigate through this era of monumental change.
To discover the technical blueprints, detailed tool integrations, customers who have done it and practical considerations that will guide your organization from AI proof of concept to production, download our ebook, “Deploying AI Agents at Scale: 3 Patterns to Move Beyond POCs” today.
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