The growing popularity of Generative AI (GenAI) solutions has prompted many businesses to explore or implement autonomous agents. Many leaders are exploring sophisticated AI agents to streamline task management and operational efficiency.
While AI agents and workflows can be valuable in the long term, many enterprises can achieve immediate return on investment (ROI) by focusing on orchestrated large language model (LLM) workflows that are easier to govern, scale, and measure. Therefore, this article highlights the benefits of using AWS Bedrock and orchestration platforms, such as Astro by Astronomer, to maximize ROI and enhance data governance.
The future is now: GenAI is here, but Enterprises need more than demos
Gone are the days of early GenAI adoption, where businesses eagerly sought new solutions for better data processes. Now, leaders are integrating GenAI through pilot projects and assessing internal tools for effectiveness. But the key question is: Are these tools truly what you need?
Many GenAI tools on the market primarily demonstrate the capabilities of AI agents and agentic workflows. Organizations with enterprise systems are encouraged to explore autonomous agents because they provide the scalability and rapid processing that many businesses require to enhance workflows for maximum efficiency.
However, what we don’t often hear in the demos is that agent-based systems aren’t the only option available to scale growth. LLM workflows can also be implemented in an enterprise system to achieve ROI and measurable results. Therefore, it’s essential for leaders to carefully assess their needs before integrating AI technologies into their operational strategies.
But first, let’s talk about AI agents
Before deciding if LLM workflows are right for you, it’s important to understand AI agents and how they differ from other GenAI solutions. AI agents are autonomous systems powered by LLMs and AI tools, designed to perform tasks independently. However, this autonomy can introduce more risk and lead to less control and transparency in decision-making, making it difficult to measure effectiveness.
While agent-based systems may work well in dynamic environments, enterprises that prioritize reliability and governance often benefit more from structured workflows with LLMs. These workflows prioritize repeatability, auditability, and integration with data pipelines, offering clear guardrails to mitigate compliance risks.
The other side of GenAI: Introducing LLM workflows with real guardrails
In contrast to agent-based systems, LLM workflows operate within structured environments where predefined steps provide explicit rules for data processing. This setup allows for greater control over the data the model accesses and the automated processes involved.
Implementing predefined and repeatable workflows also enables IT leaders to effectively oversee automation, ensuring transparency and accuracy in decision-making and data access. For example, LLM workflows can incorporate specific rules for handling input data and provide standardized responses to user inquiries, such as redacting personally identifiable information (PII) that is found in a dataset.
Therefore, data governance, security, and compliance are central to these workflows, as each step in the process is carefully mapped and monitored for auditing and evaluation purposes. Consequently, LLM workflows work well for enterprises that require adherence to strict governance protocols and expect predictable outcomes with measurable results.
Astronomer + AWS Bedrock: GenAI with guardrails
Businesses can start utilizing LLM workflows by adopting tools with strong data governance models for GenAI. For example, Astronomer, a DataOps platform built on Apache Airflow, offers enterprise-grade capabilities and a managed AI SDK for seamless LLM integration into production workflows, enhancing task automation and scalability.
AWS Bedrock complements this by providing access to various foundation models, like Amazon’s Titan, Anthropic’s Claude, and Meta’s Llama 2, all within a secure infrastructure. It features robust safeguards for sensitive data and reduces hallucinations through tools like Amazon Bedrock Knowledge Bases, which enable cross-referencing with authoritative sources.
Together, Astronomer and AWS Bedrock allow enterprises to fine-tune models with proprietary datasets and manage LLM workflows using Astronomer’s Airflow-based AI SDK. This unified solution enables businesses to create secure data pipelines without relying on third-party APIs, which reduces security risks and workflow disruptions.
GenAI governance starts with pipeline design
While many enterprises are focused on adopting new tools, GenAI governance models are crucial for designing effective data pipelines. According to an IBM Institute for Business Value survey, 68% of CEOs believe that governance for GenAI should be integrated during the design phase rather than added later. When data governance takes a backseat to tech adoption, it increases the risk of misinformation and hallucinations from generative AI.
To combat this issue, effective data governance models incorporate safeguards such as auditing and access controls, which trace where and how data is utilized. By integrating Astronomer’s DataOps capabilities with AWS Bedrock’s GenAI workflows, enterprises can significantly improve data governance protocols and enhance security.
Specifically, Astronomer and AWS Bedrock enhance governance through:
- Data lineage and observability: Astro Observe monitors the health of your data pipelines by tracking lineages, detecting anomalies, and ensuring data quality.
- Role-based access control (RBAC): Ensures that only authorized users have access to specific data assets and pipelines.
- Audit logs: Records all activities in your account and workspaces by tracking who is doing what and when. These logs are essential for maintaining compliance with regulatory standards.
- Data encryption: Protects enterprise data through various encryption methods, including encryption in transit and at rest, to safeguard information from bad actors.
- Compliance standards: Supports compliance with multiple industry-standard frameworks and certifications, including SOC 2, GDPR, HIPAA, and PCI DSS.
- Amazon Bedrock Guardrails: Implements guardrails to remove sensitive data and information from user inputs and responses. This is crucial for safeguarding customer and client data.
Measuring what matters: GenAI ROI you can truly track
When data inputs, model outputs, timing, and outcomes are fully observable, you can track how workflows influence key metrics, such as error rates, accuracy, response times, and detect indicators of model drift. By evaluating and addressing these issues, teams can scale their GenAI workflows without compromising on what matters, i.e., trust, quality, and compliance with regulatory requirements.
Increased observability also makes it easier to identify areas where workflows can improve, helping you maximize your ROI by ensuring your GenAI solutions work effectively. Regardless of the resources invested in building secure data pipelines, reaping the full benefits requires consistent maintenance and leveraging collected insights to optimize orchestration over time.
Last, but not least: It’s time to shift the GenAI mindset
While autonomous agents play a crucial role as GenAI architectures evolve, for most enterprises, starting with orchestrated LLM workflows provides the necessary control, observability, and security to build trust in AI-powered systems before layering on more autonomy. In this case, shifting from agents to orchestrated workflows could maximize your ROI from GenAI.
Within the market, integrated solutions like Astronomer and AWS Bedrock stand out by emphasizing data governance and security in the design of LLM workflows. While AWS Bedrock offers a secure environment for fine-tuning models, Astronomer’s SDK transforms those models into fully orchestrated systems.
If you’re ready to advance beyond the trial-and-error stage of GenAI adoption, now is the time to consider tools prioritizing data governance, security, and measurable outcomes, ensuring a return on the time and resources you invest in building efficient automated workflows.
