Technology advancements around AI and Large Language Models (LLMs) are moving at lightspeed. It seems like only a few months ago, tech leaders and practitioners were questioning the practical value of AI and ML in their organizations. Today, however, AI agents are quickly emerging as a powerful tool that is revolutionizing how organizations operate and make decisions.
To have a good AI strategy, you need a good data strategy, and that remains true now more than ever.
To tackle this topic, we will be publishing a three part series that explores how AI agents work, why data foundations are critical for implementing AI strategies, and how you can quickly get started today.
What is an AI Agent?
To begin, let’s first talk about what an AI agent actually is. Also known as “agentic AI”, an AI agent is simply a system that can independently make decisions and take actions to accomplish specific tasks. What makes this so compelling is that it often acts with the level of independence of a human.
Agentic systems have historically been very difficult to implement because they required complex machine learning models or rules-based programming. However, increased data availability, improved computational power, and a deeper understanding of foundational models have made agentic AI more accessible and usable than ever before.
How Do AI Agents Work?
Without getting too far in the weeds, AI agents operate using four basic steps - perceiving, analyzing, decisioning, and action.
First, a user will use natural language to tell the agent specifically what to do in the form of a prompt. The user would provide all of the details it would provide another person to ensure the output is specific to what the user is asking. The more details provided to the agent, the better the output.
On the user experience end, this step may feel similar to interacting with a run of the mill chatbot--and that's partially by design. Agentic systems are designed to be simple and to operate in natural languages. But the similarities with chatbots end there.
While a chatbot is likely to follow a script or follow a pre-defined branch of conversation, an agent will process that prompt by breaking it down into smaller tasks and subtasks. It will gather information from databases and applications, and draw insights from that data.
Once it has analyzed the data, the agent will evaluate potential actions and determine the best course of action. These decisions can be based on algorithms, rule-based logic, and predictive models.
Finally, the agent takes that decision and executes it. Actions can be things like updating a database, drafting content, or controlling physical machines.
AI as a Competitive Advantage
Agentic AI is increasingly becoming a competitive advantage to organizations. They can drive significant value through improving efficiency and by creating a better experience for their customers. Below are just a few of the benefits of deploying AI agents in your organization.
Increased productivity. AI agents can increase efficiency by automating routine and manual tasks. It can do things like draft thought leadership for marketing teams and provide account research to sellers so they can increase their win rate with more custom outreach.
Enhanced data-driven decision making. AI agents can also consume and process data much faster and more accurately than a human. They can quickly uncover patterns that will enable things like better sales forecasting and more advanced pricing decisions.
Better customer experiences. Through tailored customer service, AI agents can personalize product recommendations, optimize campaign scheduling, and automate email response systems.
We’ll spend more time delving into specific use cases in a future blog post.
The Importance of Data in Building AI Agents
While agents possess incredible decisioning abilities, an AI is only as powerful as the data that's feeding it. Organizations that are successful in implementing AI will have strong data backbones to power those initiatives. We typically think about three dimensions of data readiness:
- Data Quality: Is the data clean, deduped and organized? Do you have clear semantic definitions and defined relationships between datasets?
- Data Connectivity: Is your tech stack fully obervable to AI agents? Can your agents easily take into account data from any source and take into account the structure of your tech stack in its recommendations?
- Data Collaboration: Are all agents in your system operating on the same information? As AI adapts to serve every team in your organization, every team will need access to the full wealth of data your organization possess--even the nontechnical ones. Does your organization have a clear way to govern those access points, identify the best datasets and share them across tools to prevent silod decisionmaking?
Here at Census, we're taking steps toward making quality data that's fully observable to AI and accessible across the organization a reality. Our Universal Data Platform allows data teams and business teams to unify, transform and activate data across any platform in their tech stack. Learn more here.