This blog was written by our friends at the data science and analytics platform KNIME. If you want to learn more about how to integrate generative AI into your data workflow using visual programming, join us on Wednesday, June 18 at 2 p.m. for a free Codecademy community event with KNIME. RSVP for the virtual event here.
We’re at the tipping point of being able to use all of our data exactly when, where, and how we want to. Agentic AI could take us over it to deliver lasting value.
Making sense of all of our data has so far been surprisingly hard to do. Tucked into pockets across the organization, insights were limited, and action was based on the availability of human resources.
Marketers check lead generation and campaign data, sales managers track deals and sales cycles, finance teams examine profit and loss. But the moment you have questions that touch on data outside of your field, you often don’t know where to find it, who to ask, and how to access it.
Learn something new for free
Agentic AI is making all our data much more broadly accessible so that we can get more value out of it. Imagine you had an “Ask me anything” AI agent for all marketing, sales, and customer-related data: Anytime you had a question on leads and customers, the agent would get you the answers. That’s because they have the “agency” to autonomously make decisions and act.
This article covers what agentic AI is, why it matters, and how beginners can start working with it.
What is agentic AI?
Agentic AI refers to systems that can act autonomously to achieve a goal.
Unlike large language models (LLMs), which simply return a response to a prompt, AI agents can take that response and do something with it like fetch data, make a decision, generate a report, etc. Agentic systems can choose which actions or tools to use based on the current situation.
There are two main varieties of agents:
Agentic applications that interact directly with people: The “Ask me anything” agent is an example of an agent that interacts directly with you. You can ask questions like “Do we have customers in Prague? What’s our history with ABC Corporation? Which customers have attended more than three of our events?” And the AI agent selects the right tools and data sources behind the scenes to answer your questions.
Agentic services that run in the background, available as tools for other applications or agents: This could be an agent that autonomously triggers a series of emails to new leads based on their activities, like visiting your website, attending an event, or purchasing a subscription.
What are the key components of AI agents?
Agents are typically made up of a few key components:
- Tools: Tools handle specific tasks like aggregating data or predicting events, etc.
- Intelligent tools: An intelligent tool extends the functionality of a tool by incorporating a large language model (LLM). This allows it to understand context and perform tasks that involve language comprehension or generation. For example, an intelligent tool might summarize a document, or it could summarize the document and then use the summary to compose and send an email.
- AI workflows: AI workflows are manually orchestrated steps that connect up multiple tools to accomplish more complex tasks. AI workflows orchestrate multiple components — including LLM models, APIs, and logic — to solve complex, multi-step tasks that go beyond what a single model or tool can handle alone. These AI workflows can be dynamically assembled by an agent. AI workflows can themselves often become tools used by other AI workflows within larger systems.
- Agents: Agents are systems that select and use tools dynamically for each specific request. They decide autonomously which tools to use, delegating tasks to more specialized subagents, to accomplish increasingly complex goals.
- Memory: An agent has access to all prior actions it has completed and feedback on those actions so it can adapt behavior or follow patterns based on prior experience. This helps refine the quality of agent decision-making and actions.
Why agentic AI matters
Language models have shown their ability to generate text, but businesses need solutions that drive decisions and action. Agentic systems, with their ability to reason independently and take action, bridge that gap.
Here are some examples of business challenges that can be solved with agentic systems.
- Underutilized data: Organizations struggle to get value from all their data. Agentic systems can act as data workers that continuously analyze this data and surface insights.
- Decision-making bottlenecks: In fast-paced markets, manual approval processes and delays in data analysis can create bottlenecks that reduce business agility. Agentic systems can automate complex decision-making and eliminate bottlenecks.
- Rising customer expectations: Customers want faster, more personalized services. Agentic systems can provide immediate responses based on comprehensive customer data analysis.
What you need to know to get started with agentic AI
To get started with agentic AI, you need to understand how tools, data, and logic flow together.
You’ll benefit from understanding:
Choose your learning path
You can build agentic systems in different environments:
Code-first route: Agent frameworks like LangChain, AutoGen (Microsoft), or Haystack enable you to build AI agents. As a beginner you’ll have to learn how to code first.
Visual route: Visual workflows give you an accessible and intuitive programming environment to construct AI agents in a manageable way. You design your AI and data processes visually, connecting up a logical sequence of operations to form a visual workflow. This makes it easier to track data flow, identify issues, and explain logic clearly.
How to try it yourself
Begin with simple workflows: a sentiment analyzer, a report generator, a customer lookup tool. Then link them. Let the agent decide which one to call, and when. Deploy it as a data app, service, or API. As you build more agents, they can call each other, share memory, and evolve into powerful multi-agent systems.
Agentic AI isn’t a far-off ambition — but an achievable next step.