At first glance, agentic AI might sound like an overwhelming concept. How do you create a proactive, ever-helpful assistant that not only seems to know everything but also understands you intimately? The answer lies, as with most complex systems, in breaking the problem into smaller, manageable parts—and then thoughtfully assembling them into something powerful.
And here's the good news: many of those parts already exist. Agentic AI isn’t built from scratch—it’s built from existing capabilities arranged in new, smarter ways.
The core building blocks of agentic AI
At its core, agentic AI is built from five essential components:
- Traditional tools
- Intelligent tools
- Knowledge
- Workflows
- Agents
Each plays a distinct and important role in delivering useful, context-aware, and proactive behavior.
Tools: Getting the basics done
Tools are your utility functions—the building blocks that handle specific tasks such as pulling in data, performing calculations, triggering notifications, or connecting to APIs. These have been used in automated systems for years and are well understood.
What’s different in an agentic context is how tools are designed. It’s not enough to just surface raw data. Tools should deliver context-rich content. For example, rather than fetching rows from a CRM, a tool should return a customer’s full profile, pulling from multiple systems as needed. Similarly, instead of exposing raw calendar data, a tool could return user availability with preferences already taken into account.
Intelligent tools: Powering up with GenAI
Intelligent tools bring the capabilities of generative and predictive AI into the mix. These tools can summarize long documents, classify intent or sentiment, translate between languages, or even check visual assets for brand compliance.
These tasks—once considered technically difficult—are now accessible and often easy to implement. However, it’s important to strike a balance: while GenAI is powerful, it’s not always the best or most efficient solution. Some problems are still better solved using classic algorithms or rules-based systems. Use AI when it adds clear value, not just because it’s available.
Company knowledge: Context is everything
Company knowledge is the often-overlooked foundation that makes agentic systems truly useful. Without internal context—like product details, policies, tone of voice, user roles, or operational procedures—an agent can’t act meaningfully.
Embedding this knowledge into tools, workflows, and agents enables smarter decisions and more relevant actions. An agent without company knowledge might return a grammatically correct answer—but not the right one. Company-specific context is what transforms generic intelligence into personalized, actionable assistance.
Workflows: Chaining tools for smarter outcomes
Workflows connect multiple tools in a pre-defined process to achieve more sophisticated results. Think of them as orchestrated processes that combine both traditional and intelligent tools to complete multi-step tasks.
Here’s a common example:
- Retrieve customer data using a traditional tool
- Summarize the data with a generative AI tool
- Translate it into the account manager’s language
- Send the result via email
Workflows themselves can be encapsulated as tools and reused within other workflows or agents, creating a modular and scalable ecosystem.
Agents: The brain of the operation
Agents are where everything comes together. They dynamically orchestrate tools and workflows, making decisions on the fly based on the task at hand.
An agent might:
- Analyze a query
- Formulate a plan of action
- Sequentially use tools to gather information, reason about it, and respond
- Adjust the approach if intermediate steps change the context
This kind of dynamic reasoning is what makes agents truly “agentic”—they’re not just executing static scripts. They adapt. They iterate.
The real magic: Agents that evolve
The power of agentic AI doesn’t just come from intelligent planning—it comes from the system’s ability to grow over time. Agents have a memory to learn and adapt from previous experience. Also, agents can utilize new tools and even other agents flexibly.
This creates emergent behavior: higher-level intelligence that arises from simple, modular pieces working together. It's a modern take on "divide and conquer"—each component is relatively simple, but their collaboration leads to surprisingly sophisticated outcomes.
Final thoughts
Agentic AI isn’t a moonshot. It’s a thoughtful assembly of tools, knowledge, and orchestration—enhanced by AI but grounded in practical engineering. By focusing on five key building blocks—traditional tools, intelligent tools, company knowledge, workflows, and agents—you can build systems that don’t just respond to inputs, but take meaningful, context-aware action.
