Explore the power of knowledge graphs and knowledge-driven agentic AI in our guide, which explains why they are the definitive next step for LLMs and how they can deliver trustworthy, contextual, and grounded insights for your business.
What is knowledge-driven agentic AI?
Everyone is talking about agentic AI as the next evolution of LLMs, but did you know they've existed for years? What's truly new is how explicit symbolic representation is pushing agentic AI to the next level. Learn about what it is, its benefits and why knowledge-driven AI is the true next step for LLMs.
Table of contents:
What is an ‘AI agent’?
First, let’s review the fundamental characteristics of an AI agent. One of its key qualities is having autonomy: an agent can act—to some extent—independently towards a particular goal or activity. It’s also able to perceive its environment via sensors, and act reactively and proactively towards the goal the agent is designed to achieve.
Key characteristics of an AI agent:
- Agency and autonomy: Ability to act, pursue goals & make decisions
- Understanding: Representation & detection of user intent
- Planning: Breaking down complex tasks into actionable steps
- Tool usage: Equipped with capabilities to interact with APIs & external systems
- Memory and adaption: Maintaining context & learning from past experiences
- Collaboration: Collaborative problem solving between humans & AI agents, as well as multi-agent collaboration
What is knowledge-driven agentic AI?
Knowledge-driven agentic AI, on the other hand, is an AI agent that leverages an explicit knowledge base to follow commands, take actions or generate answers. Through this knowledge base, agents are able to have a rich and contextual understanding of your enterprise and specific domain, as well as understand the user. The capabilities towards understanding goals and developing plans can be facilitated with knowledge graphs. Together, this combination results in what we call ‘knowledge-driven agentic AI’ or ‘neuro-symbolic AI’. A knowledge graph-enabled agent becomes much more capable than one that is purely based on LLMs.
Benefits of knowledge-driven agentic AI
What does this look like for your enterprise?
If you’re seeking to adopt AI technologies to support your enterprise data environment, naturally, you’ll want to trust in the outputs of these agents. However, as with all LLMs, they still operate in “black boxes” and lack the ability to explain how they conduct a particular action or output. LLMs are also prone to hallucination and fabricating answers from a mish-mash of information online. These hallucinations and inaccuracies can cost companies up to millions of dollars and result in legal and operational risk, especially in heavily-regulated environments or when using agents for critical business decisions.
When linked to your enterprise's knowledge base, agents are instead made more trustworthy because you can trace the why or how of their actions and answers. You’ll be able to know the source from which it retrieved its answer, how up-to-date the source data is and other contextual information that can give you a clearer picture of how the agent arrived at its conclusion. All answers are rooted in facts based on your enterprise data and delivered in the same vocabulary/terminology used within your enterprise, making it a much more reliable technology.
How does knowledge-driven agentic AI work?
Tools like metis, our enterprise knowledge-driven AI platform, enable you to leverage AI agents and LLMs while interacting with your enterprise data. The platform offers a conversational interface where you can input a query using natural language, and ask “How many car manufacturing facilities do we have in North America?”, or “What is the average time to complete a specific task or process?" and receive an answer that’s grounded in facts, right in the chat. You can then take it a step further and ask the agent to transform these insights into a chart or a table that you can easily share with relevant stakeholders.
With metis, you can even leverage knowledge-driven AI to execute a myriad of tasks that will support business optimization, decision-making and more. metis ensures that you can mitigate against bias naturally found in GenAI, generate results grounded in enterprise context, and ensure safety and security when combining GenAI and enterprise data.
Curious to learn more? Discover what metis can do for your business.
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