What is Agentic AI?
Introduction
Artificial Intelligence (AI) is evolving at a remarkable pace, reshaping how we interact with technology and make decisions. What was once a simple co-pilot approach, where humans directed technology, has evolved into more autonomous "autopilot" functionality or what some are now calling “agentic.” In this landscape, AI is assuming greater responsibilities, shifting our roles from active participants to agent managers, editors, and conductors. At Perigon, we see this shift as an opportunity to connect next-generation agents with the right information, equipping them to take actions or perform analyses with comprehensive perspectives and insights.
From Assistants to Agents: The Evolution of AI
AI technology is progressing from being reactive assistants to proactive agents, capable of autonomously handling a broad set of tasks. It’s become increasingly common for us to bring one-off tasks to ChatGPT, Claude, or MetaAI for support. While assistants respond to these user commands in a very transactional way, agents are designed to act independently, using machine learning to anticipate user needs, understand multi-step operations, and ultimately proactively take action based on a desired outcome. These agents aren't just booking reservations or finding availability for meetings; they're now interpreting desired goals, laying out a path to achieve them independently, and addressing strategic challenges.
The Agentic Mindset: Proactive Intelligence in Action
An AI agent or series of agents can interpret a concept for new software, write the code, formulate a marketing plan, recommend pricing, and even suggest a name and build the website. This comprehensive approach, integrating a variety of activities, brings AI closer to achieving much more impactful results. Moreover, AI agents learn from human feedback—without becoming defensive—on their decisions and can evolve to enhance their performance. With this approach, agents can quickly become integral parts of collaborative decision-making processes and streamline workflows.
A standout feature of AI agents is their ability to be more personalized and proactive. By analyzing user behavior, preferences, and historical data, these agents tailor recommendations and actions based on what has worked previously or what they have learned from human feedback. Additionally, they can uncover insights or strategies that humans might have otherwise overlooked. The value of these agents lies in their efficiency—saving time, identifying hidden patterns, and reinforcing feedback.
For a deeper dive into the practical application of these concepts, consider the insights from Joon Sung Park, as discussed in a recent Forbes article on building generative AI agents. This resource offers valuable perspectives on the complexities and innovative strategies involved in developing AI systems that can autonomously perform a variety of tasks.
Enhancing Decision-Making through Tailored Intelligence
Imagine a real estate investor receiving tailored alerts about local market trends, a new headquarters being announced or changes to local laws. Maybe a CEO receives daily briefing that synthesizes and distills information from news, podcasts, and financial reports that sparks a potential strategic partnership, makes here aware of a big customer’s strategic shift, or alerts her to an emerging competitor gaining traction. This could take a human hours, every day, to parse through, interpret, write, and update. These agents can quickly consume information, alert and brief people in the style they prefer, answer follow-up questions, help them evaluate a decision, and adapt future collaborations accordingly. In business, these agents enhance productivity and decision-making by providing insights and recommendations across different departments and functions, freeing up time for other creative and strategic tasks.
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Building Trust and Oversight: The Path to Autonomy
While AI agents hold great promise, trust remains a concern. We're not ready to take our hands completely off the wheel, but with close oversight and exploration, we can gradually trust AI to act on our behalf. Currently, we're comfortable with AI making recommendations and describing its next steps. If we agree, we might allow it to act—maybe in the future, automatically. However, for now, keeping a human in the loop is crucial. This oversight allows AI to learn how humans might react in various situations and to build trust. Additionally, we want to understand what knowledge an AI agent relied upon to do its work.
A live example of this is Tesla's revised strategy for full self-driving. Previously, they had taken an approach to hard code decisions which led to some ethical and moral debates. Now, with the growing power of compute, they can take an alternative approach that has already proved to be much more effective, where the AI makes decisions based on how good drivers have reacted in similar circumstances based on thousands of hours of recorded video captured by Teslas in the real world.
We want AI to do what we would do or find valuable, while also discovering new insights we might have missed. By incorporating human oversight and feedback, AI agents can learn and improve, building trust and enhancing autonomy.
To further understand the nuances of building trust in AI systems, this article from Harvard Business Review offers an in-depth look at how companies can create trustworthy AI assistants. This resource explores methods and practices that ensure AI systems are reliable and transparent, key factors that are essential for fostering user trust.
Perigon's Vision: Making Information Actionable
Every day at Perigon, we work to make the world’s information more useful and actionable. We provide better insights, eliminating noise and focusing on relevance with intentional applications of artificial intelligence and machine learning. Our technology aligns with an agentic mindset, equipping AI with the knowledge to be relevant, timely, and trustworthy, empowering humans to make informed decisions with ease. We bridge the gap between data and action, enhancing daily decision-making through intelligent, context-driven insights.
Conclusion
The future of AI is rapidly evolving, with agents becoming increasingly intelligent and autonomous. This shift towards an agentic mindset in an AI-first world promises to revolutionize our interaction with technology, enhancing productivity, personalization, and innovation. At Perigon, we're excited to be at the forefront of this transformation, enhancing decision-making through superior insights.