Agentic AI

Agentic AI is a form of AI that operates autonomously, enabling it to achieve specific goals or tasks without constant human intervention. Traditional AI usually depends on human input, but agentic AI can make decisions on its own and act in real-time. An agent in this context refers to an autonomous entity within the AI system that perceives its environment, processes information, and takes actions to achieve predefined objectives.

Agentic AI is built to handle complex tasks that require reasoning, planning, and problem-solving, making it ideal for tasks like optimizing ad placements across multiple platforms. It’s particularly useful in industries like healthcare and finance where making quick and flexible decisions is essential.

How does agentic AI work?

Agentic AI uses a structured process that allows it to autonomously achieve specific goals, adapt to new information, and improve over time.

Here’s a brief overview of how it works:

1. Goal setting - The process begins with defining a clear objective or problem for the agentic AI to address. For example, a marketing team wants to improve email engagement rates by 20%.
2. Data processing - Once the goal is established, agentic AI processes data from various sources, including cloud platforms and internet of things (IoT) devices like sensors or customer interactions. By analyzing this data in real-time, the agentic AI system gains the insights needed to make an informed decision. In the email engagement example, the agentic AI would analyze customer behavior data like email open rates and click-through rates to identify trends and preferences.
3. Planning and problem-solving - Using advanced algorithms, agentic AI develops multi-step plans to complete its goal. It can identify potential obstacles and adjust its approach to overcome any challenges as they arise. To improve email engagement rates, agentic AI would create a plan that includes segmenting audiences based on engagement history and subject line tests.
4. Taking action - Based on its planning, agentic AI executes tasks without human intervention. These actions are goal-oriented and designed to deliver measurable outcomes without requiring constant oversight. After creating a plan, the agentic AI can autonomously launch the email campaign and send personalized messages to each audience segment. 
5. Feedback loop - After completing the tasks, agentic AI evaluates its performance by analyzing results against the original goal. The AI agent uses this feedback to refine its algorithms and improve future performance, ensuring continuous learning and efficiency. The agentic AI would analyze the open rates and conversions across segments after the campaign ends, identifying which strategies worked best and applying them to future campaigns.

What's the difference between agentic AI and generative AI?

Below are the primary differences between agentic AI and generative AI:

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How do other AI-related technologies interact with agentic AI?

Agentic AI integrates with various AI-related technologies to enhance its decision-making and autonomous capabilities.

LLMs

Large language models (LLMs) play a crucial role in agentic AI frameworks by orchestrating the behavior of multiple agents, such as other AI agents or tools. These intelligent agents can range from advanced AI models to tools like search engines that retrieve data from online or internal databases. 

The LLM enables seamless collaboration by interpreting tasks and delegating responsibilities to agents based on user input or system goals. In practice, an LLM can process a request to improve customer segmentation and direct agents to create personalized offers for different audience groups.

Machine learning

Machine learning is a branch of AI that focuses on creating systems capable of learning and improving from data. It’s part of the foundation of agentic AI, empowering the agent to analyze vast datasets, recognize patterns, and make informed decisions. 

By processing these datasets, machine learning helps agentic AI to:

• Identify trends, correlations, and anomalies in the data. 
• Navigate complex environments like supply chains effectively. 
• Continuously improves by learning from new data, refining its algorithms, and boosting its ability to make autonomous decisions. 
• Adapt in real-time by using learned patterns to adjust its actions when encountering new data or unexpected situations. For instance, rerouting deliveries during supply chain disruptions. 

Benefits of agentic AI

Some of the key benefits of agentic AI include:

1. Boosted team productivity and focus - Agentic AI takes over routine operations and complex analytical tasks. In market research, agentic AI can continuously monitor campaign and audience performance and generate actionable insights for data teams or marketers. This lets marketers shift their focus from time-consuming data collection and analysis to developing strategic recommendations that drive business growth. As a result, marketers can focus on high-value tasks that can bring higher order strategic impact.
2. Enhanced customer experiences - Teams can improve customer interactions by delivering highly personalized, real-time responses that adapt to individual preferences. Unlike traditional automation that relies on pre-defined scripts or limited decision trees, agentic automation dynamically assesses customer behaviors, preferences, and needs. 
3. Self-correction - Agentic AI autonomously detects and resolves issues, minimizing the need for human intervention. These systems continuously learn from feedback and adapt their behavior in real time to enhance future performance. This self-correcting mechanism reduces downtime and boosts operational efficiency. 
4. Informed decision-making - By continuously analyzing large datasets, agentic AI lets businesses make smarter decisions based on predictive analytics and actionable insights. Leveraging historical data alongside current trends, agentic AI uses predictive analytics to generate data-driven recommendations.
5. Higher accuracy and reduced errors - By precisely automating complex tasks, agentic AI architecture significantly reduces human error while continuously improving its accuracy over time. 

Challenges of agentic AI

Despite the benefits, there are some notable drawbacks to agentic AI, including: 

1. Transparency - One significant issue with agentic AI is its lack of transparency in how decisions are made. These systems operate autonomously, relying on complex algorithms and data inputs. This “black box” nature makes it difficult for users or stakeholders to understand how the agent makes decisions. As a result, tracing the reasoning behind certain actions or outcomes becomes a challenge.
2. Security - Agentic AI systems can increase  security risk when they have access to sensitive data. If they aren’t properly secured, agentic systems could be exploited by malicious actors.
3. Ethical considerations - The autonomous nature of agentic AI often raises ethical concerns,  particularly when its decisions directly impact individuals’ lives. For instance, agentic AI can influence employment decisions or healthcare recommendations where fairness and accountability are crucial.  
4. Lack of control - Agentic AI’s autonomy can sometimes mean lack of control over its actions. Once deployed, these systems may make decisions that deviate from human expectations. For example, prioritizing efficiency over ethical consideration in decision-making.
5. Bias - Just like other AI systems, agentic AI is susceptible to bias if trained on flawed or unrepresentative data, potentially leading to skewed outcomes. This bias can perpetuate inequalities, such as favoring certain demographics in hiring processes. This bias might result in unfair outcomes across industries like recruitment or lending.

Why agentic AI is rising in popularity 

Several converging factors have made agentic AI highly relevant in today’s landscape. Technological advancements in AI like LLMs and multimodal learning have paved the way for agentic AI. Traditional AI systems relied on static datasets, but agentic AI integrates LLMs with real-time data and external tools.

As global competition intensifies, businesses are looking for ways to reduce dependency on manual labor. Agentic AI presents a path to automation by autonomously managing both repetitive tasks and complex decision-making processes. Its ability to operate independently without constant human oversight has made agentic AI particularly appealing in industries like marketing and supply chain where efficiency is key.

Another key driver behind the popularity of agentic AI is its ability to make real-time decisions by accessing up-to-date information from various sources. Traditional AI models are constrained by static training data, which represents only a snapshot of the past. On the other hand, agentic AI leverages real-time inputs for more accurate insights.

Society has also increased acceptance of AI systems through everyday interactions with chatbots and virtual assistants. This familiarity has eased concerns about AI’s role in the workplace and opened the door for more advanced systems like agentic AI to be integrated into business processes. 

Agentic AI use cases

Agentic AI is changing how industries like marketing, customer service, and healthcare operate by enabling personalized experiences and efficient workflows.

Customer service

Agentic AI enables businesses to deliver hyper-personalized, efficient, and 24/7 support. Traditional AI systems rely on static responses or pre-programmed workflows. But agentic AI leverages real-time data, contextual understanding, and adaptive learning to provide meaningful customer interactions, like resolving complex queries in real time.

It enhances self-service capabilities by guiding customers through workflows tailored to their specific questions. Self-service tools powered by agentic AI reduce the burden on human agents and help customers solve problems on their terms to boost satisfaction and reduce support waiting times.

Product teams can also leverage agentic AI to analyze recurring customer issues and feedback collected by agentic systems, uncovering product improvement and innovation opportunities. Frontline agents benefit from agentic AI through task automation, real-time suggestions, and access to actionable insights that help them resolve issues faster.

Healthcare

The healthcare industry has seen a more personalized and proactive approach to care delivery thanks to agentic workflows in AI. By combining real-time data analysis with autonomous decision-making, agentic AI systems tackle pressing issues in patient care and drug development. 

Administrative burdens have been a persistent challenge in healthcare, consuming valuable time and resources that could otherwise be dedicated to patient care. Agentic AI automates time-consuming tasks such as appointment scheduling, insurance claims processing, and capturing clinical notes during patient visits.

Sifting through vast amounts of medical data to make informed care decisions has also been another long-standing issue in healthcare. Agentic AI simplifies this process by distilling critical insights from electronic health records (EHRs), imaging results, and lab reports into actionable insights. With these insights, physicians can diagnose conditions more accurately and efficiently for improved patient outcomes.

Marketing

AI has redefined how marketers approach workflows, personalization, and customer engagement. This is particularly evident in generative marketing — the application of generative AI to marketing workflows. While generative AI is effective at generating creative assets, agentic AI compliments it by analyzing data and driving decision-making.

By combining the adaptability of LLMs with autonomous decision-making capabilities, agentic AI simplifies marketers' workflows. It lets marketers deliver highly personalized content and recommendations at scale. Unlike static personalization methods, agentic AI continuously learns from customer behavior — like browsing patterns and engagement history — to refine its recommendations over time.

Managing marketing campaigns across multiple platforms can be complex and time-consuming. Agentic AI simplifies this by monitoring campaign performance. It can make real-time adjustments to variables like ad placements, audience targeting, and bidding strategies. That means performance marketers can focus on strategic initiatives while achieving better ROI through continuous optimization.

Agentic AI excels at analyzing historical data alongside real-time inputs to forecast future trends and customer behaviors, enabling more actionable insights. These insights inform marketers about product positioning, content strategies, and audience segmentation. Marketing strategists and business intelligence teams can use these insights for long-term planning and competitive positioning. 

How can agentic AI interact with the data cloud?

Agentic AI changes how businesses leverage cloud-based data warehouses by unlocking   new opportunities, especially when integrated with a single source of truth (SSOT) architecture. An SSOT is a central repository that consolidates all of a company’s data.

A few use cases of an agentic AI system on top of a SSOT in a cloud data warehouse include:

1. Proactive decision support - Marketers can use agentic AI to predict market trends or customer behavior by analyzing historical data alongside external factors in the cloud. For example, it can identify seasonal trends or shifts in customer preference, helping businesses stay ahead of competitors.
2. Operational automation - Inventory levels can be autonomously adjusted in supply chains based on demand forecasts derived from a unified data source.
3. Customer personalization - By analyzing customer touchpoint data stored in the SSOT, agentic AI creates personalized experiences such as dynamic product recommendations or adaptive pricing strategies, driving higher engagement and revenue growth.
4. Business nuance adaption: With access to an SSOT, agentic AI can account for specific business rules or market conditions. For instance, it can prioritize high-value customers for exclusive offers or adjust campaign strategies based on regional buying behaviors.