What is agentic AI? A beginner's guide
Agentic AI is an exciting advancement in AI technology.
While foundation models like ChatGPT can generate content and answers when prompted, agentic AI can act on its own to achieve the goals set for it.
This groundbreaking technology is poised to redefine the possibilities of AI, unleashing a paradigm shift that promises to reshape the business landscape.
In this guide to agentic AI, we discuss:
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What is agentic AI?
Agentic AI is an artificial intelligence system that can autonomously make decisions, plan actions, and even learn from interactions in pursuit of the goals set for it.
Unlike previous AI models that simply respond to user inputs or carry out predefined tasks, agentic AI systems have a degree of autonomy. This enables agentic AI to handle complex tasks on its own by reasoning, using tools, and connecting to digital systems in real time to solve complex, multi-step problems.
How is agentic AI different?
Agentic AI is the third wave of AI, but it’s more than just the latest iteration of the current AI models. It represents a fundamental shift in how AI interacts with digital and physical environments, and uses available information to achieve its objective.
Agentic AI goes beyond previous AI models with these new capabilities:
Chaining
Agentic AI can take a series of actions in response to a single request. By breaking down complex tasks into a series of smaller, manageable steps, these systems can fulfill intricate requests that are too complex for traditional AI.
Essentially, this “chain of logic” is the thought process that AI agents use to make decisions and execute plans to reach the desired outcome.
For example, if tasked with planning a trip to Paris, an AI agent would analyze the task and deconstruct the goal into a sequence of subtasks:
Identify travel dates and budget
Research potential flights and compare prices
Book the most suitable flight
Find and reserve accommodation based on budget
Tool calling
Agentic AI can integrate data and use tools in its environment to achieve its goals.
Unlike previous AI that was limited by its monolithic models—which can only operate on the static data it was trained on (ChatGPT for example)—agentic AI is a compound system. Rather than relying on a single self-contained model for all its information, compound architecture enables the agentic AI to access and combine multiple modular components to execute a task.
As a result, agentic AI systems base their actions on the information they perceive. And if they lack the information in their knowledge base required to execute all the subtasks within a complex goal—they can connect in real time to data sources, software systems, or even other AI agents to access the necessary information.
For example, to troubleshoot a complex technical issue for a customer, an agentic AI system could:
Query the company's database for service history and device logs
Request real-time diagnostics on external devices using an API call
Analyze the collected data to identify potential causes and suggest solutions
Memory
Agentic AI is able to store and retrieve data from past interactions in its memory database. Serving as a near-term repository for information and experiences, memory enables the agentic system to:
Provide contextual and personal experiences to users based on data in memory
Learn from interactions and adapt its strategies by moving data in memory to its core knowledge base
For example, an AI agent on an ecommerce site would track customer behavior, preferences, and outcomes in search of trends, then stores this learned information in its memory. It could then provide support and personalized recommendations based on both the current context of conversation and past interactions to deliver targeted real-time offers, like cart abandonment coupons.
Finally, and most importantly, the agentic AI will analyze the past actions and outcomes in memory, along with conversation and behavior data, then add this data to its knowledge base, allowing it to improve and adapt over time.
How does agentic AI work?
At the core of agentic AI systems are large language models (LLMs) like ChatGPT, which allow it to receive instructions and generate responses to users in natural language. Using machine learning, the agentic system can analyze data, identify patterns, and make predictions.
By combining language and machine learning with goal-oriented behavior—agentic AI is able to tackle multi-step challenges in novel ways within the boundaries set for it.
Agentic AI uses a four-step process to solve problems:
Perception: AI agents gather data from various sources like customer interactions, databases, IoT devices, and social media. They can identify relevant features, recognize objects, and process data in real-time to perceive their environment.
Reasoning: The LLM orchestrates the autonomous chain-of-logic. This includes planning action, determining solutions, coordinating tasks, and even tapping other models for specific functions like content creation. Using techniques like retrieval-augmented-generation (RAG), the AI can access proprietary data sources to provide accurate, relevant outputs.
Action: Based on the plans it has formulated, the agentic AI executes the task, using APIs to leverage tools or external data. By observing its own actions, the system can self-correct to better achieve the desired outcome. Boundaries can be set to ensure tasks are completed correctly. For instance, an AI agent for customer service may be able to handle tickets up to a certain priority, above which the AI agent will hand off the ticket to a human agent.
Learning: AI agents learn from each interaction, updating their knowledge base to improve their accuracy and effectiveness. By feeding interaction and solution data back into the model from stored memory, AI agents can seamlessly adapt to customer expectations and enhance decision-making to improve their performance.
Goals and environments defined by humans
Agentic AI systems are groundbreaking because they're autonomous in their decision-making processes. However, they still require humans to define their goals and environment for them.
The autonomous goal-oriented behavior of agentic AI is influenced by three main sources:
The developers that design and train the agentic AI system.
The developers that deploy the agentic AI system and provides a business user with access to it.
The business user that provides the AI agent with specific goals to accomplish and establishes the available tools to use.
The environment of agentic AI is made up of all the systems, data, context, and spaces it has been programmed to perceive and interact with in pursuit of a defined objective.
Once integrated with the various components of its environment, the agentic AI system can use the available tools and knowledge sources at its disposal to autonomously fulfill its objective.
For example, the AI agent for a self-driving car would process data from multiple sensors to navigate around obstacles on the road. Or, an AI agent for customer service could have a purely digital environment that extends to a company’s CRM, live chat software, social media, and web searches.
Benefits of agentic AI
Agentic AI is set to significantly improve both business operations and customer experience. In fact, Gartner predicts that agentic AI will make 15% of all organizational decisions autonomously by 2028.
These are the areas where agentic AI offers the greatest benefits:
Greater autonomy: Agentic AI can actively pursue objectives and optimize strategies with minimal human intervention, making it suitable for tasks that require continuous monitoring or quick decision-making.
Enhanced problem-solving: By combining goal-oriented behavior with machine learning capabilities, agentic AI can tackle multi-step challenges in novel ways to reach its goal.
Adaptability: These systems can adjust their strategies based on new information or changing circumstances, allowing them to respond dynamically to users and improve over time through feedback loops.
Scalability: Agentic AI systems are scalable because the modular architecture allows for individual agents to be added or removed without disrupting the larger system. Once trained, these systems can be deployed across existing applications and channels.
Personalization: By recalling past interactions and preferences, agentic AI can provide relevant responses, tailored recommendations, and targeted offers in real time that align with the current conversational context as well as business objectives.
Workflow optimization: Agentic AI is set to improve workflows and enhance business processes by optimizing resource allocation, facilitating collaboration, identifying automation opportunities, and even managing teams of AI agents that specialize in different tasks.
Open communication: Capable of processing language, these systems are easier for humans to interact with and direct. This allows non-technical teams to assign goals, confirm expectations, and evaluate reasoning processes to improve performance.
Improved customer experience: Agentic AI can anticipate customer needs and tailor interactions based on current context and past interactions, delivering personalized experiences and immediate solutions that boost customer engagement and satisfaction.
Applications for agentic AI
The potential use cases of agentic AI are as diverse as they are exciting. While it’s still early days, here are some examples of how agentic AI is poised to make a big impact across industries:
Business operations
Agentic AI has the potential to reshape how businesses handle day-to-day operations. By processing vast amounts of data and making real-time decisions, these AI agents could autonomously manage supply chains, optimize inventory levels, coordinate communications, and even forecast demand. And by minimizing human input on routine tasks like data entry and order processing, they could greatly improve operational efficiency and reduce costs.
Customer service
By automating routine interactions and providing seamless, personalized interactions at scale, agentic AI promises to streamline customer service and reduce costs. Available 24/7, these AI agents retain context from past interactions to provide immediate, relevant solutions whenever customers want.
By combining decision-making with external data, they could troubleshoot technical issues without assistance, expanding the capabilities of self-service support. And since 76% of people prefer using self-serve resources before contacting a service representative, these systems could both reduce response times and improve customer satisfaction.
Finance
Agentic AI is set to transform the world of trading and investment. For example, AI agents could analyze market trends, make trading decisions in milliseconds, and adjust trading strategies in real-time based on customer’s financial data and news events.
In financial services, agentic AI could automate repetitive tasks like data entry, transaction processing, and compliance checks to increase efficiency and productivity of human employees.
Cybersecurity
In the ever-changing world of digital security operations, agentic AI could serve as the steadfast protector of network security. These AI agents could continuously monitor network traffic, detect anomalies, and respond to potential threats in real time without constant human oversight. This proactive and adaptive approach would strengthen a company’s defenses, ensuring a resilient cybersecurity posture that’s both scalable and cost-effective.
Software development
Beyond just generating code, agentic AI could one day manage the entire development lifecycle. AI agents could autonomously design system architecture, write code, detect bugs and identify security issues, and even manage quality assurance processes. By dramatically reducing coding time, this could accelerate software development and potentially overhaul how digital products are built and maintained.
Marketing
Agentic AI could create better, more personalized marketing campaigns and content. AI agents can use their generative capabilities to create a campaign brief, identify the audience, then create relevant content for that customer segment—saving employees three hours per piece of content.
Beyond streamlining content creation and dynamically tailoring content, Agentic AI could also optimize targeting based on current context and interaction history to improve conversion. All while integrating insights, proactively suggesting improvements, and analyzing performance against key performance indicators to improve ROI.
Healthcare
Agentic AI goes beyond the automated scheduling and triaging of current AI chatbots for healthcare—it can actively improve patient outcomes by tailoring care and streamlining operations with real-time patient data from various sources.
For example, a hospital could deploy an AI agent to assist diabetic patients. The agent would monitor patient glucose levels through wearable devices in real-time, recommend diet and lifestyle adjustments, and trigger in-app notifications to caregivers and patients if trends indicate potential issues. This in addition to providing 24/7 support and autonomous scheduling, helping to free up hospital employees and increase operational efficiency.
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Challenges and risks in agentic AI systems
For all its exciting potential, agentic AI isn’t without its risks and challenges. Here are the key considerations that enterprises should to keep in mind as they consider adopting agentic AI:
Integration complexity: Agentic AI systems, unlike generative AI, cannot operate in a technological silo. They must be connected to many different systems to take action and provide accurate outputs. While companies can use generative AI chatbots with minimal impact on their technology, integrating agentic AI into existing infrastructure can be costly and time-consuming.
Accuracy: The quality of an AI agent's decision-making relies on the quality of data it receives, which may be inconsistent in real-world scenarios, or fall out-of-date if there aren’t regular updates to the knowledge base of the agentic AI system.
Explainability: Agentic AI can execute a chain of actions that are complex and often invisible to human operators, making it difficult to understand or predict at times. This lack of explainability leads to questions about trust in outputs, as well as security and compliance concerns.
Risk management: Continuous monitoring and guardrails are needed to identify and mitigate the potential risks associated with agentic systems, including data privacy and security, compliance, or system failures.
Maintenance and oversight: Continuously monitoring and managing the behavior of autonomous agents in complex environments requires an ongoing effort and dedicated resources to ensure they are performing as intended.
Best practices for deploying agentic AI
If you’re thinking about adopting an agentic AI system, here are some best practices to keep in mind:
1. Define your objectives: What do you want to achieve with agentic AI? For example, it can be used to streamline operations, provide customer service, or refine code. Having a clear goal will simplify implementation and help you measure success.
2. Prepare your data: Ensure you have robust data collection and management systems in place. To provide accurate and relevant outputs, agentic AI requires access to vast amounts of high-quality business data, such as customer information, transaction histories, social media, IoT devices, and more.
3. Establish governance: Create a comprehensive governance framework for agentic AI systems that define the roles and responsibilities of all stakeholders involved in development and deployment. Lay out ethical guidelines, compliance measures, and and implement mechanisms for continuous monitoring and auditing.
4. Plan for human oversight: Establish human oversight and control mechanisms with human-in-the-loop processes to reduce risks and maintain compliance. Consider when human intervention or guardrails will be needed to validate outputs for accuracy and relevance around complex problems or sensitive interactions.
5. Ensure security: Make sure you have robust security measures in place, such as encryption, access controls, and regular vulnerability audits to protect sensitive data and maintain system integrity.
6. Test and validate: Ensure the reliability and safety of agentic AI systems with rigorous testing and validation before deployment. Carry out comprehensive testing that simulates real-world scenarios to identify and address biases, performance issues, or flaws in decision-making.
7. Monitor and optimize: AI agentic should be continuously monitored and updated to ensure its security and performance. Tracking agent action logs, feedback loops, performance metrics, as well as gathering user feedback can inform improvements and learning that maximizes the value and reliability of agentic AI systems in the long run.
Agentic AI: The next generation of enterprise automation
Imagine having a workforce of AI agents that specialize in different roles, from customer service to lead generation to content creation. By integrating an agentic AI system across platforms, enterprises could soon reshape entire workflows and automate traditionally-siloed operations while staying agile and efficient. For business leaders, it's a realistic vision of the future.
Faced with a landscape defined by fierce competition, rising costs, and relentless innovation—many enterprises are already adopting these solutions.
However, the responsible adoption of agentic AI requires striking a balance between automation and oversight. AI’s potential to streamline operations and reduce costs can only be realized through regular monitoring and careful correction as part of a strategic approach.