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What is Agentic AI?
Agentic AI refers to advanced artificial intelligence systems that have the ability to make decisions on their own in complex environments. They go beyond rule-based or generative models and demonstrate a high degree of autonomy and adaptability.
Agentic AI can set objectives, devise strategies, and execute multistep tasks with minimal human supervision.
How Agentic AI Works
- Understand and interpret nuanced instructions in natural language.
- Reason by setting appropriate goals, creating strategies, and prioritizing actions.
- Generate and execute on workflows.
- Adapt actions based on changing circumstances and feedback.
- Interact with multiple systems and data sources to complete multistep tasks.
- Learn from experiences and improve performance over time.
Below are some relevant concepts that are often components of agentic AI:
Large Language Models (LLMs)
LLMs (e.g., GPT, Claude) are pretrained on a vast amount of factual knowledge, usually from publicly available data sources. They enable agentic AI to understand, generate, and respond to natural language requests.
Reinforcement Learning (RL)
Agentic AI uses RL to learn optimal actions by balancing exploration (using new strategies) and exploitations (using known strategies), receiving rewards or penalties based on its actions.
Using trial and error, an agent continuously adapts its actions to accumulate more rewards, effectively optimizing performance over time, using algorithms including Q-Learning and Deep Q-Networks.
Deep Neural Networks (DNNs)
DNNs process high-dimensional inputs and model complex relationships in large datasets, allowing agentic AI systems to make decisions without the need to program each and every scenario.
Multi-agent Systems (MAS)
MAS consists of multiple autonomous agents that interact and collaborate to perform complex tasks. While each operates independently, they can communicate, share and access a common knowledge base, and coordinate actions.
This framework enables more robust and flexible problem-solving with better resource management.
Here is a conventional embodiment of an AI-enabled agent:
- User request: “Our sales team needs to improve customer engagement and conversion rates. Analyze our current customer data, identify key patterns, and create personalized marketing campaigns.”
- Agents: In MAS, agents can each be responsible for individual tasks (e.g., Agent 1 for segmenting customers, Agent 2 for analyzing purchase behavior).
- Memory: This serves as the foundation for knowledge retention and decision-making, storing information relevant to immediate tasks for quick responses (short-term memory) and past experiences and learned patterns to refine strategies and performance over time (long-term memory).
- Tools: Agents integrate with external tools including APIs and services to access external data sources, software libraries, user interfaces, etc.
- Reflection: Agentic AI can self-evaluate and learn from past actions to refine its outputs. It uses stored logs and memory models to evaluate the efficacy of its actions, measure against goals, and update its knowledge base or policies accordingly.
- LLMs: LLMs play a crucial role, providing foundational knowledge that allows agentic AI to understand instructions, make informed decisions, communicate with users using natural language, and leverage external tools and APIs. With support for chain-of-thought (CoT) reasoning, LLMs help agentic AI handle complex multistep tasks. They also maintain context to provide relevant responses based on past interactions.
Common tools for building agentic workflows include AutoGen, CrewAI, and LangGraph from LangChain. More recently, OpenAI launched an experimental multi-agent framework called Swarm. Agents can also use retrieval-augmented generation (RAG) to access external, domain-specific proprietary data, in order to make more informed decisions. RAG allows agents to retrieve relevant information such as company-specific policies, customer profiles, and real-time order updates to supplement its knowledge base when reasoning about a task or problem.
| Agentic AI | Multi-agent Systems | |
|---|---|---|
| Number of agents | Involves a single autonomous agent. | Comprise multiple agents interacting within the same environment. |
| Interaction | Focuses on how one agent perceives and acts within its environment independently. | Concerned with interactions among agents, including communication and coordination. |
| Goals | Centers on achieving the goals of a single agent. | May involve shared goals among agents or conflicting objectives requiring negotiation. |
Agentic AI vs. Generative AI
Generative AI is focused on creating content (e.g., text, images, videos) while agentic AI is designed for autonomous decision-making and taking action, interacting with its environment and adapting over time.
| Agentic AI | Generative AI | |
|---|---|---|
| Core Functionality | Emphasis on autonomy, capable of acting independently and intelligently to accomplish tasks. | Emphasis on creativity, using LLMs to generate new information based on the data it has been trained on. |
| Autonomy | Operates with minimal human supervision. | Requires human guidance to determine the context and goals of its output. |
| Learning & Adaptation | Continuously learns from experience and environmental feedback, using reinforcement learning algorithms. | Pre-trained on large datasets and sometimes fine-tuned to produce content. Does not adapt content generation based on real-time feedback. |
| Interaction with Environment | Has mechanisms for perception, decision-making, and action – can use sensory data and machine learning models (e.g., computer vision, NLP) to understand the environment and adapt to fluctuations in energy demand, for example, to optimize energy consumption. | Does not typically interact with its environment. Context is provided as part of training or coupled as part of a prompt. |
| Applications | Robotics, self-driving vehicles, autonomous trading, smart grid optimization | ChatGPT (chatbot), Stable Diffusion (image generator), GitHub Copilot (coding tool) |
Benefits of Agentic AI
Agentic AI is a powerful tool for helping organizations across a variety of industries enhance their operations from reducing human error by handling data-intensive processes to improving customer interactions. Key benefits include:
Increased efficiency and productivity
Agentic AI can handle complex, decision-intensive tasks, allowing human employees to focus on higher-value strategic and creative initiatives.
Cost reduction
With less reliance on human oversight as well as the ability to automate and improve end-to-end processes, businesses can realize lower labor costs, operational savings, and minimize the rate of costly accidents.
Scalability
Once trained, agentic AI systems can be deployed across many different applications, teams, and regions. Built with modular architecture, it can extend functionality as organizational needs grow and integrate with new data sources and tools. It also autonomously allocates resources to meet increasing data and demand.
Personalized customer experiences
Agentic AI can provide personalized and highly responsive, human-like interactions at scale, inferring customer intent and offering tailored support or recommendations 24/7 that also evolve based on real-time feedback.
Faster innovation
Agentic AI accelerates innovation by enabling more efficient ideation, rapid prototyping and testing. It can autonomously run simulations and quickly adapt in virtual environments to evaluate different designs without the time and cost constraints of real-world experiments. It can reduce the development cycle of new products or for fields like pharmaceuticals, simulating drug interactions faster than traditional lab testing.
Challenges of Agentic AI
Here are a few challenges to take into consideration for the development, implementation, and governance of agentic AI systems to ensure that they remain safe and aligned with technical and organizational interests.
Developing methods to make the AI’s decision-making process transparent and interpretable in order to increase user confidence and allow for oversight (e.g., auditing in a regulatory environment).
Implementing safeguards to prevent data leakage or unintended or harmful actions by the autonomous system.
Building and incorporating agentic AI (and multi-agent architectures) into established processes and legacy systems can be technically challenging and disruptive, requiring SMEs and know-how that may not be available.
To prevent garbage in-garbage out, agentic AI systems need high-quality, trustworthy, and contextualized data – often in real time to support mission-critical use cases. Enterprise data architectures with tightly coupled point-to-point connections and batch ETL processing would hinder the ability of agents to access the data needed to execute tasks.
Agentic AI Use Cases
The applications for agentic AI are diverse. Here are a few examples of real-world enterprise-scale use cases:
Healthcare & Insurance Claims Processing
Agentic AI can autonomously review claims, verify documentation, and resolve discrepancies, reducing approval times as well as manual workload while increasing customer satisfaction.
Airline Operations
Analyzing vast amounts of data (e.g., market trends, competitor pricing, customer behavior), agentic AI helps set dynamic pricing for flights in real time based on demand fluctuations. It can also monitor aircraft systems and components, leveraging IoT sensor data to detect potential issues, schedule maintenance crews, and update dashboards.
Supply Chain and Logistics
Monitoring data from sales, seasonal trends, and supply chain variables, agentic AI can autonomously place orders with suppliers when stock is low, for example, as well as integrate with billing, calendar, and other systems to make adjustments in real time. Agents assist with route optimization due to inclement weather, evaluating supplier performance, and identifying areas where efficiencies can be gained.
Financial Decision-Making
Agentic AI can manage investment portfolios based on macro trends and real-time prices, make rapid trading decisions, and adapt investment strategies based on economic data and news events to deliver higher returns for investors.
Building Agentic AI with Confluent
It starts with building a real-time, contextualized, and trustworthy knowledge base:
Continuously capture and share real-time events with agentic AI systems.
Integrate disparate data from any environment with 120+ pre-built and custom connectors.
Use Flink stream processing to enrich data with real-time context at query execution.
Use data lineage, quality controls, and traceability to ensure data for AI is secure and verifiable.
Additional Resources
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