What is Agentic AI?
Agentic AI is an autonomous AI system that can act independently to achieve pre-determined goals. Traditional software follows pre-defined rules, and traditional artificial intelligence also requires prompting and step-by-step guidance. However, agentic AI is proactive and can perform complex tasks without constant human oversight. "Agentic" indicates agency — the ability of these systems to act independently, but in a goal-driven manner.
AI agents can communicate with each other and other software systems to automate existing business processes. But beyond static automation, they make independent contextual decisions. They learn from their environment and adapt to changing conditions, enabling them to perform sophisticated workflows with accuracy.
For example, an agentic AI system can optimize employee shift schedules. If an employee is off sick, the agent can communicate with other employees and readjust the schedule while still meeting project resource and time requirements.
What are the characteristics of agentic AI systems?
Here are the key features of an agentic AI system.
Proactive
Agentic AI acts proactively rather than waiting for direct input. Traditional systems are reactive, responding only when triggered and following predefined workflows. In contrast, agentic systems anticipate needs, identify emerging patterns, and take initiative to address potential issues before they escalate. Their proactive behavior is driven by environmental awareness and their ability to evaluate outcomes against long-term goals.
For instance, in a supply chain setting, a traditional logistics platform updates delivery statuses when a user checks in or through periodic notifications. An agentic AI system, however, can monitor inventory levels, track weather conditions, and anticipate shipping delays. It can proactively raise alerts and even reroute shipments to reduce downtime.
Adaptable
A key feature of agentic AI is its ability to adapt to changing environments and specific domains. Traditional SaaS solutions are built to scale across industries and handle repetitive tasks, but they often lack the depth to understand unique domain-specific situations. Agentic systems fill this gap by using context awareness and domain knowledge, enabling AI agents to respond intelligently. They adjust their actions based on real-time input and can handle complex scenarios that standard solutions cannot.
For example, while a generic customer service platform might respond with predefined answers, an agentic AI system supporting a healthcare provider understands medical terminology and complies with healthcare regulations. It can adapt to evolving patient concerns and delivers more accurate, context-sensitive support.
Collaborative
Agentic AI is designed to collaborate with humans and with other agentic AI systems. AI agents work as part of a broader team. They can understand shared goals, interpret human intent, and coordinate actions accordingly. They work well in settings that require human oversight or decision-making by considering inputs from multiple sources.
For example, a treatment planning agent can coordinate with several different medical teams to prepare an integrated treatment and follow-up plan for a cancer patient.
Specialized
Agentic AI typically builds upon multiple hyperspecialized agents, with each focused on a narrow area of expertise. These AI-powered agents coordinate with each other, sharing insights and handing off tasks as needed. This approach enables significantly deeper domain-specific performance.
For instance, in financial services, one agent might specialize in regulatory compliance, another in fraud detection, and another in portfolio optimization. Working together, they can monitor transactions in real time, flagging anomalies and recommending investment adjustments, all while maintaining regulatory compliance.
What are the use cases of agentic AI?
Agentic AI has unlimited applications and can be fully customized to any requirement. We give some examples of early adoption.
Supporting research and development
Research and development in any field requires a great deal of manual processes, such as testing hypotheses, gathering research information, collecting data, synthesizing insights across data sources, and more. Agentic AI can reduce the need for human intervention with these manual processes. It streamlines research and better coordinates teams that are working on research and development challenges.
Agentic AI also facilitates multi-agent orchestration, where supervisors use multiple specialist models to construct complex research and development pipelines. For example, agentic AI could draw from recent research published on credible platforms, synthesize the results, plan further tests, and present researchers with the final product they need to investigate. This approach saves a significant amount of time and cost involved in research.
Code transformation
Agentc AI can use specialized AI-powered agents to remove the complexity of modernization and migration tasks. For example, agentic AI models for .NET can modernize Windows-based .NET applications to Linux significantly faster using machine learning, graph neural networks, Large language models (LLMs), and automated reasoning.
Equally, agentic AI can decompose monolithic z/OS COBOL applications into individual components, reducing the timeframe of this process from months to minutes. Agentic AI offers unmatched speed, scale, and performance in automating application migration and modernization.
Incident response automation
Whenever an incident occurs, whether due to a vulnerability or a manual error, agentic AI can expedite the incident response process, saving your business time and improving time-to-recovery. Agentic AI can automate the entire incident response pathway, rolling back issues, creating incident reports, and notifying any team members who need to stay informed.
Agentic AI enhances incident response speed while also providing a more specific and in-depth post-incident analysis to prevent the same errors from recurring in the future.
Customer service automation
In many customer service scenarios, the information that a customer needs is already online in a tutorial or help article. Agentic AI processes customer service inquiries and rapidly searches through available company documents to find a suitable answer that helps them out. If this alone isn’t enough to solve a query, agentic AI can then communicate with the user to gather more information about their case and direct them toward a solution. They are designed with modular components, such as reasoning engines, memory, cognitive skills, and tools, that enable them to remedy the vast majority of problems.
AI-powered agents can operate independently, learn from their environment, adapt to changing conditions, and develop more effective strategies to assist customers. If, after several attempts, it cannot solve a customer’s issue, it then contacts a human support agent and assigns them to the case. Utilizing this form of AI in customer service scenarios alleviates the burden on human teams and enables the vast majority of customer-oriented services to operate 24/7.
What are the benefits of agentic AI?
There are several business benefits to using agentic AI.
Increased efficiency
Agentic artificial intelligence enables businesses to simplify the complexity of various challenging or specialized tasks through automation. Instead of relying on human-driven manual practices, using agentic AI can automate tedious processes, freeing up time for your employees. Your employees can use the extra time that agentic AI saves them on more demanding tasks, such as problem-solving, strategic planning, and other drivers of growth.
Increased user trust
Agentic AI can offer a higher degree of personalization when interacting with customers. By utilizing existing customer data, agentic AI can quickly produce tailored messaging, engage with the customer in their preferred tone, and offer practical product recommendations. Over time, agentic AI improves customer relationships and builds trust between customers and your business.
Businesses can also utilize agentic artificial intelligence to analyze customer feedback, identify the most frequently occurring information, and provide it to product engineers. It can also directly respond to users who leave feedback, creating positive feedback loops where customers feel that their feedback is taken seriously by your company.
Continuous improvement
Agentic AI can continuously learn and improve, adapting to any tasks assigned to it. It interacts, learns from feedback, and optimizes its decision-making based on this recursive loop. For businesses, this means that it continues to deliver its benefits at higher and higher levels over time.
Human augmentation
Agentic AI can serve as a fantastic collaboration tool for human agents, enhancing their productivity and reducing the number of laborious manual tasks they must complete. By working alongside agentic AI models, human agents can overcome complex challenges, automate difficult decision-making pathways, and drive their efficiency.
What are the types of agentic AI systems?
Agentic AI can be single or multi-agent setups. In a single-agentic AI system, one AI agent handles all tasks sequentially. These are preferable when businesses need a faster solution that can work on a well-defined problem or process.
Multi-agentic AI, on the other hand, involves multiple AI agents collaborating to break down complex workflows into smaller segments. This approach is more scalable than single systems and is much more flexible for solving complex scenarios. The vast majority of agentic AI agents refer to this latter, more diverse form of AI deployment.
Here are a few different structures of multi-agent systems.
Horizontal multi-agent
Horizontal multi-agent AI is a system of working where every AI agent has the same level of technical proficiency and complexity. Each agent specializes in a narrow skill, bringing their findings together to solve a complex problem. This structure utilizes lateral collaboration and communication among the specialized AI agents.
Vertical multi-agent
In a vertical multi-agent system, there is a hierarchical structure in which lower-level AI agents have ‘easier’ tasks compared to the higher ones. The highest levels of this structure handle tasks that require more processing power and LLMs, such as critical thinking, reasoning, and decision-making. Lower-level AI agents in this structure perform tasks such as collecting data, formatting it, or processing it to pass it to higher levels.
How does agentic AI work?
Agentic AI agents operate by using a structured pathway that moves through four stages — perceive, reason, act, and learn. Each stage in this process integrates several advanced AI technologies and methods.
Perceive
At the perception stage, AI agents collect real-time data from a range of diverse sources, ingesting structured, semi-structured, and unstructured data. Agents directly interact with RESTful APIs, gRPC services, and GraphQL endpoints to ingest data as needed from cloud platforms, enterprise systems, and SaaS applications.
In certain legacy systems or those that require interaction with document-heavy environments, optimal character recognition technology (OCR) and natural language processing can help sift through scanned documents for relevant information. At the perceive stage, agents also process data to determine what is useful based on the task context in which they are working.
Reason
The reasoning stage is powered by LLMs that help to interpret the context of the goals a model has, develop an action plan to follow, and adapt in real-time using new information received through the perceive stage. LLMs employ models that utilize semantic reasoning, error handling, and adjust to any ambiguous user inputs.
Beyond just processing ideas and developing strategy in this stage, some LLMs use predictive machine learning models to manage complex problems. For example, a predictive ML model can forecast surges in demand, enabling better preparation for future use cases.
At this stage, LLMs use long-term memory systems to ensure that situational and context-dependent tasks remain consistent throughout the entire process.
Act
At the act stage, agentic AI takes action to achieve what was set out by the reasoning stage effectively. As agentic AI can access administrator-installed plugins on each of these external software systems, it can directly interact with and run tasks on these third-party applications.
The act stage orchestrates several subtasks that the agentic models will then tackle sequentially, with specific actions ranging from compiling code to interacting with software and documents, running simulations, migrating applications, and performing functions within a third-party application. For some agentic AI models, actions are gated by human-in-the-loop systems, where developers must verify what the model is doing and approve its actions.
All actions taken by a model are closely monitored and logged, allowing businesses to align with governance and safeguard their use of this technology.
Learn
The learning stage of agentic AI is what enables these models to continually improve their functionality and effectiveness. The agent utilizes reinforcement learning techniques, such as proximal policy optimization (PPO) and Q-learning, to refine actions based on the success of a specific task within the broader system.
AI agents learn from autonomous agents, LLMs, or through human feedback, all of which can fine-tune the system to improve its functioning. There are several metrics that a model can use to track its performance, including latency, confidence, and success rate. Multi-agent AI typically distributes learning across different agents, sharing information in communal memory layers to enhance the entire system's performance.
Over time, this style of reinforcement learning can utilize successful iterations to improve its overall functioning and enhance efficiency continually.
What are the challenges with agentic AI systems?
Several challenges are associated with agentic AI and building effective models.
System design
The process of building a multi-agent architecture that effectively coordinates with other models, has specific knowledge of how to tackle certain tasks, and can perform high-level reasoning and strategic planning is a challenging task. Agentic AI is a cutting-edge area of technology that relies on numerous challenging AI strategies. Due to the complexity of designing an effective system, many companies will struggle to access an effective version of agentic AI.
Testing and debugging
Agentic AI works independently and with minimal human intervention. This benefit also makes testing, debugging, and determining where an AI model has gone wrong a challenge. Developers must build traceability and reproducibility into the AI model, with special attention paid to tracing any errors and determining their causes.
Trust and transparancy
Even in advanced AI systems, AI hallucinations can impact workflows, leading to significant errors and problems for the business operating the model. If models generate false information and then relay it to the rest of the AI agents, incorrect data can rapidly spread, escalating errors in the final output. Especially in industries such as finance and healthcare that have severe real-world implications, businesses must have a great deal of trust in their product before using it extensively.
How can AWS support your agentic AI requirements?
Amazon Bedrock is a fully managed service that offers a choice of industry-leading foundation models (FMs) along with a broad set of capabilities needed to build generative AI applications. Amazon Bedrock Agents use the reasoning of FMs, APIs, and data to break down user requests, gather relevant information, and efficiently perform tasks. Building an agent is straightforward and fast, with setup in just a few steps. Amazon Bedrock supports:
- Memory retention for seamless task continuity
- Multi-agent collaboration to build multiple specialized agents under the coordination of a supervisor agent
- Amazon Bedrock Guardrails for built-in security and reliability.
AWS has introduced an open-source toolkit with a growing catalog of starter agents purpose-built for healthcare and life sciences use cases.
AWS Transform is the first agentic AI service for transforming .NET, mainframe, and VMware workloads. Built on 19 years of migration experience, it deploys specialized AI agents to automate complex tasks like assessments, code analysis, refactoring, decomposition, dependency mapping, validation, and transformation planning. It helps organizations to simultaneously modernize hundreds of applications while maintaining quality and control.
Amazon Q Business is a generative AI-powered assistant designed to help you find information, gain insights, and take action at work. It puts the power of agentic AI creation in the hands of every employee. Anyone can use it to create lightweight agentic AI apps that interact with common enterprise software and automate repetitive tasks.
Get started with agentic AI on AWS by creating a free account today.