Agentic AI What is Agentic AI?
The next level of AI advancement, agentic AI, uses LLMs, machine learning, and corporate automation to do complicated, multi-step operations without human interaction. It lets smart computers understand context, adapt to new knowledge, and work with humans to solve complex problems. By letting machines work freely in unstructured contexts, agentic AI is redefining automation.
Time to read: 5 minutes | Updated: November 4, 2025
Table of Contents
Agentic AI and AI
AI agents power systems and applications that improve efficiency, automation, and decision-making in our daily lives. These agents have varied complexity levels for different jobs and interactions.
- Reactive agents (Simple agents): These respond instantly without memory or learning. Simple chatbots and real-time thermostats are some of its examples.
- Model-based agents: These agents use an internal world model to evaluate previous interactions and anticipate future situations. Siri and Alexa contextualize commands using this method.
- Goal-based agents: These agents make decisions based on objectives rather than responses. For instance, navigation applications monitor traffic to find the optimal route.
- Utility-based agents: These consider efficiency, cost, and risk to choose the best action. Netflix and Amazon recommend entertainment and items using this strategy.
- Learning agents: These adapt to new data and improve over time. Fraud detection and tailored healthcare machine learning models use this agent.
- Autonomous agents: These agents make complicated data-driven judgments without human involvement. Examples of this agents include self-driving vehicles and RPA.
Advanced AI systems are built on these agent categories. When integrated, they generate complex tools like:
- Networks leverage integrated agent types to autonomously plan, configure, and optimize infrastructure in response to changing demands.
- Self-driving cars utilize model-based and utility-based agents to drive safely.
- Customer service AI uses learning and goal-based agents to personalize help.
- Dynamic AI frame generation for video game graphics utilizing predictive models.
- Agentic AI relies on AI agent integration to provide systems more autonomy, intelligence, and adaptability.
Agentic AI benefits
Agency AI improves productivity, decision-making, and automates complicated activities, revolutionizing enterprises. By incorporating autonomous intelligent agents, enterprises can boost productivity, safety, and customer experience.
- Productivity boost: Multi-step procedures are automated by agentic AI, saving time and effort. Businesses may simplify procedures, streamline operations, and free up people for strategic tasks.
- Faster decision-making: AI-driven insights enable teams examine massive volumes of data in real time, boosting reaction times in finance, supply chain management, and healthcare.
- Workforce augmentation: AI agents solve monotonous or administrative duties so workers may focus on creativity, problem-solving, and innovation.
- Continuous operation: AI systems can work 24/7 without tiredness, maintaining global team productivity.
- Safety improvements: Agentic AI reduces human mistake, detects dangers in real time, and enforces industry laws.
- Safe workplace: AI-powered monitoring systems can detect dangerous circumstances in real time, minimizing accidents in high-risk industries like manufacturing and construction.
- Cybersecurity: AI bots analyze network activity, detect abnormalities, and prevent data breaches.
- Autonomous systems: Self-driving cars and robotic automation minimize transportation and logistics accidents.
- Top consumer experience: By comprehending context, adjusting to user demands, and enabling seamless interactions, Agentic AI boosts consumer engagement and happiness.
- Personalization interactions: AI-driven recommendation systems customize information, goods, and services to boost user engagement.
- More efficient customer service: Automated AI agents respond instantly, lowering wait times and boosting resolution rates.
- Proactive assistance: AI anticipates client demands and provides answers before challenges develop in online shopping, banking, and healthcare.
Agentic AI makes commercial processes more effective, safe, and customer-focused by operating autonomously and adapting to dynamic situations.
How business use Agentic AI
Agentic AI automates complicated procedures, improves decision-making, and boosts efficiency, transforming industries. Here's how different sectors use it.
- In financial services, agency AI improves security, efficiency, and client experience.
- Fraud detection and risk management: AI analyzes transactions in real time to discover irregularities and prevent fraud.
- Automatic trading: AI-driven algorithms analyze market movements and execute precise trades.
- Personalised banking & support: AI-powered chatbots automate and advise 24/7.
- Manufacturers utilize AI to boost efficiency, save waste, and improve quality.
- Predictive maintenance: AI predicts equipment breakdown, reducing downtime and maintenance costs.
- Supply chain optimization: AI agents optimize material procurement and inventory management.
- Automation: AI-driven visual inspection finds faults, assuring manufacturing consistency.
- Healthcare AI is revolutionizing patient care, diagnosis, and research.
- AI-driven diagnostics: Examines medical photos and test reports to help clinicians spot early diseases.
- Treatment plans: AI uses patient history and genetic data to propose therapies.
- Administrative automation: AI simplifies scheduling, coding, and insurance claims.
- Automotive: Agency AI enables autonomous driving, smart manufacturing, and vehicle maintenance.
- Autonomous driving: Self-driving cars use AI for real-time navigation, danger identification, and decision-making.
- Smart vehicle maintenance: AI predicts component failures and arranges maintenance.
- AI-enhanced supply chains: Automakers use AI to optimize manufacturing, material procurement, and logistics.
- AI is changing software development, testing, and maintenance.
- Automation: AI writes, debugs, and optimizes code.
- AI-powered testing: AI finds software flaws before deployment.
- DevOps automation: simplifies software deployment, monitoring, and infrastructure scalability.
Law companies and departments employ AI for research, compliance, and case analysis.
- Contract review and analysis: AI checks legal papers for significant terms and dangers.
- Legal research and case law analysis: AI identifies relevant precedents and legal insights faster than humans.
- Regulatory compliance monitoring: AI watches policy changes and ensures organizations comply with changing requirements.
Businesses may automate difficult procedures, improve decision-making, and boost productivity by integrating AI agents across various areas. Agentic AI revolutionizes innovation and competitiveness.
How does agentic AI differ from traditional AI?
Most conventional AI systems are built to perform narrowly defined tasks, even if they can adapt within those boundaries. These systems range from basic rule-based engines to sophisticated models like Anthropic’s Claude, which excel at language understanding and generation. However, they are reactive in nature, mostly responding to prompts and situational cues, and require at least some human input and oversight.
In contrast, agentic AI is designed to operate with autonomy. These systems don’t just follow instructions. They pursue goals, adapt on the fly, and interact with their environment to make decisions in real time. This capacity for independent, purpose-driven behavior is what gives agentic AI its defining characteristic: agency. While traditional AI is tethered to human direction and task-specific limits, agentic AI incorporates reasoning, planning, and self-directed action that even the most advanced traditional models can’t replicate.
Its self-directed, outcome-driven behavior is what elevates agentic AI beyond traditional automation, offering levels of reasoning, planning, and autonomy that traditional AI can't match.
Risk of Agentic AI
Agentic AI has many benefits, but businesses must manage its risks. Below are key challenges, their potential effects, and mitigation strategies.
- Lack of transparency and accountability
- Risk: Agentic AI systems are typically "black boxes," making decision-making difficult to explain. If the AI makes a mistake or a judgment that leads to bad results, finding the specific reason of the error might be tough.
- Impact: When AI makes high-stakes commercial choices like financial transactions, employment, or legal problems, accountability may suffer.
- Mitigation: Businesses should prioritize explainable AI (XAI) solutions that provide insights into decision-making processes and implement human oversight for critical applications.
- Bias and discrimination
- Risk: AI algorithms learn from previous data, which may contain prejudices. AI can perpetuate or even magnify prejudice in sectors like recruiting, financing, and law enforcement if these biases are not discovered and remedied.
- Impact: Unchecked prejudice can result in unjust treatment of particular groups, legal liabilities, reputational harm, and loss of consumer confidence.
-Mitigation: Regular audits of training data and AI decisions should be conducted. Diverse and inclusive datasets, as well as bias-detection tools, can help reduce discriminatory outcomes.
Security vulnerabilities
- Risk: AI systems can be targeted by cyberattacks, including adversarial attacks, data poisoning, and model manipulation, which can lead to incorrect or malicious outputs.
- Impact: Security breaches can compromise sensitive business and customer data, leading to financial losses, regulatory penalties, and operational disruptions.
- Mitigation: Implement strong cybersecurity protocols, such as encryption, adversarial testing, and real-time monitoring to detect and mitigate threats before they cause harm.
- Operational dependence
- Risk: Businesses that incorporate AI too deeply into vital activities may struggle if the technology malfunctions, experiences a mistake, or becomes obsolete.
- Impact: Overreliance on AI can reduce human expertise and problem-solving skills, leaving organizations vulnerable if the AI system fails or produces incorrect results.
- Mitigation: To mitigate, use AI to complement but not replace human decision-making. Keep staff trained to intervene.
- Resource and cost allocation
- Risk: Developing, maintaining, and continuously improving AI systems requires significant investment in hardware, software, and skilled personnel.
- Impact: High costs can strain budgets, particularly for small businesses, and lead to inefficient resource allocation if AI projects do not generate expected returns.
- Mitigation: Before implementing AI, companies should do a cost-benefit analysis, start with scalable solutions, and consider AI-as-a-service.
- Unpredictable behavior
- Risk: AI models and new scenarios may lead to unexpected or unintended outcomes.
- Impact: Unpredictable behavior in AI-driven automation, such as self-driving vehicles or financial trading bots, can lead to costly blunders, accidents, or ethical dilemmas.
- Mitigation: Monitoring AI performance, updating models, and testing AI in controlled contexts before deployment are mitigation strategies.
Agentic AI implementation risk reduction
Follow these best practices to safely integrate Agentic AI into business operations:
- Human oversight: Ensure AI-driven choices are evaluated by humans, especially in sensitive sectors like finance, law, and healthcare.
- Regulatory compliance: Stay informed about AI-related laws and ensure compliance with data protection and ethical AI guidelines.
- Strong testing and monitoring: Perform frequent audits, stress testing, and real-time monitoring to find abnormalities and increase system dependability.
- AI ethics frameworks: Develop and implement AI ethical policies to eliminate prejudice, promote fairness, and increase transparency.
- Failure-safe mechanisms: Backup systems and manual override options ensure business continuity if AI fails.
By proactively addressing these risks, businesses can harness the full potential of Agentic AI while minimizing potential downsides.
Working with HPE on Agentic AI
- HPE GreenLake—AI as a service: HPE GreenLake's scalable, cloud-based AI infrastructure lets enterprises execute AI workloads efficiently without upfront expenses. Its hybrid cloud features optimize performance and flexibility by integrating on-premise and cloud settings. The pay-as-you-go strategy simplifies AI deployment by reducing financial and operational costs. Businesses can expedite AI implementation and reduce costs with HPE GreenLake.
- HPE Ezmeral—AI/data analytics platform: A complete AI and analytics platform, HPE Ezmeral lets enterprises handle huge information and gain real-time insights. AI model creation, deployment, and monitoring are simplified while data governance and security are maintained. Businesses can improve workflows and decision-making using its automation and machine learning. Ezmeral's versatility facilitates seamless AI operations with on-premise and cloud-based AI applications.
- High-performance AI computing HPE Cray Supercomputing: The HPE Cray supercomputer powers sophisticated AI tasks like deep learning and large-scale model training. It dramatically decreases AI training time, improving AI-driven decision-making accuracy and efficiency. Its tremendous processing capacity benefits banking, healthcare, and autonomous systems. HPE Cray accelerates AI-driven innovation by processing massive volumes of data fast.
- HPE AI-optimized storage: For quick, secure, and scalable AI data management, HPE Alletra and HPE Apollo storage systems are ideal. While retaining regulatory compliance and security, these systems allow fast AI training data access. Intelligent storage tiering manages AI model training and inference data to save expenses. Businesses can enhance AI processes and performance by incorporating AI-specific storage solutions.
- HPE Aruba AI-driven network optimization: HPE Aruba optimizes network speed, security, and connectivity for AI-driven apps with AI-powered automation. It streamlines distributed AI workload communication and improves network reliability and efficiency. AI-driven threat detection and response protect networks against cyberattacks. HPE Aruba provides fast, secure AI infrastructure networking.
- HPE AI ethics and security frameworks: HPE addresses bias, data protection, and compliance threats using AI security and ethical AI frameworks. These methods guarantee AI is transparent and follows responsible AI standards to avoid unforeseen consequences. AI-driven threat identification and continuous monitoring protect enterprises against threats. Businesses can trust and account for AI deployment with HPE's security safeguards.
Agentic AI in HPE Networking
Agentic AI is redefining enterprise networking by deploying autonomous agents that proactively plan architectures, streamline refresh cycles, intelligently manage configurations, and continuously operate infrastructure, delivering real-time insights, adaptive troubleshooting, and collaborative optimization.
Unlike traditional AI, Agentic systems don’t wait for instructions, they reason, plan, and act independently. In network planning, for example, agentic systems can interpret business intent like “optimize for low-latency edge workloads” and automatically generate topologies, capacity models, and routing policies to match.
During network refreshes, agents can monitor device health and lifecycle status, then autonomously recommend or initiate firmware upgrades and hardware replacements that are prioritized by risk and performance impact.
For network management, agentic agents can continuously assess configurations, detect anomalies, and orchestrate changes across distributed environments.
And in network operations, agentic agents can perform real-time root cause analysis, correlate alerts across domains, and trigger automated remediation workflows, all while transparently explaining their decisions through a traceable chain of thought.
HOLD THE THOUGHT
- This shift from reactive automation to autonomous orchestration enables networks to evolve from static infrastructure into dynamic, self-optimizing ecosystems.
- Think self-driving network. Today’s network operators make decisions based on the programmed or rules-based information that is served up or pulled by them. Automation could have been used in pulling the information, but a human needs to evaluate it against process, policy, judgement and then determine next steps.
- With agentic, the network is making the decision on it’s own, based on precedence and on xxx and so it fully autonomous, or self-driving. For troubleshooting, “agents” are kicking off processes in the background and talking to each other, so we’re bringing agentic to the network
- Agentic AI is an AI system that can make decisions and take actions on its own to achieve a goal without being told what to do at every step.
- Agentic AI involves
- Goal oriented planning
- Multi-step reasoning
- Autonomous decision making and performing actions
It does not need to be told what to do. It can think, plan, and act
- Agents are systems where LLMs dynamically direct their own processes and tool usage maintaining control over how they accomplish things
- Agents are autonomous systems that operate independently over extended periods using various tools to accomplish complex tasks
- Workflows are systems where LLMs and tools are orchestrated through predefined code paths
Agentic AI: This refers to a class of AI systems that can operate autonomously, making decisions and performing tasks without human intervention. They have the ability to perceive their environment, reason based on that information, set goals, make decisions, execute actions, and learn from feedback.
HPE GreenLake Intelligence: This is a specific framework developed by HPE that leverages agentic AI to transform and simplify hybrid cloud operations. It incorporates various AI agents for different functions like networking, storage, compute, cloud costs, sustainability, and workload optimization. HPE GreenLake Intelligence is a specific implementation of agentic AI within the HPE GreenLake hybrid cloud platform.
- A framework to enable AI agents to collaborate, learn, adapt, and act with context — across clouds, domains, tools, and tasks.
Central, will soon incorporate agentic mesh capabilities and become HPE’s first domain-specific service to feature GreenLake Intelligence. The agentic mesh will significantly advance us towards a fully self-driving network.
ESG says: AI can affect networking planning, network refresh, network management, network operations and everything in between, across every networking domain.