Agentic AI Tools: Key Capabilities and 7 Tools to Know in 2025

What Are Agentic AI Tools?

Agentic AI tools are systems that empower AI to autonomously perform complex tasks by coordinating multiple AI agents, leveraging large language models (LLMs) and complex reasoning. These tools go beyond individual AI agents, creating integrated systems that can handle intricate workflows, make decisions, and interact with users in more natural and intuitive ways.

Unlike traditional automation tools that follow strict, predefined instructions, agentic AI systems leverage complex algorithms, machine learning, and reasoning to navigate dynamic environments, interpret ambiguous scenarios, and pursue high-level objectives. By operating with autonomy, these tools can handle tasks requiring judgment, adaptability, and prioritization.

The rise of agentic AI reflects a broader shift toward AI components capable of functioning as proactive agents rather than passive assistants. These tools do not simply execute commands; instead, they can set goals, strategize, monitor progress, and adjust their actions as conditions change. 

This is part of a series of articles about SIEM tools

Key Capabilities of Agentic AI Tools 

Autonomous Decision-Making

Agentic AI tools can make decisions independently, minimizing the need for human oversight during routine and complex operations. Unlike systems that require explicit instructions for every action, these tools are programmed to evaluate changing inputs, assess multiple outcomes, and select the most appropriate response. This autonomy is achieved through technologies such as deep learning, reinforcement learning, and probabilistic reasoning.

Such decision-making capabilities are especially valuable in situations where rapid response is essential, such as detecting security threats or managing IT incidents. By continuously interpreting data, agentic AI tools can anticipate problems, prioritize actions, and allocate resources automatically. 

Proactive Goal Setting and Execution

Agentic AI can set goals and pursue them proactively, rather than only responding to immediate instructions. These tools can assess organizational objectives, identify bottlenecks, and develop actionable plans to achieve outcomes. By evaluating available resources and constraints, agentic AI systems prioritize tasks, schedule workflows, and coordinate with various software or human stakeholders to drive progress autonomously. 

Agentic AI tools can re-evaluate their goals as new information emerges, refining their strategies to adapt to changing priorities or environments. For example, in customer support, an agentic AI system can spot recurring issues, propose preventative measures, and implement solutions across an entire organization, demonstrating initiative well beyond passive ticket resolution.

Dynamic Reasoning and Planning

Dynamic reasoning allows these tools to construct and update plans in environments characterized by uncertainty or frequent change. Rather than following static sequences, agentic AI evaluates alternative scenarios, simulates outcomes, and adjusts its approach. This allows it to handle tasks such as multi-step process coordination, complex workflow management, or troubleshooting multi-faceted problems with a higher level of sophistication.

Effective planning requires not only logical deduction but also the capacity to manage trade-offs, recognize dependencies, and respond to unforeseen obstacles. Agentic AI can parse evolving input, weigh competing priorities, and shift its strategies as conditions demand. This capability is particularly useful in enterprise settings, such as IT automation or supply chain management.

Continuous Learning and Adaptation

Unlike static automation scripts, agentic AI tools are designed for continuous learning, using real-world feedback to improve their performance over time. Through machine learning techniques like supervised, unsupervised, or reinforcement learning, these tools analyze the results of their actions and update their decision-making models accordingly. This adaptability allows agentic AI to evolve in response to new data or shifting user expectations.

Such learning processes enable agentic AI to deliver more accurate, efficient, and context-aware solutions the longer they are deployed. In scenarios like fraud detection or workflow optimization, the ability to learn from emerging patterns helps the system stay relevant and effective. 

Transparency and Explainability

As agentic AI takes on more critical decisions, transparency and explainability become essential for trust and accountability. Leading agentic AI tools are built to provide clear rationales for their actions, outlining the factors considered and the reasoning behind choices. By exposing internal decision-making logic, these systems make it easier for users to understand, audit, and, if necessary, override automated processes.

Explainability also supports compliance and risk management, especially in regulated industries where decisions must be documented and justified. Transparent agentic AI helps organizations demonstrate due diligence, reduce liability, and maintain user trust. Vendors often embed tools such as dashboards, logs, and interactive reports to give visibility over automated operations.

Tips from the expert

Steve Moore is Vice President and Chief Security Strategist at Exabeam, helping drive solutions for threat detection and advising customers on security programs and breach response. He is the host of the “The New CISO Podcast,” a Forbes Tech Council member, and Co-founder of TEN18 at Exabeam.

In my experience, here are tips that can help you better design, deploy, and govern agentic AI tools for enterprise success:

Build a “meta-orchestration” layer: When managing multiple agentic AI tools, use a central orchestration layer that tracks goals, assigns tasks to the most capable agents, and prevents redundant or conflicting actions.

Predefine escalation pathways for anomalies: Even with adaptive decision-making, ensure that rare or high-risk scenarios are immediately escalated to human operators with full context and logs.

Inject adversarial testing into deployment cycles: Regularly simulate malicious inputs, corrupted data streams, or unexpected UI changes to ensure agents can detect and recover without unsafe outcomes.

Implement state checkpointing for long-running workflows: Allow agents to save and roll back to prior “safe states” if downstream tasks fail, reducing the need to restart entire processes from scratch.

Enforce operational “quiet periods”: Temporarily halt agent actions during system upgrades, policy changes, or major data migrations to prevent misalignment with shifting operational rules.

Notable Agentic AI Tools

1. Exabeam

Exabeam Nova is a combination of six AI agents that are purpose-built for security operations centers (SOCs). It combines multiple specialized agents that can analyze logs, detect anomalies, score risk, and recommend or initiate response actions. Unlike traditional SIEM approaches that rely on static rules, Exabeam Nova adapts to changing attacker behaviors and continuously benchmarks security program effectiveness.

Key features include:

• Agentic SOC automation: Automates tasks like alert triage, investigation enrichment, and case correlation, allowing analysts to focus on high-priority threats.
• Behavioral analytics integration: Establishes baselines of user and entity behavior to detect deviations that may signal insider threats or advanced attacks.
• Contextual recommendations: Acts as an advisor by suggesting investigative steps and mapping incidents to likely attacker tactics, techniques, and procedures.
• Transparent decisioning: Provides reasoning and traceability for AI-driven actions, enabling analysts to validate outcomes and maintain trust in automation.
• Outcome-based benchmarking: Provides security teams with the ability to measure program performance against peers in similar industries using MITRE ATT&CK coverage. This helps organizations understand where they are strong, identify capability gaps, and prioritize investments based on real-world outcomes.

Exabeam positions Exabeam Nova as a way to help security teams detect, investigate, and respond to threats faster while reducing analyst fatigue. By embedding agentic AI into the SOC workflow, Exabeam aims to create measurable improvements in both detection accuracy and operational efficiency.

2. SentinelOne AI SIEM

Operator is a browser-based agent developed by OpenAI that performs web-based tasks autonomously using a built-in browser. Unlike passive AI assistants, Operator can interact with websites like a human, clicking buttons, filling out forms, and navigating pages without explicit APIs. It uses the Computer-Using Agent (CUA), which combines GPT-4o’s vision capabilities with reinforcement learning, allowing it to observe webpages and take actions. 

Key features include:

• Web interaction via built-in browser: Operator can see and interact with webpages directly, including clicking, typing, and scrolling.
• Autonomous task execution: It can perform multi-step workflows like ordering groceries, booking reservations, or filling out forms.
• Reasoning and self-correction: Uses reinforcement learning to plan actions and adapt when tasks don’t go as expected.
• Collaborative control: Operator hands over control to the user when sensitive actions are required, such as entering passwords or payment info.
• Custom workflow configuration: Users can personalize behavior across all or specific sites, save prompt templates, and run multiple concurrent tasks.

Source: OpenAI 

3. Aisera

Aisera is an agentic AI platform to automate enterprise support across IT, HR, finance, and customer service. Unlike intent-based systems that require structured training data, Aisera uses intentless understanding and domain-specific language models to interpret user queries in natural language. It operates with contextual awareness, adapts to unexpected requests, and supports interactions across channels and input types. 

Key features include:

• Intentless understanding: Handles user queries without relying on predefined intents, using natural language understanding to respond accurately.
• Contextual disambiguation: Resolves ambiguous input through multi-turn conversations and contextual reasoning.
• Multi-fulfillment & multi-modality: Supports end-to-end task execution by retrieving data and triggering actions across systems, while handling inputs in text, voice, image, and document formats.
• Agent studio: Offers no-code, low-code, and pro-code tools to build and manage AI agents.
• Domain-specific language models: Tailored LLMs trained on specific industries ensure higher accuracy and relevance in specialized workflows.

Source: Aisera 

4. Moveworks

Moveworks is an enterprise-ready agentic AI assistant to automate tasks, resolve issues, and surface information across business systems. Built on a multi-LLM Reasoning Engine, Moveworks goes beyond static workflows, understanding natural language, planning next steps, and executing actions without human intervention. 

Key features include:

• Unified enterprise support: Connects to major systems and apps to find answers and complete tasks instantly.
• Agentic reasoning engine: Uses multiple large language models to interpret user intent, plan workflows, and take action.
• Omnichannel and multilingual: Delivers support in over 100 languages across chat, email, service portals, and browsers.
• Integrations: Deploys quickly with hundreds of prebuilt integrations.
• Cross-department automation: Enables IT, HR, Finance, Engineering, Sales, and Marketing teams to resolve issues, fulfill requests, and access information autonomously.

Source: Moveworks 

5. Adept Beam

Adept’s Beam platform is an agentic process automation solution that enables organizations to build, manage, and deploy AI agents at scale. Intended for enterprise-grade use, Beam helps teams to automate entire workflows without writing custom code. It combines proprietary models, a dedicated actuation layer, and domain-specific training data.

Key features include:

• AI agent hub: A central dashboard to monitor, manage, and orchestrate the fleet of AI agents with visibility into task history, current actions, and future workflows.
• Agentic automation: Automates multi-step processes across departments, reducing manual work.
• Integrations: Works out of the box with platforms like Airtable, Asana, and ServiceNow, allowing agents to operate within the tools the team already uses.
• AI tools: Choose from a library of prebuilt tools or build new ones to create tailored workflows that meet business needs.
• Trigger-based activation: Agents can be automatically activated by events or conditions, enabling hands-free task execution.

Source: Beam

6. UiPath

UiPath is an enterprise automation platform for agentic AI, combining agents, software robots, and human oversight to automate business processes at scale. Unlike traditional RPA, UiPath’s agentic automation enables systems that can reason, adapt, and take initiative, automating workflows as UI elements or business conditions change. It supports BPMN 2.0, integrated AI tools, and customizable agents.

Key features include:

• Agentic automation platform: Integrates agents, AI models, robots, and people into a cohesive system.
• UiPath agent builder: Lets teams design, train, and deploy AI agents for domain-specific tasks like invoice disputes.
• Healing agent: Automatically adapts to UI changes in applications, ensuring ongoing automation without manual rework.
• Conversational AI agents: Boosts productivity across roles through natural language interaction, enabling context-aware execution of tasks.
• Process intelligence and monitoring: Tracks process performance, handles exceptions, and delivers insights with analytics and KPI-driven dashboards.

Source: UiPath

7. Kore.ai

Kore.ai offers an agentic AI platform for designing, deploying, and orchestrating agents across diverse business functions. It supports customer service and internal operations, enabling AI agents that reason, act autonomously, and collaborate across workflows. It includes orchestration, safety, governance, and integration capabilities.

Key features include:

• Multi-agent orchestration: Coordinate collaborative agents with memory, tool access, and task routing across workflows.
• Prebuilt & custom agents: Access templates or build agents for domains like HR, IT, healthcare, and banking using no-code and pro-code tools.
• AI for work, service, and process: Enable agents to handle enterprise search, customer service, or internal process automation with features like intelligent routing, approvals, and human-in-the-loop support.
• Agentic RAG & search AI: Combine retrieval-augmented generation (RAG) with multi-vector and knowledge graph search to support context-aware responses and dynamic query decomposition.
• AI engineering tools: Use Model Hub, Prompt Studio, and Evaluation Studio to manage models, fine-tune prompts, and test agent performance.

Source: Kore.ai 

Conclusion

Agentic AI tools represent a shift from static automation toward adaptive, goal-driven systems that can operate with a high degree of independence. By combining reasoning, planning, and continuous learning, they are capable of managing complex, multi-step workflows across dynamic environments. Their ability to set objectives, adapt strategies, and collaborate with both humans and software makes them especially valuable for enterprises seeking scalable automation that can keep pace with changing business demands.