---
title: "What is agentic AI?"
page_name: "Agentic AI"
type: "glossary"
slug: "agentic-ai"
published_at: "2026-02-18"
modified_at: "2026-02-18"
url: "https://www.sumologic.com/glossary/agentic-ai"
canonical: "https://www.sumologic.com/glossary/agentic-ai"
markdown_url: "https://www.sumologic.com/glossary/agentic-ai.md"
lang: "en"
excerpt: "Learn what agentic AI is, how it differs from traditional and generative AI, and how Sumo Logic Dojo AI transforms security operations."
---

[Glossary](/glossary)# Agentic AI

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##### Table of contents

 

 

 

## What is agentic AI?

Agentic AI refers to artificial intelligence systems that can reason, plan, and act on behalf of human teams. Unlike traditional AI, which waits for predefined inputs and delivers predefined outputs, agentic AI can act autonomously, execute multi-step workflows, wield tools, and make decisions with minimal human intervention. Rather than just surfacing patterns, an AI agent interprets signals in context and recommends, or even initiates, the next necessary step.

Key takeaways

- Agentic AI systems autonomously execute complex workflows without constant human oversight.
- AI agents use automation in security operations for faster triage, investigation, and response to complex tasks.
- Sumo Logic’s Dojo AI uses multiple AI agents to help SOC teams work smarter and faster.

## What’s the difference between agentic AI, traditional AI, and generative AI?

Traditional AI/ML systems focus on detecting anomalies, surfacing patterns, and automating repetitive tasks. It’s trained on historical data to recognize patterns and make decisions based on what it’s seen before.

[Generative AI](https://www.sumologic.com/blog/generative-ai-latest-example-systems-of-insight) introduces a new evolution. Rather than just analyzing existing data, it creates new content based on patterns learned from vast datasets. They respond to prompts but don’t take autonomous action like agentic systems.

Agentic AI combines the pattern recognition of traditional AI with the language understanding of generative AI. It differs by adding autonomous decision-making and task execution, as shown in [Sumo Logic Dojo AI](https://www.sumologic.com/blog/agents-dojo-ai-soc-analyst-mcp). Inside the dojo, multiple AI agents work with your analysts to detect, investigate, and respond to threats in real time, helping you turn overwhelming data into decisive action.

## Why use agentic AI?

Speed and accuracy are everything in security operations. But with analysts facing burnout, massive data volumes, and a widening talent gap, traditional security tools can’t keep up. Agentic AI helps teams work faster and smarter.

### Combat data overload and alert fatigue

Modern security and [DevOps](https://www.sumologic.com/glossary/devops) teams are drowning in telemetry. [Traditional security tools create alert fatigue](https://www.sumologic.com/blog/why-siem) by generating fragmented, low-context signals that analysts have to manually piece together. Agentic AI automates manual tasks that slow analysts down, correlates events, and surfaces only what matters for faster incident detection and response.

### Bridge the cybersecurity talent gap

Junior analysts often lack the context and confidence to investigate security threats quickly. An AI agent acts as a teammate, guiding them step by step, allowing them to form hypotheses and investigate incidents with the confidence of senior analysts.

Lean teams also benefit from agentic systems. With [67% of organizations reporting staffing shortages](https://www.isc2.org/Insights/2024/10/ISC2-2024-Cybersecurity-Workforce-Study), agentic AI provides the assistive automation small teams need to manage complex cloud and hybrid environments. By automating repetitive tasks, analysts can focus on more strategic and critical work.

### Accelerate investigation and response

Agents do more than execute tasks. An [autonomous AI agent can reason through context](https://www.sumologic.com/blog/soc-analyst-agent-for-soc-team), adapt to new scenarios, and deliver actionable recommendations in real time. For security teams managing complex workflows, this means faster [MTTR](https://www.sumologic.com/glossary/mttr), fewer missed threats, and analysts who can finally get ahead of security incidents.

## Challenges with agentic AI 

As these [AI systems become more autonomous, novel security risks are introduced](https://www.sumologic.com/briefs/agent-kill-chain-framework). From AI hallucinations to over-reliance, [deploying AI systems requires thought, proper vetting, and human oversight](https://www.sumologic.com/blog/questions-to-ask-ai-agent-candidates).

Here are potential security risks to look out for:

- **Hallucinations and false confidence**: AI agents may generate plausible-sounding but incorrect analyses.
- **Self-escalation of privileges**: An autonomous agent might inappropriately grant itself higher levels of access to perform a task.
- **Over-reliance on automation**: Analysts may trust agent recommendations without sufficient validation, potentially missing critical context or nuance.
- **Model drift and data quality**: Agents trained on historical data may struggle with novel attack patterns or environmental changes.
- **Security and privacy concerns**: Agents with broad access to logs, systems, and sensitive data require careful permission scoping and audit logging.

## Transform your SOC with Sumo Logic Dojo AI 

With [Sumo Logic Dojo AI](https://www.sumologic.com/blog/welcome-dojo-ai-agents-soc), you get a team of intelligent agents that work alongside your SOC to automate tasks, streamline investigations, and reduce alert fatigue. Purpose-built agents handle alert triage, threat investigation, and query generation. Mobot is your single point of access to the entire agentic team, providing a unified conversational interface that lets analysts work in natural language. And with the Model Context Protocol (MCP), you can bring your own AI models and integrate existing tools.

  

[**Learn how Dojo AI is driving intelligent DevSecOps.**](https://www.sumologic.com/solutions/dojo-ai)

### FAQs

 Does Dojo AI use any open-source library, GenAI providers, or cloud providers?+Yes. For Generative AI, Mobot leverages a foundation model provided via Amazon Bedrock, as detailed in our documentation. Additionally, our classical machine learning capabilities utilize select open-source Python libraries that have been reviewed and approved by Sumo Logic for security and compliance.

 What is Sumo Logic Dojo AI?+Sumo Logic Dojo AI is a multi-agent AI platform built to power intelligent security operations and incident response. It is designed to act autonomously while continuously adapting to evolving threats.

 What is Mobot?+Mobot is the unified conversational interface of Sumo Logic Dojo AI that connects users to specialized agents, turning natural language requests into actionable insights quickly and intuitively.

 Do Dojo AI agents access customer data?+Agent interaction with customer data varies by capability.

Mobot (including Query Agent and Knowledge Agent) and Summary Agent do NOT process or analyze customer data.

The SOC Analyst Agent (in preview as of February 2026 with certain chosen customers) processes customer data in order to help review insight data, correlate activity, and assist in triage and investigation as directed by the user.

Any AI capability that processes customer data:

- Is available only through explicit customer opt in (never automatically provisioned)
- Requires execution of the applicable AI addendum to the client agreement

Customers retain control over whether these data-processing capabilities are enabled in their environment.

[AI Instructions](https://www.sumologic.com/ai-instructions.md)