From Developers to SecOps: How AI Agents Are Changing Business Processes in 40+ Scenarios

The implementation of AI agents is transforming business processes, enabling companies to automate complex tasks that previously required constant human involvement. These autonomous systems, capable of making decisions and acting independently to achieve set goals, are already being used in over 40 different scenarios, from software development to cybersecurity, demonstrating significant efficiency gains and reduced operating costs.

Routine is an invisible drain on resources: thousands of hours are spent on repetitive operations that create no new value but are necessary to keep processes running. From information retrieval and report generation to manual code testing and processing of typical requests, all this distracts qualified specialists from strategic tasks. AI agents offer a solution, allowing this burden to be transferred to machines and freeing up human potential for more complex and creative endeavors.

Software Development: From Code to Deployment

In software development, AI agents act as full-fledged assistants, accelerating every stage of the software lifecycle. They are capable of not just generating code snippets, as many co-pilots do, but also independently designing architecture, selecting optimal tools, and even deploying finished applications.

• Application Building. AI code editors like Cursor AI Editor, Windsurf Editor, and Replit enable the creation and deployment of full-fledged applications (e.g., To-Do list apps) based on simple text prompts. Agents select appropriate frameworks (Flask for APIs, React for frontends), generate code in various languages (Python, JavaScript), and automate workflows using tools like GitHub Actions for testing and deployment. One developer managed to build an entire app in 90 minutes using AI agents that autonomously exchanged credentials and ran tests.
• API Development. AI code editors automate API creation by transforming specifications into functional code. They analyze OpenAPI/Swagger files, generate backend code based on described endpoints, and integrate the result into the developer's environment for testing and deployment.
• Natural Language Code Editing. Agents can understand and execute commands phrased in plain language, such as: "Double the size of the board. Make it green – like an Apple 2e." They identify intent, modify relevant code across multiple files, and apply changes.
• Website Building. AI website creators like v0 by Vercel, Bolt, Lovable, and CerebrasCoder are capable of creating complex platforms, such as e-learning websites, generating key pages: homepages, course listing pages, personalized student dashboards.
• CRM Dashboard Generation. AI agents not only create the frontend interface but also configure backend logic and interact with databases to build complete CRM dashboards.
• Recursive Coding and Legacy Code Modernization. Agents can autonomously rewrite large blocks of code, apply configuration changes, and test outcomes in cycles until a goal is met. For example, GT Edge AI and Persistent use multi-agent frameworks for autonomous migration of COBOL code to modern Java.
• Code Refactoring. AI agents continuously improve the design of the code without changing its functionality, making it easier to understand and maintain.
• Code Suggestion Generation in IDEs. AI agents integrated into IDEs (e.g., VS Code and JetBrains) can perform multi-step coding tasks, going beyond auto-completion to offer real-time solutions.
• CI/CD Monitoring and Optimization. AI agents manage infrastructure in cloud-native environments like Kubernetes. They identify running ingress controllers or other workloads, interpret high-level commands (e.g., "shut down the NGINX pod"), and can be connected to Kubernetes to query cluster state.

SecOps: Cybersecurity at a New Level

In cybersecurity, AI agents significantly enhance the efficiency of SecOps teams by automating threat detection, incident response, and proactive vulnerability hunting.

• Threat Intelligence. Agents gather and correlate threat actor TTPs (tactics, techniques, and procedures) from open-source and proprietary feeds, integrating findings into detection workflows. For example, Microsoft's Security Copilot includes a specialized Threat Intelligence Briefing Agent that dynamically gathers, filters, and summarizes threat intelligence.
• Detection & Triage. Agents perform alert deduplication, suppress false positives, and group related alerts (user, host, tactic) into a single incident, significantly reducing information noise for analysts.
• Contextual Enrichment & Threat Attribution. After initial triage, agents add depth and context to alerts. Automated attacker attribution systems ingest CTI feeds, extract behavioral and temporal features, and map them to known threat actors (e.g., APT41, Mozi, Lazarus).
• Escalation & Handoff. Agents translate technical alerts into analyst-friendly summaries, automatically create tickets, and route incidents to appropriate teams. For example, Torq's Socrates coordinates specialized sub-agents to manage the incident lifecycle, creating cases in ITSM platforms (ServiceNow, Jira) and assigning analysts.
• Proactive Response Actions. In SecOps, agents can isolate endpoints, disable accounts, or kill malicious processes. They automatically query logs, run automated playbooks for investigations and escalation, and generate infrastructure-as-code for remediation.
• Threat Hunting. Agents continuously scan for anomalies across identity, network, and cloud logs, automate repetitive hunts (e.g., IOC lookups), and flag unknown threats by comparing behavior against historical baselines.
• Automated Software Testing. AI testing agents create and execute unit, integration, vulnerability, and performance tests without extensive manual intervention.

Gaming Characters: A New Level of Realism

In the gaming industry, AI agents improve NPCs and other processes in the game world, providing more realistic behavior, adaptability, and procedural content generation.

• Autonomous NPCs. AI agents give non-player characters human-like behavior. For example, researchers created a virtual town with 25 AI agents ("Stanford AI Village") where users can observe their interactions, news sharing, relationship building, and group activity organization.
• Game Playing. AI agents can play video games or assist human players in achieving goals, using pathfinding, navigation, and input emulation. They explore game environments, using trial and error to find optimal strategies.

Other Applications of AI Agents

Beyond the listed areas, AI agents are finding application in many other fields, demonstrating their versatility:

• Content Creation. Agents can generate articles, marketing copy, images, and videos based on specified parameters.
• Insurance. Assist in claims processing, risk assessment, and providing personalized recommendations to clients.
• Human Resources (HR). Automate candidate screening, answer common employee questions, and manage internal processes.
• Customer Service. Provide 24/7 support, resolve standard issues, and route complex inquiries to human operators.
• Scientific Research. Aid in searching and analyzing scientific literature, processing large volumes of data, and formulating hypotheses.
• Computer Users. Agents can perform complex multi-step tasks within operating systems, automating routine operations.
• AI Agent Builders. Agents themselves can participate in the development and optimization of other AI agents, creating self-evolving systems.

How to Implement This in Your Business

If your company has processes that require constant human oversight but follow specific rules, this is a ready-made case for implementing an AI agent. Here's how to get started:

• Identify bottlenecks. Find tasks that consume significant time and resources but do not require creative thinking or complex interpersonal communication. This could include processing typical requests, information retrieval, report generation, or system monitoring.
• Start small. Don't try to automate the entire process at once. Choose a small but impactful work segment where the benefits of implementation will be quickly noticeable and risks are minimal. This might be automating responses to frequently asked questions or initial data analysis.
• Integrate the agent into existing tools. The less employees have to change their habits and learn new software, the faster and more successful the implementation will be. The agent should become a natural part of the familiar work environment, not a separate tool.
• Train and adapt. AI agents require training and continuous adaptation. Collect feedback from users, analyze the agent's performance, and adjust its functionality to best meet your business needs.

If this case sounds like what's happening in your company, our manager can help: he'll analyze your business and niche for free and point out where an AI agent would bring a real result in your case. Message the manager