AI agents are one of the defining trends in both enterprise automation and developer productivity. They are already taking over routine work and saving professionals several hours a day — qualifying leads, generating marketing content, writing code, or producing test coverage.
What Are AI Agents
An AI agent (also referred to as an LLM-based agent is a solution built on large language models that can execute a sequence of steps until a task is done, with limited human involvement. It can plan a sequence of steps toward a defined goal, make decisions within a set of rules, gather data, and interact with external systems.
AI agents are a key component of agentic AI systems, where software can reason, plan, and execute tasks with minimal user intervention.
How AI Agents Differ from LLM Chats and RPA
Unlike LLM-based chat interfaces, which stay within a single conversation, AI agents are proactive and autonomous.
At the same time, AI agents determine their own path to a goal, rather than following a fixed script the way RPA does.
Advantages of AI Agents
- Working with natural language and unstructured data.
- The ability to use a variety of tools to complete tasks.
- Adapting to changes in interface, prompt wording, or input format.
- Handling tasks without a predefined plan, including edge cases and rare scenarios.
- Transparent reasoning — agents can surface their thought process and explain the logic behind their decisions.
- Native support for multi-agent workflows, enabling collaboration between agents.
Limitations of AI Agents
- Non-deterministic behavior — the same prompt can yield different results; hallucinations and flawed reasoning are possible.
- Higher token costs compared to traditional automation approaches.
- Security risks, including potential data leakage and vulnerability to prompt injection attacks.
- Dependence on high-quality data, clear instructions, and well-designed tools.
- A limited track record of proven business outcomes and a lack of established standards — though both are areas of active development.
- Effective deployment requires governance, monitoring, access control, and integration with existing business processes.
What Tasks AI Agents Handle
AI agents perform best on routine tasks that involve unstructured data — emails, documents, code, tickets, and similar inputs — and where there is room for variability in how the task gets approached.
It's also worth distinguishing between AI agents built for business use and those built for software development.
Business AI agents operate on the end-user side — serving managers, analysts, and support staff — within enterprise systems such as CRM, ERP, and BPM platforms. They work with business data: customer records, deals, documents, correspondence, and information gathered from the web.
Development AI agents are available through specialized tooling — IDEs, CLIs, and vendor-built applications — and operate on source code, tests, specifications, and technical documentation.
Examples include coding agents, AI code assistants, and agentic development tools that can generate, modify, test, and review code.
Business Processes AI Agents Can Automate:
- Lead qualification and opportunity management.
- Research, information gathering, and data analysis.
- Resume screening and initial candidate assessment.
- Marketing content generation and adaptation for different audiences.
- Travel search and booking.
Software Development Tasks AI Agents Can Automate:
- Feature implementation and code refactoring.
- Requirements and specification preparation.
- Code review.
- Legacy system modernization and migration.
- Test generation based on functional requirements.
These capabilities are increasingly delivered through coding agents and AI-assisted development tools integrated into modern IDEs and software delivery workflows.
How Jmix Supports AI Agent Development for Business
Jmix enables teams to design AI agents as part of enterprise application development. These agents use a large language model as their intelligence layer and rely on enterprise application services as their tools. This approach makes it possible to build enterprise AI agents and agentic applications that operate within existing business processes, security rules, and governance policies.
Jmix provides:
- A standard architectural foundation for enterprise applications.
- LLM integration through Spring AI and REST services built on the standard Jmix technology stack.
- Application infrastructure that allows agents to create records, assign tasks, generate documents, manage email workflows, and more.
- Human-in-the-loop workflows for validating and approving agent outputs.
- Fine-grained security controls that restrict agent access to sensitive data and help prevent unauthorized data exposure.
- Built-in auditing and activity logging for agent operations.
How Jmix Supports AI-Assisted Software Development
Together with partner technologies, Jmix helps organizations build a controlled AI-driven development environment that aligns with corporate standards and governance requirements.
- Jmix AI Assistant uses RAG technology to generate framework-aware specifications, improving prompt quality and development outcomes.
- AI coding agents such as Codex, Claude Code, and OpenCode can run on local language models with access to project documentation.
- The Jmix framework provides an architectural template for code generation based on patterns validated across more than 3,000 projects.
- Jmix Studio visualizes intermediate results and validates generated code against predefined rules.
- The IntelliJ IDEA MCP protocol allows agents to use the static analyzer from JetBrains IntelliJ, which also includes inspections from Jmix Studio.
- Jmix Security and Server-side UI provide application-level security, including gateway-based data access control, client-side data leak protection, and integration with authentication providers such as OpenID Connect, LDAP, SAML, and Identity Blitz.
- A dedicated Java runtime environment enforces security controls at the infrastructure level, providing a stable and protected foundation for running agentic workloads in production.
- IntelliJ IDEA provides a unified control center where all development tools are integrated and can communicate with one another.