AI Agent – Autonomous AI Systems in Software Development

An AI agent is an AI system that does not merely respond to individual questions, but autonomously pursues goals, makes decisions, and in doing so uses external tools, APIs, or other systems. Unlike a simple chatbot or a Large Language Model (LLM) in pure question-answer mode, an AI agent operates in a continuous cycle of perception, planning, action, and reflection – until a task is completed or a defined abort criterion is reached.

The term became widely used with the emergence of powerful LLMs like GPT-4 and Claude from 2023 onwards. Modern AI agents combine the language competence of an LLM with the ability to perform real actions: reading and writing files, executing code, querying databases, controlling web browsers, or calling external services. This closes the gap between a passive tool and an action-capable system.

What Distinguishes an AI Agent from a Simple LLM?

LLM in Simple Mode

An LLM without an agent architecture receives a prompt and returns a response. The interaction is stateless and one-time:

• No persistent state between requests
• No ability to control external systems
• No autonomous planning across multiple steps
• No ability to detect and correct its own errors

AI Agent

An AI agent goes significantly further:

• Persistent state: The agent remembers the progress of a task across multiple steps
• Tool use: It can call functions (read file, make API request, execute code)
• Multi-step planning: It breaks complex tasks into subtasks and works through them
• Self-reflection: It evaluates its own results and corrects errors
• Goal orientation: It works toward a defined goal, not just a single answer

Properties of an AI Agent

Autonomy

An AI agent independently decides which steps are needed to achieve a goal. It does not wait for a new instruction after each step but continues working on its own. The degree of autonomy varies by system: from "human-in-the-loop" (a person confirms each step) to fully autonomous agents that work for hours or days without intervention.

Goal Orientation

Instead of reacting to individual prompts, an AI agent receives a high-level goal: "Implement this feature", "Analyze this data and create a report", or "Process incoming customer requests". The goal remains constant throughout the entire task, even if the agent executes dozens of individual steps to achieve it.

Tool Use

Tools are the key that distinguishes AI agents from pure text generators. An agent can:

• Execute filesystem operations (read, write, search)
• Run code in a sandbox and evaluate the result
• Perform web searches
• Call APIs and process the responses
• Query databases
• Control browsers (e.g. via Playwright)

Persistence and Memory

AI agents can store information beyond the current session. This happens through various mechanisms: short-term memory in the context window, medium-term storage in files (memory files), or long-term persistence in databases. This capability enables working on a project over days or weeks.

Decision-Making Ability

An AI agent continuously makes decisions: Which tool should be called next? Is the intermediate result sufficient, or does it need improvement? What is the most sensible approach in case of an error? These decisions are made by the LLM at the core of the agent based on the current context and state.

Types of AI Agents

Conversational Agents

Conversational agents conduct multi-step dialogues and can retrieve external information in the process. They are the most common form: customer service bots that access order data, or assistants that retrieve information from a knowledge base before responding. The difference from a simple chatbot lies in the ability to integrate real data via tool calls.

Coding Agents (Agentic Coding)

Coding agents specialize in software development. They read codebases, write and edit files, run builds and tests, analyze error messages, and iterate until a task is fulfilled. These agents have fundamentally changed software development. Well-known examples are Claude Code, Cursor Composer, Devin, and GitHub Copilot Workspace.

Task Agents

Task agents execute clearly defined tasks in a specific domain: researching and summarizing information, processing incoming documents, monitoring systems and automatically responding to certain events. They are frequently integrated into automation platforms like n8n.

Multi-Agent Systems

In multi-agent systems, multiple specialized agents work together. An orchestrator agent splits a complex task and coordinates specialized sub-agents: a frontend agent, a backend agent, a testing agent. Communication between agents happens via structured messages or shared storage.

AI Agents in Software Development

Claude Code

Claude Code is a terminal-based coding agent from Anthropic. It has direct access to the filesystem, can execute Git commands, start tests, and call multiple tools in parallel. Via CLAUDE.md project instructions, it can be bound to project conventions. Claude Code is the tool that Elasticbrains uses daily for developing all projects.

Cursor Composer

Cursor is a VS Code-based editor with an integrated agent function ("Composer"). The agent can open and edit multiple files simultaneously, execute commands in the terminal, and be bound to project standards via .cursorrules.

GitHub Copilot Workspace

Copilot Workspace is GitHub's approach to agent-based coding directly from issues. The agent reads the repository, creates an implementation plan, and implements changes introduced via pull requests.

Devin

Devin by Cognition AI is one of the most well-known fully autonomous coding agents. It operates in a complete development environment with terminal, browser, and editor, and can work autonomously on complex tasks for hours.

Agentic vs. Non-Agentic AI: A Comparison

The difference between a passive LLM and an active AI agent can be illustrated with a concrete example:

Task: "Find all security vulnerabilities in our backend and create a ticket."

Non-Agentic LLM: Returns general hints about common security vulnerabilities in backend code – based on its training, without knowing the actual project.

AI Agent: Reads the actual backend code, runs static analysis tools, identifies concrete weaknesses in the relevant files, creates a structured report, and opens a ticket in the project management system.

Architecture of a Coding Agent: The Perception-Planning-Action-Reflection Loop

1. Perception

The agent perceives its state and environment. It reads relevant files, checks the current Git status, analyzes error messages from a failed build, or reads the task description. Everything needed for planning flows into the context.

2. Planning

Based on the perceived information, the LLM at the core of the agent creates a plan. In Claude Code this happens internally: Which files need to be changed? Which tests should be run afterwards? In what order must steps occur to account for dependencies?

3. Action

The agent executes its planned action. It calls tools: reads files, writes code, executes bash commands, queries APIs. A modern agent like Claude Code can issue multiple tool calls in parallel when they are independent of each other, significantly increasing efficiency.

4. Reflection

After the action, the agent evaluates the result. Was the build successful? Do tests fail? Does the result match the expectation? Based on this reflection the agent plans the next step or recognizes that the task is complete. This cycle repeats until the goal is reached.

Multi-Agent Systems: When Agents Work Together

For very complex tasks, a single agent is not enough. Multi-agent architectures rely on specialization and coordination:

• Orchestrator Agent: Receives the overall task, breaks it into subtasks, and delegates to sub-agents
• Specialized Sub-Agents: Each agent is responsible for a specific area (e.g. research, implementation, testing, review)
• Communication Protocol: Agents exchange structured messages to pass on intermediate results
• Shared Storage: Agents can access common files or databases to share information

Multi-agent systems can parallelize certain tasks and thus process them significantly faster than a single agent. However, coordination overhead and error potential increase with the complexity of the system.

Challenges When Using AI Agents

Hallucinations and Errors

Since the LLM at the core of the agent can still invent information that does not match reality, errors can propagate across multiple steps. An agent that makes a wrong assumption in step 2 can arrive at a completely wrong result in step 8. Regular checkpoints and human review are therefore important.

Security and Permissions

An autonomous agent that can write files, execute commands, and call APIs carries significant security risks. Prompt injection attacks – where malicious instructions from external sources (e.g. web pages being read) are introduced into the context – are a real problem. Agents must be operated with minimal permissions following the least-privilege principle.

Control and Traceability

With fully autonomous agents it can be difficult to trace why the agent made a particular decision. Logging all tool calls and decision steps is essential for auditing and debugging. Many systems therefore offer gradations: from "agent asks at every step" to "agent acts fully autonomously".

Cost

A multi-step agent workflow can consume many thousands of tokens, since the entire context accumulated so far is sent with every LLM call. For high-frequency or lengthy tasks, API costs can be substantial. Efficient context management and choosing the right model for each sub-step are important optimization areas.

Infinite Loops and Abort Conditions

Without clear abort conditions, agents can get stuck in infinite loops in which they repeatedly attempt to fix the same error. Maximum iteration counts, time limits, and explicit success criteria are necessary guardrails for every agent workflow.

Elasticbrains and AI Agents

At Elasticbrains, AI agents have been an integral part of software and product development since 2023:

• Development with Claude Code: All projects are developed with Claude Code as the primary coding agent. Through structured CLAUDE.md project instructions, the agent operates within defined conventions and security rules.
• EQ-Sales-AI: Our AI sales assistant uses an agent-based architecture for processing sales conversations, PII detection, and automated follow-up processing. Multiple specialized services (Whisper, GLiNER, Guardian) work together as coordinated agents.
• Quality Assurance: Playwright-based test agents automatically run end-to-end tests after deployments and report deviations.

Agentic Coding Workshop at Elasticbrains

Would you like to use AI agents in your software development? In the Agentic Coding Workshop we teach hands-on how coding agents are used professionally:

• AI agent fundamentals: architecture, possibilities, and limitations
• Coding agents in everyday use: Claude Code, Cursor Composer, and further tools
• Project instructions and context engineering for reproducible results
• Security and control: how to integrate agents into production environments
• Multi-agent setups: when specialization and coordination are worthwhile
• Hands-on: Solve real tasks from your project with AI agents

The workshop is aimed at development teams that want to approach the use of AI agents in a structured and methodical way – beyond ad-hoc experiments.

Further Resources

• Glossary: Agentic Coding – professional AI-assisted development
• Glossary: LLM (Large Language Model) – the technological foundation of AI agents
• Glossary: MCP (Model Context Protocol) – standard for tool integration in AI agents
• Glossary: Context Engineering – context management for agents
• Glossary: Prompt Engineering – foundation for effective agent instructions
• Workshop: Agentic Coding Workshop