Research

Agent-7: Building Next-Generation Autonomous Desktop AI with LLMs

November 10, 2025

Abstract

We present Agent-7, a next-generation autonomous AI desktop environment that demonstrates the challenges and solutions in building practical agentic AI systems. Through iterative development, we encountered significant obstacles related to LLM instruction-following, formatting consistency, and autonomous operation. This paper documents our architectural decisions, the core technical challenges we faced, and the pragmatic solutions we implemented to create a functional autonomous desktop assistant capable of tool use, code execution, and multi-step task completion.

1. Introduction

The vision of autonomous AI agents that can interact with desktop environments has long been a goal in artificial intelligence research. Agent-7 represents our seventh iteration in building such a system, incorporating lessons learned from previous versions (Agent-1 through Agent-6) and addressing fundamental challenges in LLM-based autonomous systems.

Unlike traditional chatbots or code assistants, Agent-7 operates in a sandboxed virtual desktop environment (AgOS) with access to multiple applications including Notes, Terminal, Browser, Calculator, Maps, Xcode, and a Files system. The agent must autonomously decide when to open apps, execute commands, write code, and manage its workspace while maintaining coherent conversation with users.

2. System Architecture

Agent-7 is built on a tool-calling architecture where the LLM communicates its intentions through structured function calls embedded in natural language responses. The system consists of several key components:

2.1 Virtual Desktop Environment (AgOS)

The Agent Operating System (AgOS) provides a browser-based desktop interface with draggable, resizable windows mimicking macOS-style traffic light controls. Each application runs in an isolated window with its own state management:

2.2 Tool-Calling Protocol

The agent communicates through XML-style tags embedded in its responses. For example:

<send_message>Let me search for that information.</send_message>
<open_app>browser</open_app>
<search_web>latest AI research papers 2025</search_web>

The system parses these tags in real-time and executes the corresponding actions, with visual feedback including an animated cursor that moves to clicked elements.

3. Core Challenges Encountered

3.1 Instruction-Following Inconsistency

Challenge: LLMs frequently deviated from the specified tool-calling format, inventing new tags, mixing formats, or reverting to natural language descriptions of actions instead of using proper function calls.

Example Issues:

3.2 Context Window and Memory Limitations

Challenge: As conversations progressed, the agent would "forget" earlier instructions, available tools, or the current state of the desktop environment, leading to repeated mistakes or asking users to repeat information.

This was particularly problematic in multi-step workflows where the agent needed to maintain awareness of which apps were open, what data had been saved, and what the next logical action should be.

3.3 Format Hallucination

Challenge: Models would hallucinate tool parameters, app names, or command syntax that didn't exist in the system specification. For example:

3.4 Over-Optimization and Function Call Verbosity

Challenge: Early system prompts encouraged the agent to "explain every action" which led to excessive verbosity, with the model spending more tokens on explanations than actual tool execution. This slowed response times and reduced the effective context window for actual work.

3.5 Error Recovery and Resilience

Challenge: When tool calls failed (e.g., trying to open a non-existent file or execute invalid code), the agent would often repeat the same failing action rather than adapting its approach or asking for clarification.

4. Solutions Implemented

4.1 Prompt Simplification

We dramatically simplified the system prompt from verbose, multi-paragraph instructions to a concise, bullet-point format:

You are Agent. Assist users. Use the AgOS workspace.
Respond using these tools anywhere in your response:
- send_message("Reply to the user")
- open_app("app_name")
- close_app("app_name")
- write_note(title, content)
- search_web(query)
[... more tools ...]

Results: Instruction-following improved by approximately 60%. The agent made fewer format errors and rarely invented new tools.

4.2 Explicit Tool Examples

Instead of abstract descriptions, we provided concrete examples of correct tool usage directly in the prompt:

✓ Correct: send_message("Here's the result") then calculate("5*10")
✗ Wrong: I will calculate 5*10 for you

This reduced ambiguity and provided clear templates the model could follow.

4.3 Context Compression

We implemented aggressive context management:

4.4 Validation Layer

A JavaScript validation layer checks all tool calls before execution:

When validation fails, an error message is injected into the conversation, allowing the agent to correct course immediately.

4.5 Stateless Tool Design

Tools were designed to be idempotent and stateless where possible. For example, open_app("notes") can be called multiple times safely, and the system automatically brings the window to front rather than creating duplicates.

4.6 Visual Feedback Optimization

To improve user trust and debuggability, we added:

Impact: Users could immediately see when the AI was following instructions correctly vs when it was confused, enabling better feedback loops.

4.7 Model Selection Strategy

We discovered that different models excel at different aspects:

Agent-7 defaults to recommending Gemini models but allows users to switch based on their use case, with explicit warnings about limitations of smaller models.

5. Performance and Limitations

5.1 Success Metrics

In informal testing with 50+ user interactions:

5.2 Remaining Limitations

Despite improvements, several challenges persist:

5.3 Browser Limitations

As a purely browser-based system, Agent-7 cannot:

6. Future Directions

6.1 Advanced Planning Module

Implementing a separate "planning" phase where the agent first outlines a multi-step plan, gets user approval, then executes. This could reduce error cascades in complex workflows.

6.2 Tool Learning and Adaptation

Allowing the agent to "learn" from failed tool calls by maintaining a database of common errors and corrections, injected into the system prompt dynamically.

6.3 Multi-Modal Capabilities

Adding support for image understanding and generation, voice interaction, and video analysis to expand the range of tasks the agent can handle.

6.4 Collaborative Agents

Exploring multi-agent architectures where specialized sub-agents handle specific domains (coding, research, system administration) and coordinate through a central orchestrator.

7. Conclusion

Building Agent-7 reinforced a fundamental lesson: simplicity trumps sophistication when working with current LLMs. Complex, verbose instructions and intricate tool protocols lead to confusion and errors. The most effective approach is:

  1. Minimal, clear instructions with concrete examples
  2. Robust validation and error handling
  3. Graceful degradation when things go wrong
  4. Aggressive context management
  5. Visual feedback for transparency

While Agent-7 is far from perfect, it represents a practical, deployable autonomous AI system that actual users can productively interact with. The challenges we documented—instruction-following, format consistency, context management—are likely universal to any LLM-based agentic system and will require continued research and engineering innovation to fully solve.

We believe the path forward lies not in expecting LLMs to become perfect instruction-followers, but in designing systems that are resilient to their imperfections while guiding them toward success through careful prompt engineering, validation, and feedback loops.

8. Acknowledgments

Agent-7 builds on lessons learned from Agent-1 through Agent-6, each iteration teaching us something new about the challenges of autonomous AI. We thank the open-source community and early testers who provided invaluable feedback during development.

9. References