Terminal Agents Suffice for Enterprise Automation
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
There has been growing interest in building agents that can interact with digital platforms to execute meaningful enterprise tasks autonomously. Among the approaches explored are tool-augmented agents built on abstractions such as Model Context Protocol (MCP) and web agents that operate through graphical interfaces. Yet, it remains unclear whether such complex agentic systems are necessary given their cost and operational overhead. We argue that a coding agent equipped only with a terminal and a filesystem can solve many enterprise tasks more effectively by interacting directly with platform APIs. We evaluate this hypothesis across diverse real-world systems and show that these low-level terminal agents match or outperform more complex agent architectures at a fraction of the cost. Our findings suggest that simple, flexible programmatic interfaces combined with strong foundation models should be the backbone of enterprise automation.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 30 of its 38 references that are in the map. First posted on arXiv 31 Mar 2026.
Builds on
30 of 38 references.
Closest ancestors · 4
Same direction, newest first
- 2024AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents26 Jul
- 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May
- 2024Executable Code Actions Elicit Better LLM Agents1 Feb
Other references · 26
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2021Program Synthesis with Large Language Modelscited by 3,637
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
- 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
- 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,242
- 2025A-MEM: Agentic Memory for LLM Agentscited by 1,063
- 2023Large Language Modelscited by 780
- 2025UI-TARS: Pioneering Automated GUI Interaction with Native Agentscited by 610
- 2025Agentic Context Engineering: Evolving Contexts for Self-Improving Language Modelscited by 398
- 2024WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?cited by 363
- 2024TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Taskscited by 326
14 more
- 2026SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Taskscited by 315
- 2025ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memorycited by 284
- 2025Plan-and-Act: Improving Planning of Agents for Long-Horizon Taskscited by 195
- 2025Recursive Language Modelscited by 104
- 2025MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Serverscited by 96
- 2024WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Taskscited by 90
- 2024Beyond Browsing: API-Based Web Agentscited by 78
- 2024CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environmentscited by 71
- 2025WALT: Web Agents that Learn Toolscited by 16
- 2026EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settingscited by 15
- 2024AXIS: Efficient Human-Agent-Computer Interaction with API-First LLM-Based Agentscited by 12
- 2026World of Workflows: A Benchmark for Bringing World Models to Enterprise Systemscited by 11
- 2025SCUBA: Salesforce Computer Use Benchmarkcited by 7
- 2024Generating a Low-code Complete Workflow via Task Decomposition and RAGcited by 3
Built on by
Citing papers per year: 2026: 6.
6 papers.
- 2026Are Online Skill and Memory Modules Always Worth Their Tokens? A Budget-Constrained Study of Web Agentscited by 6
- 2026GUI vs. CLI: Execution Bottlenecks in Screen-Only and Skill-Mediated Computer-Use Agentscited by 5
- 2026GUI Agents with Reinforcement Learning: Toward Digital Inhabitantscited by 3
- 2026Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamicscited by 2
- 2026Terminal Agents: A Survey of AI Agents in Command-Line Environmentscited by 1
- 2026CUAWright: A Minimal Unified Interface for Digital Agentscited by 0
- 2026CUAWright: A Minimal Unified Interface for Digital Agents2 Oct 2026
- 2026Terminal Agents: A Survey of AI Agents in Command-Line Environments20 Aug 2026
- 2026GUI vs. CLI: Execution Bottlenecks in Screen-Only and Skill-Mediated Computer-Use Agents22 Jun 2026
- 2026Are Online Skill and Memory Modules Always Worth Their Tokens? A Budget-Constrained Study of Web Agents12 Jun 2026
- 2026Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamics12 May 2026
- 2026GUI Agents with Reinforcement Learning: Toward Digital Inhabitants30 Apr 2026
Similar, not cited
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- 2026Designing Intelligent Enterprise Agents: A Capability-Aligned Multi-Agent Architecture
- 2025AI Agentic workflows and Enterprise APIs: Adapting API architectures for the age of AI agents
- 2026Agentic Nesting: A New Methodology for Existing Enterprise Application Integration and Services
- 2026From Everything-is-a-File to Files-Are-All-You-Need: How Unix Philosophy Informs the Design of Agentic AI Systems
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