Building AI Coding Agents for the Terminal: Scaffolding, Harness, Context Engineering, and Lessons Learned
- URL: http://arxiv.org/abs/2603.05344v1
- Date: Thu, 05 Mar 2026 16:21:08 GMT
- Title: Building AI Coding Agents for the Terminal: Scaffolding, Harness, Context Engineering, and Lessons Learned
- Authors: Nghi D. Q. Bui,
- Abstract summary: We present OPENDEV, an open-source, command-line coding agent engineered specifically for this new paradigm.<n>It overcomes these challenges through a compound AI system architecture with workload-specialized model routing.<n>It employs an automated memory system to accumulate project-specific knowledge across sessions and counteract instruction fade-out.
- Score: 9.127884945730019
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The landscape of AI coding assistance is undergoing a fundamental shift from complex IDE plugins to versatile, terminal-native agents. Operating directly where developers manage source control, execute builds, and deploy environments, CLI-based agents offer unprecedented autonomy for long-horizon development tasks. In this paper, we present OPENDEV, an open-source, command-line coding agent engineered specifically for this new paradigm. Effective autonomous assistance requires strict safety controls and highly efficient context management to prevent context bloat and reasoning degradation. OPENDEV overcomes these challenges through a compound AI system architecture with workload-specialized model routing, a dual-agent architecture separating planning from execution, lazy tool discovery, and adaptive context compaction that progressively reduces older observations. Furthermore, it employs an automated memory system to accumulate project-specific knowledge across sessions and counteracts instruction fade-out through event-driven system reminders. By enforcing explicit reasoning phases and prioritizing context efficiency, OPENDEV provides a secure, extensible foundation for terminal-first AI assistance, offering a blueprint for robust autonomous software engineering.
Related papers
- The Auton Agentic AI Framework [5.410458076724158]
The field of Artificial Intelligence is undergoing a transition from Generative AI to Agentic AI.<n>This transition exposes a fundamental architectural mismatch: Large Language Models (LLMs) produce unstructured outputs, whereas the backend infrastructure they must control requires deterministic, schema-conformant inputs.<n>The present paper describes the Auton Agentic AI Framework, a principled architecture for the creation, creation, and governance of autonomous agent.
arXiv Detail & Related papers (2026-02-27T06:42:08Z) - EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
arXiv Detail & Related papers (2026-01-29T11:33:49Z) - Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs [65.6660735371212]
We present textbftextscJustAsk, a framework that autonomously discovers effective extraction strategies through interaction alone.<n>It formulates extraction as an online exploration problem, using Upper Confidence Bound--based strategy selection and a hierarchical skill space spanning atomic probes and high-level orchestration.<n>Our results expose system prompts as a critical yet largely unprotected attack surface in modern agent systems.
arXiv Detail & Related papers (2026-01-29T03:53:25Z) - ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
arXiv Detail & Related papers (2026-01-27T13:43:59Z) - CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents [60.98294016925157]
AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss.<n>We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content.<n>Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks.
arXiv Detail & Related papers (2026-01-14T23:06:35Z) - Agentic AI Reasoning for Mobile Edge General Intelligence: Fundamentals, Approaches, and Directions [74.35421055079655]
Large language models (LLMs) have enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and autonomous decision-making capabilities.<n>Mobile Edge General Intelligence (MEGI) brings real-time, privacy-preserving reasoning to the network edge.<n>We propose a joint optimization framework for efficient LLM reasoning deployment in MEGI.
arXiv Detail & Related papers (2025-09-27T10:53:48Z) - State and Memory is All You Need for Robust and Reliable AI Agents [29.259008600842517]
Large language models (LLMs) have enabled powerful advances in natural language understanding and generation.<n>Yet their application to complex, real-world scientific remain limited by challenges in memory, planning, and tool integration.<n>Here, we introduce SciBORG, a modular agentic framework that allows LLM-based agents to autonomously plan, reason, and achieve robust and reliable domain-specific task execution.
arXiv Detail & Related papers (2025-06-30T02:02:35Z) - AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance [7.110126223593506]
This paper envisions a future where AI agents autonomously manage tasks that previously required distinct expertise and manual coordination.<n>We introduce AssetOpsBench -- a unified framework and environment designed to guide the development, orchestration, and evaluation of domain-specific agents.<n>We outline the key requirements for such holistic systems and provide actionable insights into building agents that integrate perception, reasoning, and control for real-world industrial operations.
arXiv Detail & Related papers (2025-06-04T10:57:35Z) - Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI [0.36868085124383626]
Review presents a comprehensive analysis of two emerging paradigms in AI-assisted software development: vibe coding and agentic coding.<n> Vibe coding emphasizes intuitive, human-in-the-loop interaction through prompt-based, conversational interaction.<n>Agentic coding enables autonomous software development through goal-driven agents capable of planning, executing, testing, and iterating tasks with minimal human intervention.
arXiv Detail & Related papers (2025-05-26T03:00:21Z) - Autonomous Deep Agent [0.7489814067742621]
Deep Agent is an advanced autonomous AI system designed to manage complex multi-phase tasks.<n>The system's foundation is built on our Hierarchical Task DAG framework.<n>Deep Agent establishes a novel paradigm in self-governing AI systems.
arXiv Detail & Related papers (2025-02-10T21:46:54Z) - Planning-oriented Autonomous Driving [60.93767791255728]
We argue that a favorable framework should be devised and optimized in pursuit of the ultimate goal, i.e., planning of the self-driving car.
We introduce Unified Autonomous Driving (UniAD), a comprehensive framework that incorporates full-stack driving tasks in one network.
arXiv Detail & Related papers (2022-12-20T10:47:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.