Developer Experience & AI Agents - The software engineering industry has seen rapid changes in the past year, making future predictions difficult [1][2][3] - Companies are questioning whether current investments in developer tools will be valuable in the future [4] - Coding agents are transformative, but not the only investment needed for software engineering organizations [5] - No-regret investments should focus on inputs to AI agents and things around them that enhance their effectiveness [7][8] Development Environment & Tools - Standardize development environments using industry-standard tools to align with AI model training sets [9][10] - Prioritize CLIs or APIs for agent actions to ensure accuracy and effectiveness [13][14] - Validation is crucial; high-quality validation with clear error messages significantly improves agent capabilities [15][16] Codebase & Documentation - Invest in well-structured and testable codebases for better agent performance [18][19] - Comprehensive documentation is essential, especially for information not directly in the code [20][21][22][23][24][25] Code Review & Collaboration - Improve code review velocity to address bottlenecks caused by increased PRs from agentic coding [26][27] - Distribute code review responsibilities and establish clear ownership with SLOs to avoid overburdening individual reviewers [29][30][31] - Maintain high code review quality to prevent a decline in productivity from agentic coders [32][33][34] Key Principle - What benefits humans also benefits AI; investments in these areas will help developers regardless of AI outcomes [44][45]

AI Coding & Productivity - AI is considered a significant technological wave with the potential to create substantial economic impact and productivity gains [3] - The development of effective agentic solutions, particularly in software and coding, has become visible, with widespread adoption of AI assistance in coding since the introduction of GitHub Copilot [4][5] - The industry anticipates that the paradigm of AI assistance will extend to other areas of software development and various industries beyond coding [5] Challenges & Solutions - Generating more AI code without addressing subsequent steps can be a liability, increasing incidents and making code maintenance harder [12][13] - The industry believes the solution lies in applying AI to monitor, maintain, and troubleshoot AI-generated code to improve overall velocity [14][15] - Resolve AI focuses on building AI solutions that prioritize trust for software engineers, allowing AI to investigate and propose solutions, with human oversight before full automation [17][18] Future of AI in Software Engineering - The industry predicts that within a year, AI will become the primary driver of software, with humans overseeing at a higher level, and within two to three years, AI will make most decisions [20] - The industry emphasizes the importance of deep agentic applications that understand the domain and customer context, requiring innovation in models to handle more data and longer task horizons [27] - Multi-agent systems with various layers of guardrails, checks, and validations are crucial for reliable AI performance, with an orchestrator agent managing other agents [31][32][33] AI Model Specialization - The most capable and expensive AI models are typically used at the top level for reasoning and planning, while specialized or open-source models can handle underlying tasks [34][36] - The industry anticipates that domain-specific large models will emerge for areas like software and customer service due to their significant economic impact [36] Adoption & Cultural Impact - While engineers are early adopters of AI, there is some resistance to change and concerns about job security [38][39] - The industry believes the goal is to produce technology faster, benefiting the world, and engineers will operate at a higher level of abstraction, with AI handling low-level tasks [40][44]

Agent Technology Adoption - Agents are increasingly used in software engineering, but deployment results are inconsistent [1] - Agents often perform well in demonstrations but fail in production environments [1] - The issue is not model quality but the readiness of the environment for agents [1] Factors Affecting Agent Performance - Agents require fast feedback loops, clear instructions, and predictable environments [1] - Agents can fail due to missing environment variables, undocumented dependencies, and unwritten rules [1] Agent Readiness Framework - Agent Readiness can be measured and improved to address the challenges [1] - Eight categories determine codebase agent-readiness, including style validation, build systems, development environments, and observability [1] - Organizations can score their repositories, identify quick wins, and create environments where agents can reliably ship code [1] Practical Application - Factory AI's experience running autonomous agents in enterprise production repositories provides real-world insights [1] - A practical framework can help teams make their agents more productive [1]

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AI in Software Engineering - AI models have solved coding, but not software engineering, as production speed is crucial [4] - Building AI to accelerate production is challenging due to reliability requirements and the need for multi-agent orchestration [5][6][7] - Resolve AI focuses on using AI to address the complexities of production systems, which involves more than just code [13] Resolve AI's Solution - Resolve AI provides AI site reliability engineer agents to troubleshoot alerts and incidents [11] - Resolve's agents can understand production systems from code to backend databases, offering a faster solution [11] - Customers are using Resolve AI for "vibe debugging," indicating usage beyond incidents and alerts, leading to increased product usage [12] Talent Acquisition - Resolve AI competes with companies like Meta, OpenAI, and Anthropic for AI engineers [14] - Resolve AI attracts top researchers by offering the opportunity to significantly impact the company and change software engineering [16] Future of Software Engineering - Humans will operate at a higher level of abstraction, with agents handling much of the work [17] - Underlying infrastructure and tools will adapt to be more suitable for agents [17]

Software Development & Problem Solving - Software development addresses problems created by software itself [1] - Fixing bugs on GitHub exemplifies using software to solve software-related issues [1] Engineering & Crisis Management - The idea that engineering cannot solve crises it creates is considered flawed [1]

AI in Software Engineering - AI models have solved coding, but not software engineering, as production speed and tribal knowledge are key [4] - Building AI to accelerate production is challenging due to reliability requirements and the need for multi-agent orchestration [5][6][7] - Resolve AI focuses on using AI agents to troubleshoot alerts and incidents, acting as an AI site reliability engineer [11] - The company's AI agents can replace significant amounts of work, offering value exceeding coding agents [10] Resolve AI's Business and Technology - Resolve AI was founded to address the problem of increasing data and work for humans caused by existing observability tools [9] - Resolve AI's agents utilize human tools and understand production systems from code to backend databases [11] - Large enterprises are adopting Resolve AI's product in production with success, using it for "vibe debugging" beyond incidents and alerts [12] - Resolve AI differentiates itself by understanding the entire production system, not just code, and extracting knowledge unique to each company [13] Talent Acquisition and Future Vision - Resolve AI competes with major AI labs like Meta, OpenAI, and Anthropic for AI engineering talent [14] - Resolve AI attracts talent by offering the opportunity to have a significant impact on the company and the enterprise software engineering landscape [16] - The future of production engineering involves humans operating at a higher level of abstraction, with agents handling much of the underlying work [17]

AI发展现状 - AI技术目前擅长的领域并非软件工程，而是作为辅助工具，由软件工程师监督AI的工作 [1] - 软件工程师的角色正在转变为监督AI，通过输入指令来指导AI完成任务，而非直接编写代码 [1][2] - 行业普遍认为AI并非万能，在某些特定测试中表现不佳，技术发展尚未达到终点 [3][4] 行业趋势 - 行业内存在对AI价值的过度炒作和高估现象，需要警惕 [2] - 行业不应忽视AI的持续发展，回到2022年的状态是不现实的 [4]

AI Coding Evolution - 软件开发正经历从终端到图形界面，再到AI辅助的快速演变 [1][2][3][4] - Cursor 旨在通过AI 自动化编码流程，重点在于模型和人机交互 [46] - Cursor 的目标是让工程师更专注于解决难题、设计系统和创造有价值的产品 [47][49] Context Engineering & Coding Agents - Context Engineering 关注于为模型提供高质量和有针对性的上下文信息，而非仅仅依赖 Prompt 技巧 [16][17] - Semantic Search 通过自动索引代码库并创建嵌入，提升代码搜索的准确性和效率 [19][20] - Semantic Search 将计算密集型任务转移到离线索引阶段，从而在运行时获得更快、更经济的响应 [22] - Cursor 发现用户更倾向于使用 GP 和 Semantic Search 相结合的方式，以获得最佳效果 [22] Cursor's Products & Features - Tab 功能每天处理超过 4 亿次请求，通过在线强化学习优化代码建议 [7] - Cursor 正在探索多种 Coding Agents 的管理界面，包括并行运行和模型竞争 [38][39][42][43] - Cursor 正在探索为 Agent 提供计算机使用权限，以便运行代码、测试并验证其正确性 [44] - Cursor 允许用户通过自定义命令和规则，共享 Prompt 和上下文信息，实现团队协作 [32][33]

Software Development Efficiency - The primary goal is to accelerate the deployment of software to production [1] - The industry observes that deploying a prototype to production often takes longer than expected due to unforeseen issues [1] - A significant improvement would be achieved if deploying to production becomes as simple as creating the initial prototype [1] Agentic AI Impact - Agentic AI and software engineering aim to improve the speed of software delivery [1] Measurement of Success - The key metric for success is the increased speed at which software can be shipped to a broad audience [1]