本文来自PRO会员通讯内容，文末关注「机器之心PRO会员」，查看更多专题解读。 AI Agent 进入生产环境后，业界关注的重点正从生成转向执行。随着长程任务中的上下文挤压、工具开销和业务语境缺口持续暴露，单一的 Context Engineering 已难以支撑 Agent 的稳定运行，围绕执行环境、约束机制和反馈回路展开设计的 Harness Engineering 因而受到更多关注。 目录 01. Agent 的稳定性问题还是得靠 Harness 来补？ Harness Engineering 将是 Context Engineering 之后的新范式？... 02 . 为什么 Context Engineering 还远远不够？ Andrej Karpathy 力挺的 Context Engineering 现在也不够用了？LLM 性能提升的关键不在于输入更多的 token？... 3、自 2025 年 12 月开始，AI 社区的 Harness Engineering 的讨论开始逐步升温，并将其视为 Prompt Engineering、Context Engineering 之后，Agent 工程 ...

Core Insights - The article discusses the evolution of AI development practices from Prompt Engineering to Context Engineering, and now to Harness Engineering, emphasizing the importance of the environment in which AI agents operate [4][40][41] Group 1: Evolution of Engineering Practices - In 2023, Prompt Engineering was at its peak, focusing on crafting effective prompts for AI to deliver results [9] - By mid-2025, Context Engineering emerged, shifting the focus to designing dynamic systems that provide the necessary context for AI tasks [9][10] - As of February 2026, Harness Engineering was introduced, highlighting that the environment in which AI agents operate is crucial for their performance [11][12][13] Group 2: OpenAI's Experiment and Findings - OpenAI conducted an experiment with a team of engineers who delivered over 1 million lines of code without writing any human code, relying entirely on Codex Agent [15] - The experiment revealed that the most significant challenges lie in designing the environment, feedback loops, and control systems for AI agents [22][42] - The team learned that a well-structured documentation system is essential, evolving from a single large document to a more organized directory structure [17][18] Group 3: Framework of Harness Engineering - Birgitta Böckeler outlined a three-dimensional framework for Harness Engineering, which includes Context Engineering, Architectural Constraints, and Entropy Management [24][25][26] - Context Engineering ensures that agents receive the right information at the right time, while Architectural Constraints enforce boundaries through automated mechanisms [24][25] - Entropy Management addresses the degradation of the system over time, ensuring that the harness remains effective and does not become outdated [26] Group 4: Industry Adoption and Examples - Companies like Stripe and LangChain are implementing Harness Engineering principles, with Stripe's Minions system merging over 1,300 AI-generated pull requests weekly [28][29] - LangChain demonstrated a significant performance improvement in its coding agent by optimizing the harness without changing the underlying model [29][30] - The concept of Harness Engineering is being internalized by tool vendors, with MCP (Model Control Protocol) becoming a standard for agent tool access [31] Group 5: Future Directions for Engineers - The core responsibilities of engineers are shifting from writing code to designing environments that ensure reliable operation of AI agents [33] - Engineers are now focused on building documentation systems, defining business intents in machine-readable formats, and creating automated validation mechanisms [33][34] - The industry is recognizing the need for a deeper understanding of system design over mere coding speed, leading to a re-evaluation of team structures and roles [35][36]

Summary of Elastic (NYSE:ESTC) 2026 Conference Call Company Overview - **Company**: Elastic (NYSE:ESTC) - **Event**: Morgan Stanley TMT Conference - **Date**: March 02, 2026 - **Speakers**: CEO Ash Kulkarni, CFO Navam Welihinda Key Points Industry Context - The current market is focused on the impact of AI on software companies, with investors assessing which companies will remain durable in the AI era [4][10] Core Value Proposition - Elastic positions itself as a data platform that provides context to large language models (LLMs), emphasizing the need to bring models to data rather than moving data to models [5][9] - The company highlights its ability to deliver specific data relevance and context for various AI use cases [9] Financial Performance - **Sales-led subscription revenue growth**: Accelerated to 19% from 17% in the previous quarter [10] - **Operating income**: Remains strong, with a record number of million-dollar deals reported [12] - **AI adoption**: Approximately 25% of the 100K customer cohort is now using Elastic for AI applications [14] Market Trends - There is a growing interest in self-managed environments, particularly in regulated industries where data sensitivity is paramount [14][16] - The demand for running technologies in sovereign environments is increasing in Europe [16] Competitive Advantage - Elastic's ability to offer a comprehensive platform that can be deployed both in cloud and self-managed environments is seen as a significant advantage over competitors [17] - The company emphasizes the importance of its data store and context accuracy as its defensible moat against competitors [34] AI Integration and Growth - Elastic is experiencing increased consumption from AI workloads, with a quantified difference of approximately 6% in consumption between AI users and non-users [51][53] - The company anticipates that AI will serve as a tailwind for growth, with midterm targets set to exceed 20% sales-led subscription revenue growth by fiscal 2029 [57][58] Observability and Security - The observability business is growing, particularly in metrics, which has historically been a weaker area for Elastic [75][80] - The company is developing specialized backend stores to improve performance in observability, expected to launch mid-year [80] Capital Allocation Strategy - Elastic maintains a disciplined approach to stock-based compensation while investing in sales and marketing capacity [92][93] - The company has allocated over 50% of its capital for share repurchases as part of its strategy to return value to shareholders [97] Future Outlook - Elastic expects steady growth rather than a sudden inflection, focusing on long-term market share and efficiency improvements [88][89] - The company is committed to evolving its platform to meet the changing demands of AI and data management [46][67] Additional Insights - Context engineering is defined as the processes and capabilities needed to provide LLMs with accurate context, which Elastic aims to excel in [67][68] - The shift from human to AI interfaces is anticipated to change how data platforms operate, with a focus on APIs over traditional UIs [46][68]

Financial Data and Key Metrics Changes - Total revenue for Q3 was $450 million, representing an 18% growth year-over-year and 16% growth on a constant currency basis [24][25] - Sales-led subscription revenue grew to $376 million, an increase of 21% as reported and 19% on a constant currency basis [25] - Current remaining performance obligations (CRPO) reached approximately $1.06 billion, growing 19% as reported and 15% on a constant currency basis [25][26] - Non-GAAP operating margin was 18.6%, with subscription gross margins at 82% and total gross margins at 78% [28] Business Line Data and Key Metrics Changes - Sales-led subscription revenue growth was driven by both Self-Managed and cloud offerings, with strong consumption trends [25][27] - The number of customers with an annual contract value (ACV) over $100,000 increased to over 1,660, growing 14% [26] - 28% of the greater than $100,000 ACV cohort now utilizes Elastic for AI, indicating strong demand for AI capabilities [27] Market Data and Key Metrics Changes - The company saw balanced deal momentum across all geographies, with multi-year commitments indicating strong customer confidence in the Elastic platform [26] - The demand for Elastic's solutions is being driven by the need for organizations to manage increasing data volumes and leverage AI for innovation and efficiency [10][27] Company Strategy and Development Direction - The company is focused on becoming the essential infrastructure for AI-powered businesses, emphasizing the importance of context in AI applications [6][23] - Elastic aims to bridge the gap between LLMs and proprietary data, enhancing AI adoption among its customer base [13][15] - The introduction of new features like Agent Builder and Elastic Workflows aims to enhance the platform's capabilities for building intelligent applications [19][22] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in the ongoing demand for Elastic's platform, particularly in the context of AI and data management [24][29] - The company anticipates continued growth in sales-led subscription revenue and adjusted free cash flow, with a focus on maintaining strong customer commitments [34][35] - The outlook for Q4 includes expected total revenue in the range of $445 million to $447 million, representing 15% growth at the midpoint [31][32] Other Important Information - The company has made significant progress on its $500 million share repurchase program, returning approximately $186 million to shareholders in Q3 [30] - The partnership with NVIDIA aims to enhance AI application deployment without straining IT infrastructure [18] Q&A Session Summary Question: Potential for growth acceleration among AI-native customers - Management noted that as more customers reach the $100,000 mark, there is potential for accelerated growth beyond the current 5% average [38][40] Question: Importance of context in AI applications - Management emphasized the need for a comprehensive data platform that can handle various data types and provide accurate context for AI applications [42][43] Question: Performance of Self-Managed versus cloud customers - Management highlighted the strength in Self-Managed business, particularly as customers prefer to keep sensitive data within their control [48][49] Question: Impact of AI on internal operations - Management shared that AI has significantly improved efficiency and reduced headcount needs in support operations [60][62] Question: Traction from recent CISA win - Management confirmed that the CISA win has led to additional agencies coming on board, indicating strong future growth potential [68][69]

Context engineering is the new bottleneck!Most of the work in building production agents today has nothing to do with the model.Instead, it's:→ Setting up PostgreSQL for conversation history→ Wiring S3 for file storage→ Writing custom logic to compress long contexts→ Building format converters between OpenAI and Anthropic→ Stitching together monitoring from scratchAnd you do this separately for every agent you build.A smarter approach is now actually implemented in Acontext, which is an open-source context ...

Core Insights - The article emphasizes the transition from traditional chatbots to Long-horizon Agents, which are capable of performing complex tasks over extended periods, thus redefining the value proposition of AI products from speed of response to quality of output [3][8][10]. Group 1: Long-horizon Agents - Long-horizon Agents are designed to operate autonomously over longer time spans, allowing for multi-step decision-making and iterative processes, which are essential for tasks like research reports and code reviews [16][20]. - The emergence of Long-horizon Agents marks a significant shift in AI capabilities, moving from simple question-answer interactions to producing high-quality deliverables that require time and context [7][8][11]. Group 2: Harness Concept - The concept of "Harness" is introduced as a runtime environment that includes best practices for building Long-horizon Agents, distinguishing it from traditional frameworks by providing integrated tools and capabilities [11][23]. - Harnesses facilitate the development of agents that can autonomously manage tasks, including planning, memory management, and sub-task coordination, thus enhancing their effectiveness [11][23][24]. Group 3: Evolution of AI Agents - The evolution of AI Agents is categorized into three phases: simple prompting and chaining, cognitive architecture, and the current Long-horizon Agent era, which began around mid-2025 [26][30][31]. - The transition to Long-horizon Agents is characterized by improved model capabilities and a focus on context engineering, which is crucial for optimizing agent performance [29][34]. Group 4: Applications and Future Directions - Long-horizon Agents are particularly effective in generating initial drafts for various applications, such as coding, research, and customer support, where they can significantly reduce the workload for human users [20][22]. - The future of AI development is expected to focus on enhancing context engineering, memory management, and the integration of file systems, which are seen as critical components for the success of Long-horizon Agents [34][42][46].

Financial Data and Key Metrics Changes - Revenue for Q4 2025 was $5.3 billion, representing a 3.8% year-over-year growth in constant currency, all organic [30][41] - Full-year revenue reached $21.1 billion, growing 6.4% in constant currency, surpassing the $20 billion mark [6][30] - Adjusted Operating Margin improved to 16%, up 30 basis points year-over-year, with a full-year adjusted operating margin of 15.8%, exceeding guidance [6][39] - Adjusted diluted EPS for Q4 was $1.35, up 12% year-over-year, leading to a full-year EPS of $5.28, an 11% increase from the prior year [41][45] Business Line Data and Key Metrics Changes - Financial Services segment led growth with a 9% year-over-year increase in constant currency for Q4 and 7% for the full year, marking the highest annual level since 2016 [5][32] - Health Sciences segment grew at 6%, outperforming the company average, driven by strong demand for administrative and patient care solutions [86] - BPO business experienced a 9% year-over-year growth, indicating strong demand for AI-enabled operations [80][82] Market Data and Key Metrics Changes - North America was the standout region with over 4% year-over-year growth in constant currency, primarily driven by Financial Services and healthcare [37] - Europe grew 2% in constant currency, with healthy growth in Financial Services and Life Sciences [38] - The Rest of World segment grew in line with the total company, driven by the Middle East [38] Company Strategy and Development Direction - The company aims to bridge the AI velocity gap, focusing on transforming AI technology into measurable returns for clients [11] - Cognizant's strategy includes a three-vector approach to capture demand, emphasizing AI-led productivity and the development of new agentic software [12][13] - The company plans to maintain its position in the industry's winner's circle by continuing to innovate and expand its AI capabilities [29] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in sustaining growth in 2026, supported by a strong pipeline of large deals and a focus on AI integration [31][67] - The operating environment remains complex, but the company views it as an opportunity to capture wallet share in large deals and help clients reinvest savings into innovation [32] - Management highlighted the importance of maintaining productivity and cost discipline to navigate market challenges [59] Other Important Information - The company returned $2 billion to shareholders through dividends and share repurchases in 2025 [10][41] - The acquisition of 3Cloud was completed, adding over 1,200 Azure specialists to enhance capabilities in AI and cloud services [10] - The company is actively evaluating potential strategic acquisitions and a possible secondary listing in India [46] Q&A Session Summary Question: Insights on AI's impact on revenue and package implementation - Management noted that AI provides opportunities for increased total addressable spend and emphasized the need for reimagining processes to integrate new technologies effectively [50][54] Question: Gross margin dynamics for 2026 - Management indicated that Q4's gross margin decline was primarily due to higher variable compensation, but they expect to maintain productivity and manage costs effectively in 2026 [56][59] Question: Confidence in large deal growth for 2026 - Management expressed optimism about the strong pipeline of large deals and the potential for timely deal ramps, expecting acceleration in growth during the year [68][69] Question: Risks and opportunities of fixed-price contracts - Management explained that fixed-price contracts place delivery risk on the service provider, but they have a robust process to monitor performance and ensure delivery aligns with expectations [75][76] Question: Durability of BPO growth - Management highlighted that the BPO business has shown consistent growth and sees a long-term tailwind due to the need for ongoing transformation and maintenance of AI-enabled processes [82][80]

Financial Data and Key Metrics Changes - Revenue for Q4 2025 was $5.3 billion, representing a 3.8% year-over-year growth in constant currency, all organic [25][30] - Full-year revenue reached $21.1 billion, growing 6.4% in constant currency, surpassing the $20 billion mark [6][25] - Adjusted Operating Margin improved to 16%, up 30 basis points year-over-year, with a full-year adjusted operating margin of 15.8%, exceeding guidance by 50 basis points [6][30] - Adjusted diluted EPS for Q4 was $1.35, up 12% year-over-year, leading to a full-year EPS of $5.28, an 11% increase from the prior year [31][34] Business Line Data and Key Metrics Changes - Financial Services segment led growth with a 9% year-over-year increase in constant currency for Q4 and 7% for the full year, marking the highest annual level since 2016 [5][25] - Health Sciences segment grew at over 6%, significantly above the company average, driven by strong performance in administrative and clinical processes [78] - Digital Engineering practices saw an 8% year-over-year growth in Q4, supported by proprietary platforms [19] Market Data and Key Metrics Changes - North America was the standout region with over 4% year-over-year growth in constant currency, primarily driven by financial services and healthcare [28] - Europe experienced a 2% growth in constant currency, with healthy performance in financial services and life sciences [28] - The Rest of World segment grew in line with the total company, driven by the Middle East [28] Company Strategy and Development Direction - The company aims to bridge the AI velocity gap, focusing on transforming AI technology into measurable returns for clients [11][12] - Cognizant's strategy includes a three-vector approach to capture demand, emphasizing AI-led productivity and the development of new agentic software [12][13] - The company plans to maintain its position in the industry's winner's circle by 2027, having achieved this goal two years early [8][22] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in sustaining growth in 2026, supported by a strong pipeline of large deals and a focus on AI integration [24][58] - The operating environment remains complex, but management views it as an opportunity to capture wallet share in large deals and help clients reinvest savings into innovation [25][26] - The company anticipates a modest increase in defined benefit costs due to recent labor law changes in India, but this is not expected to materially impact the P&L [30] Other Important Information - The company returned $2 billion to shareholders through dividends and share repurchases in 2025 [10][32] - Cognizant completed the acquisition of 3Cloud, adding over 1,200 Azure specialists to enhance its capabilities in AI and application innovation [10][32] - The company has a healthy M&A pipeline and intends to maintain an active acquisition strategy aligned with its AI builder strategy [32][34] Q&A Session Summary Question: Insights on AI's impact on revenue and package implementation - Management sees AI as a net positive, increasing total addressable spend and creating opportunities for innovation and productivity [40][41] Question: Gross margin dynamics for 2026 - The decline in Q4 gross margin was primarily due to higher variable compensation, but management remains confident in maintaining margins through productivity measures [47][50] Question: Confidence in large deal growth for 2026 - Management expressed strong confidence in the pipeline for larger deals, expecting timely ramps and acceleration in growth throughout the year [55][58] Question: Risks and opportunities of fixed-price contracts - Management indicated that while delivery risk resides with the service provider, they have a robust process to monitor performance and maintain margins [66][67] Question: Durability of BPO growth - Management believes the BPO segment will continue to grow due to the need for ongoing transformation and maintenance of processes enabled by AI [71][74] Question: Health Sciences segment growth amidst regulatory pressures - Management is confident in the Health Sciences segment's growth, leveraging their platform to transform administrative processes and improve patient care [78][80]

Group 1 - The core assertion of the article is that AGI (Artificial General Intelligence) represents the ability to "figure things out," marking a shift from the era of "Talkers" to "Doers" by 2026, driven by Long Horizon Agents [1][2] - Long Horizon Agents are characterized by their ability to autonomously plan, operate over extended periods, and exhibit expert-level features, expanding their capabilities from specific verticals to complex tasks across various domains [1][2] - The article highlights that the value of Long Horizon Agents lies in their ability to produce high-quality drafts for complex tasks, with a focus on the need for opinionated software harnesses and file system permissions as standard features for all agents [1][2][3] Group 2 - Harrison Chase emphasizes that the recent advancements in models and the understanding of effective harnessing have led to the successful implementation of Long Horizon Agents, particularly in the coding domain, which is rapidly expanding to other fields [2][4] - The article discusses the importance of Scaffolding and Harness in the development of agents, where Scaffolding refers to auxiliary code structures that guide model outputs, while Harness encompasses the software environment that manages context and tool interactions [3][8] - The emergence of AI Site Reliability Engineers (AI SREs) is noted as a significant application of Long Horizon Agents, capable of handling long-duration tasks and generating comprehensive reports for human review [5][6] Group 3 - The article outlines the evolution of agent frameworks, transitioning from general frameworks to more opinionated harness architectures, with a focus on the integration of planning tools and file system interactions [8][10] - The concept of Deep Agents is introduced, which represents the next generation of autonomous agent architecture built on LangGraph, emphasizing the need for effective context management and compression techniques [9][12] - The discussion includes the challenges of context management in Long Horizon Agents, particularly the need for efficient compression strategies as task cycles extend [11][18] Group 4 - The article identifies the critical role of Memory in Long Horizon Agents, allowing them to self-improve and adapt over time, which is essential for maintaining performance in long-duration tasks [36][37] - The future interaction models for Long Horizon Agents are anticipated to combine asynchronous and synchronous modes, allowing for effective management and collaboration between agents and users [38][39] - The necessity for agents to have access to file systems is emphasized, as it enhances context management and operational capabilities, particularly in coding tasks [41][42]

Core Insights - The article asserts that AGI represents the ability to "figure things out," marking a shift from the era of "Talkers" to "Doers" in AI by 2026, driven by Long Horizon Agents [2] - Long Horizon Agents are characterized by their ability to autonomously plan, operate over extended periods, and exhibit expert-level features across complex tasks, expanding from coding to various domains [3][4] - The emergence of these agents is seen as a significant turning point, with the potential to revolutionize how complex tasks are approached and executed [3][21] Long Horizon Agents' Explosion - Long Horizon Agents are finally beginning to work effectively, with the core idea being to allow LLMs to operate in a loop and make autonomous decisions [4] - The ideal interaction with agents combines asynchronous management and synchronous collaboration, enhancing their utility in various applications [3][4] - The coding domain has seen the most rapid adoption of these agents, with examples like AutoGPT demonstrating their capabilities in executing complex multi-step tasks [4][5] Transition from General Framework to Harness Architecture - The distinction between models, frameworks, and harnesses is crucial, with harnesses being more opinionated and designed for specific tasks, while frameworks are more abstract [8][9] - The evolution of harness engineering is particularly advanced in coding companies, which have successfully integrated these concepts into their products [12][14] - The integration of file system permissions into agents is essential for effective context management and task execution [24] Future Interactions and Production Forms - Memory is identified as a critical component for self-improvement in agents, allowing them to retain and utilize past interactions to enhance performance [35] - The future of agent interaction is expected to blend asynchronous and synchronous modes, facilitating better user engagement and task management [36] - The necessity for agents to access file systems is emphasized, as it significantly enhances their operational capabilities [39]