AI Adoption in Enterprises - Enterprises' adoption of AI is crucial for realizing AI's full potential and impact [2] - Enterprises initially experiment with OpenAI and Anthropic models, often deploying them on Azure or AWS for security and privacy [7] - In 2023, enterprises were "toying around" with AI, but by 2024, 40-50% had production use cases built on closed models [9][10] Challenges with Closed Models - Vendor lock-in is not a primary concern for enterprises due to the increasing number of interoperable models [12][13] - Ballooning costs, especially with agentic use cases involving potentially 50 inference calls per user action, are becoming a significant concern [20] - Enterprises are seeking differentiation at the AI level, not just at the workflow or application level, leading them to consider in-house solutions [21] Reasons for Open Source Model Adoption - Frontier models may not be the right tool for specific use cases, such as medical document extraction, where enterprises can leverage their labeled data to build better models [16][17] - Generic API-based models may not suffice for tasks requiring low latency, such as AI voices or AI phone calls [18] - Enterprises aim to reduce costs and improve unit economics by running models themselves and controlling pricing [20][21] Inference Infrastructure Challenges - Optimizing models for latency requires both model-level and infrastructure-level optimizations, such as speculative decoding techniques like Eagle 3 [23][24][25][26] - Guaranteeing high availability (four nines) for mission-critical inference requires robust infrastructure to handle hardware failures and VLM crashes [27][28] - Scaling up quickly to handle traffic bursts is challenging, with some enterprises experiencing delays of up to eight minutes to bring up a new replica of a model [29]

You’d never let a swarm of fresh interns ship to prod on day one—same deal with AI agents. Mentoring the Machine dives into how acting like a tech lead (not just a user) turns those bots into real leverage. In this talk, Eric will deliver practical advice for working with AI agents in the SDLC. He'll also preview how effective use of AI agents changes the calculus of software engineering at both a micro and macro level. ---related links--- ...

Agentic Development Landscape - The adoption of agentic technology is rapidly increasing, with a 254% increase in companies self-identifying as agentic in the last three years based on Y Combinator data [5] - Agentic systems are complex, and while initial prototypes may achieve around 70% accuracy, reaching perfection is difficult due to the long tail of complex scenarios [6][7] - The industry defines an agent as an entity that can reason, act, communicate, and adapt to solve tasks, viewing the foundation model as a base for adding components to enhance performance [8] - The industry emphasizes that agency should not be the ultimate goal but a tool to solve problems, ensuring that increased agency maintains a high level of effectiveness [9][11][12] Tool Use and Orchestration - Exposing tools and functionalities to language models enables agents to invoke functions via APIs, but requires careful consideration of which functionalities to expose [14] - The industry advises against a one-to-one mapping between APIs and tools, recommending grouping tools logically to reduce semantic collision and improve accuracy [17][18] - Simple workflow patterns, such as single chains, are recommended for orchestration to improve measurability, reduce costs, and enhance reliability [19][20] - For complex scenarios, the industry suggests considering a move to more agentic patterns and potentially fine-tuning the model [22][23] Multi-Agent Systems and Evaluation - Multi-agent systems can help scale the number of tools by breaking them into semantically similar groups and routing tasks to appropriate agents [24][25] - The industry recommends investing more in evaluation to address the numerous hyperparameters involved in building agentic systems [27][28] - AI architects and engineers should take ownership of defining the inputs and outputs of agents to accelerate team progress [29][30] - Tools like Intel Agent, Microsoft's Pirate, and Label Studio can aid in generating synthetic inputs, red teaming agents, and building evaluation sets [33][34][35] Observability and Common Pitfalls - The industry emphasizes the importance of observability using tools like OpenTelemetry to understand failure modes and improve systems [38] - Common pitfalls include insufficient evaluation, inadequate tool descriptions, semantic overlap between tools, and excessive complexity [39][40] - The industry stresses the importance of designing for safety at every layer of agentic systems, including building tripwires and detectors [41][42]

[Music] the number of agents in the world I believe won't just match number of humans but they will basically be the number of humans to the power of n and what is the power of n I have really no idea but we can see already today that we are using multiple of them more are spinning up more and so it will be a very very large number and this isn't just hypothetical I mean we don't have a lot of data around this I actually researched a lot and try to find what we have but the some key elements that we do have ...

[Music] Thanks Alex for the introduction. That was a very impressive LLM generated summary of me. Uh I've never heard it before but uh nice.Um so um today I'm going to talk to you about something that has been keeping me up at night. Uh probably some of you too. So how to build enterprise aware agents.How to bring the brilliance of AI into the messy complex realities of uh how your business operated. So let's jump straight to the hottest question of the month for AI builders. Uh should I build workflows or ...

As AI continues to transform industries, companies are faced with the critical challenge of effectively monetizing AI-driven products in a way that captures value, ensures customer adoption, and scales revenue sustainably. Unlike traditional SaaS models, AI-powered products have unique complexities - such as fluctuating usage patterns, variable compute costs, and evolving customer demands, making conventional pricing strategies unhelpful to the growth of an AI product-led startup. In this session, Alvaro Mo ...

Productivity Impact of AI - AI adoption shows an average developer productivity boost of approximately 20% [1] - AI's impact on developer productivity varies significantly across teams, with some experiencing decreased productivity [1] Factors Influencing AI Adoption Success - Company types, industries, and tech stacks play a crucial role in determining the extent of productivity gains from AI [1] - Data-driven evidence is essential for building a successful AI strategy tailored to specific contexts [1] Study Details - The study analyzed real-world productivity data from nearly 100,000 developers across hundreds of companies [1] - The research was conducted at Stanford University [1]

AI Market Growth & Trends - AI infrastructure spending has reached $0.5 trillion, yet many companies are limited to basic chatbots and code generation [2] - Anthropic's annualized revenue has grown rapidly, 3xing in 5 months, reaching $3 billion by the end of May [3] - OpenAI is projected to reach $12 billion in revenue by the end of 2025, a 3x increase from the previous year, driven by enterprise AI spending [4] - Cost per token for AI inference dropped dramatically by 99.7% from 2022 to 2024 [33] - Google searches for "AI agents" increased 11x in the last 16 months [34] Retool's Agentic AI Solution - Retool is breaking into Agentic AI with the release of Retool Agents, enabling enterprises to build agents with guardrails that integrate into production systems [2] - Retool customers have automated over 100 million hours of work, freeing up human potential [31] - Retool's cheapest agent is priced at $3 per hour [33] Agent Development Strategies - Companies have four options for agent development: building from scratch, using a framework like Lang graph, using an agent platform like Retool Agents, or using verticalized agents [16][17][18][19] - The decision to build or buy agents depends on whether it's part of the core product, involves regulated data, or is a commodity workflow needed quickly [21] - When considering a managed platform, evaluate the breadth of connectors, built-in permissioning, compliance, audit trails, and observability [22][23] Enterprise Considerations for AI Agents - Enterprises need to consider single sign-on, role-based access control, secure integration with external services, audit logs, compliance, and internationalization when deploying AI agents [13][14] - Risks of using AI-generated code in production include hallucinations, unpredictable results, security vulnerabilities, and cost overruns [15]

[Music] Hi, I'm Jaspit. I'm a senior staff engineer in it. I work on Genifi for Turboax.And today we'll be talking about how we use LLMs at Inuit to well help you understand your taxes better. So I think uh to just to understand the scale right uh into Turboax successfully processed 44 million tax returns for tax year 23 and that's really the scale we're going for. We want everybody to be have high confidence in how their taxes are filed and understand them that they are getting the best deductions uh that ...

Strategy - AI first 意味着从在产品中添加 AI 功能发展到通过 AI 视角重新思考如何规划、构建和交付价值 [4] - AI first 公司需要像初创公司一样的好奇心和敏捷性，同时具备企业般的纪律性，两者并行 [12] - 公司需要平衡当前客户需求和对未来 AI 投资之间的关系，避免过度关注一方而落后 [11] - 规划方式需要拥抱不确定性，学习和发现塑造前进的道路，目的地本身也会随着对可能性的了解而演变 [13] Ways of Working - 需要将发现过程视为可重复的、有意的过程，在规划周期中构建用于实验、黑客马拉松和学习的时间 [19][20] - 将流程视为产品，根据结果评估其有效性，如果流程不能提高方向的清晰度、帮助团队或加速决策，则需要迭代或完全删除 [23] - 从速度转向智能速度，意味着培养有目的地快速行动的能力，在清晰、动力和适应性中工作 [25] People - 成为 AI first 公司主要是一种文化转型，需要重新思考在 AI 时代优秀人才的定义，不仅在 AI 团队中，而且在整个公司中 [26][27] - 投资于 T 型人才，即拥有深厚专业知识，同时可以扩展宽度、快速原型设计、跨部门流畅协作并实现端到端系统的人才 [29] - 需要在整个组织内建立 AI 流利度，让每个团队都感到有能力理解 AI，并有足够的信心使用 AI 进行构建 [33][34]