As the amount of consumer facing AI products grows, the most forward leaning enterprises have created a new role: the AI Architect. These leaders are responsible for helping define, manage, and evolve their company's AI agent experiences over time. In this session, Clay Bavor (Cofounder of Sierra) will join Alessio Fanelli (co-host of Latent Space) in a fireside chat to share what it means to be an AI Architect, success stories from the market, and the future of the role. ---related links--- https://x.com/f ...

Healthcare Industry Challenges - 40% of hospitals operate at a negative margin due to broken revenue cycle processes [1] - Healthcare administration has increased 30-fold in the past three decades, while clinicians have barely doubled, indicating growing complexity [11][12] - 20% of the GDP is attributed to the healthcare system, with a large proportion being the administration of healthcare [9] Revenue Cycle Management (RCM) & AI Opportunity - Revenue cycle management (RCM) refers to the financial process of the patient's journey within the healthcare system [3] - AI has a big opportunity to shift resources from bureaucracy towards clinical care by addressing inefficiencies in RCM [16] - Ensemble Health Partners estimates a large amount of the cost associated with healthcare is related to friction in communication between payers, providers, and patients [14] Ensemble Health Partners' Approach - Ensemble Health Partners is an end-to-end RCM organization with 14,000 employees, providing a unique lens into inefficiencies throughout the entire process [2][3] - Ensemble Health Partners uses its EIQ platform to bring together multiple data formats within a single platform to address the challenges of unstructured data scattered across systems [33][34] - Ensemble Health Partners has seen a 40% reduction in time in the clinical appeal process by using GenAI [30] - Ensemble Health Partners aims to build a smarter, more coordinated system to reduce waste in the overall revenue cycle process [37]

AI Team Anatomy - Companies should recognize that technology is not always the limitation to success, but rather how technology is used [1] - Companies need to identify their bottlenecks, such as shipping features, acquiring/retaining users, monetization, scalability, and reliability, to prioritize hiring accordingly [3][4] - Companies should consider whether to trade their existing team with domain knowledge for AI researchers from top labs, weighing the value of domain expertise against specialized AI skills [1] Generalists vs Specialists - Companies should structure AI teams comprehensively, recognizing that success isn't tied to a single role [2] - Companies should prioritize building a comprehensive AI team with skills in model training, model serving, and business acumen, balancing budget constraints [7] - Companies should understand the trade-offs between hiring generalists and specialists, with generalists being adaptable and specialists pushing for extra performance [18][19] Upskilling and Hiring - Companies should focus on upskilling employees in building, domain expertise, and human interaction [19] - Companies should hire based on the need to hold context and act on context, ensuring accountability for AI systems [23][24][25] - Companies should verify trends and think from first principles when hiring, considering new grads, experienced professionals, and retraining opportunities [27]

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]

AI Agent Development & Management - AI agents require mentorship similar to interns to ensure effective deployment [1] - Treating AI agents as a tech lead would, rather than just a user, maximizes their leverage [1] - Effective use of AI agents impacts software engineering at both micro and macro levels [1] Software Development Lifecycle (SDLC) - The report previews how AI agents can change the calculus of software engineering [1] - Practical advice for working with AI agents in the SDLC will be provided [1]

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]

Agent Experience Definition and Importance - Agent experience is defined as how easily agents can access, understand, and operate within digital environments to achieve user-defined goals [5] - The industry believes agent experience is the only experience that matters because agents will be the largest user base [33] - The industry suggests that if a tool requires human intervention, it hasn't fully addressed agent needs [33] The Shift in Development Tools - 37% of the latest YC batch are building agents as their products, indicating a shift from co-pilots and legacy SAS companies [1] - The industry argues that tools built for humans are for the past, and the focus should be on building tools for agents [3] - The industry emphasizes the need to build tools that enable agents to operate autonomously [12][13] Key Components of Agent Experience - Seamless authentication is crucial; agents should be able to authenticate without exposing passwords [6][7] - Agent-readable documentation is essential, with standards like appending ".md" to URLs and using llm's.txt [8][9] - API-first design is critical, providing agents with machine-native interfaces to access functionality efficiently [10] Daytona's Approach to Agent Native Runtime - Daytona aims to provide agents with a computing environment similar to a laptop for humans [19] - Daytona's initial focus was on speed, achieving a spin-up time of 27 milliseconds for agent tools [21] - Daytona preloads environments with headless tools like file explorers, Git clients, and LSP to help agents do things faster [22] Daytona's Features for Autonomous Agents - Daytona offers a declarative image builder, allowing agents to create and launch new sandboxes with custom dependencies [27] - Daytona provides Daytona volumes, enabling agents to efficiently share large datasets across multiple machines [29] - Daytona supports parallel execution, allowing agents to fork machines and explore multiple options simultaneously [31]

[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]