Agentic Platform Engineering Implementation - Outshift by Cisco is redefining platform engineering with agentic platform engineering, aiming to automate workflows and allow platform engineers to focus on high-value work [1][2] - The company built Jarvis, a genai-powered multi-agentic system, to automate tasks and improve efficiency [2] - Langraph, combined with Langsmith's observability tools, enables debugging agentic applications and improving reasoning capabilities at scale [6] Efficiency Improvement - Implementing Jarvis allows developers to self-service through genai-powered automation, eliminating manual toil [4] - CI/CD pipeline setup time reduced from a week to less than an hour [7] - Resource provisioning time (e.g., S3 buckets, EC2 instances, access keys) reduced from half a day to nearly instantaneous [7] - The company has eliminated unnecessary back and forth between developers and SREs [7] Workflow Transformation - The company shifted from traditional automation to agentic reasoning-based workflows by adopting Langraph [5] - Developers interact with Jarvis for platform-related questions and configurations, retrieving information autonomously [8] - The new system allows the company to handle a higher volume of requests with the same team while reducing burnout [8] Technology Evaluation - Langraph's tight integration with Langsmith, especially for debugging and evaluations, is a significant advantage [9] - The company found Langraph to be superior compared to other agentic solutions or custom-built alternatives [9]

Company Overview - Box is a B2B company operating as an unstructured data platform, serving large enterprises including Fortune 500 companies [1][2] - Box has over 115,000 companies as customers, tens of millions of users, and manages over 1 exabyte of data [2] - Box is often the first AI deployed within large enterprises due to existing trust relationships [3] Data Extraction Evolution - Box initially used a straightforward architecture for data extraction involving pre-processing, OCR, and large language models [8] - The initial AI deployment processed 10 million pages, but encountered challenges with complex documents, OCR accuracy, language variations, and the need for confidence scores [9][10][11] - The company experienced a "trough of disillusionment" as the initial AI solution proved insufficient for diverse customer needs [12] Agentic Approach Implementation - Box re-architected its data extraction process using a multi-agent approach, separating problems into sub-agents [12] - The agentic system intelligently groups related fields, dynamically determines data extraction methods, and incorporates a quality feedback loop for continuous improvement [13] - This approach allows for easier updates and specialization, enabling the company to quickly adapt to new document types and customer requirements [13] Engineering and Customer Impact - Building agentic systems helps engineers think about AI and agentic workflows, leading to better understanding of customer needs [13] - This approach facilitates the development of tools that integrate with customer-built agents, enhancing the overall ecosystem [13] - The company advises building agentic systems early when developing intelligent features [14]

AI Developer Tool Strategy at Uber - Uber's AI developer tool strategy focuses on products that directly improve developer workflow, such as writing tests and reviewing code [6] - The strategy emphasizes building crosscutting primitives, foundational AI technologies applicable across multiple solutions [7] - Intentional tech transfer is a cornerstone, identifying and spinning out reusable components to reduce barriers for future problem-solving [8][9] Key Products and Their Impact - Validator, an IDE experience, flags best practices violations and security issues, resulting in thousands of fixed interactions daily [11][16] - Autocover, a test generation tool, leverages domain expert agents and has increased developer platform coverage by approximately 10%, saving an estimated 21,000 developer hours [17][27][28] - Uber has built an internal custom GPT store where you can build chatbots that are steeped in Uber knowledge [29] Technical Learnings - Building super capable domain expert agents yields outsized results due to better context utilization and reduced hallucination [34] - Composing agents with deterministic sub-agents, like the lint agent in Validator, ensures reliable output [36] - Scaling development efforts is achieved by creating and reusing agents across multiple applications, such as the build system agent [37] Strategic Learnings - Encapsulation through well-defined abstractions like Langraph boosts collaboration and allows for horizontal scaling of development [39] - Graphs model interactions effectively, mirroring developer workflows and improving both AI and non-AI experiences [41]