Coding agents are transforming how software gets built, tested, and deployed, but engineering teams face a critical challenge: how to embrace this automation wave without sacrificing trust, control, or reliability. In this 80 minute workshop, you’ll go beyond toy demos and build production-minded AI agents using Dagger, the programmable delivery engine designed for real CI/CD and AI-native workflows. Whether you're debugging failures, triaging pull requests, generating tests, or shipping features, you'll le ...

[Music] Hi everybody, my name is Eno. I really appreciate that introduction. Um, and maybe I can start with a bit of background.Uh, I started working on LLMs about two and a half years ago. uh when uh GBT3.5% was coming out and it became increasingly clear that agentic systems were going to be possible with the help of LLMs. . At factory we believe that the way that we use agents in particular to build software is going to radically change the field of software development. We're transitioning from the era ...

Imbue's Focus and Sculptor's Purpose - Imbue is focused on creating more robust and useful AI agents, specifically software agents, with Sculptor as its main product [1] - Sculptor aims to bridge the gap between AI-generated code and production-ready code, addressing the challenges of using AI coding tools in established codebases [3] - The goal of Sculptor is to build user trust in AI-generated code by using another AI system to identify potential problems like race conditions or exposed API keys [7][8] Key Technical Decisions and Features of Sculptor - Sculptor emphasizes synchronous and immediate feedback on code changes to facilitate early problem detection and resolution [9][10] - Sculptor encourages users to learn existing solutions, plan before coding, write specs and docs, and adhere to strict style guides to prevent errors in AI-generated code [11][12][13][15][16][18] - Sculptor helps detect outdated code and documentation, highlights inconsistencies, and suggests style guide improvements to maintain code quality [17][18][19] Error Detection and Prevention Strategies in Sculptor - Sculptor integrates automated tools like linters to detect and automatically fix errors in AI-generated code [21][22] - Sculptor promotes writing tests, especially with AI assistance, to ensure code correctness and prevent unintended behavior changes [25][26][27] - Sculptor advocates for functional-style coding, happy and unhappy path unit tests, and integration tests to improve test effectiveness [28][29][30][33] - Sculptor utilizes LLMs to check for various issues, including style guide violations, missing specs, and unimplemented features, allowing for custom best practices [38] Future of AI-Assisted Development - Imbue is interested in integrating other developer tools for debugging, logging, tracing, profiling, and automated quality assurance into Sculptor [42][44] - The company anticipates that improved contextual search systems and AI models will further enhance the development experience [43]

RT BSCN (@BSCNews)🚨 2025 DEEP DIVE: $PORT3 - With more than 5 million users and a unique approach to AI agents, @Port3Network is one you need to know about...https://t.co/CYCa85ND3o ...

New Anthropic research: Building and evaluating alignment auditing agents.We developed three AI agents to autonomously complete alignment auditing tasks.In testing, our agents successfully uncovered hidden goals, built safety evaluations, and surfaced concerning behaviors. https://t.co/HMQhMaA4v0 ...

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 agents are coming fast. But without their own L1, they’ll be locked in private silos.Youmio puts agents on-chain with transparent identity, memory, and provenance.Users stay in control. Developers get composable rails.It all starts here:https://t.co/cLTuR1t323 ...

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]

Core Functionality - LlamaIndex introduces FlowMaker, an experimental open-source visual agent builder enabling AI agent creation via drag-and-drop without coding [1] - FlowMaker automatically generates TypeScript code for visual flows [1] - The platform integrates with LlamaCloud indexes and tools [1] - It offers an interactive browser testing environment for real-time feedback [1] Key Features - FlowMaker features a visual drag-and-drop interface for no-code agent development [1] - It supports complex flow patterns with loops and conditional logic [1] Use Cases - FlowMaker facilitates basic agent creation by connecting user input nodes to language models [1] - It enables tool integration, demonstrated by a resume-searching agent using LlamaCloud indexes [1] - The platform allows implementing decision logic, conditional branching, and loop-back mechanisms for intelligent conversation routing [1] Feedback - LlamaIndex is actively seeking user feedback on FlowMaker [1]

Exclusive: Walmart built so many AI agents, things started to get confusing. Now the retail giant is looking to simplify. https://t.co/FxdgFZF1OC ...