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All digests tagged Build Automation
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Learn how to secure AI agents against the OWASP Top 10 risks. This talk provides practical, layered security models for mitigating goal hijacking, tool misuse, and cross-agent privilege escalation.
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Building self-improving agents requires understanding three parts: Model, Harness, and Context. The foundation for improvement is robust observability via Traces and Evals.
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Learn how to build any native Mac App using BAMA, a free set of Claude Code skills. This process guides AI agents through planning, design, and shipping, creating optimized Swift/SwiftUI applications.
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The next leap in agentic engineering is moving from Human In the Loop to Out the Loop. This requires building durable, proactive agents in isolated sandboxes, mastering the Core Four (Context, Model, Prompt, Tools), and building the system that builds the system.
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Building an AI code review agent requires more than a simple sandbox setup. Key fixes include model inversion, deep codebase context, autonomous testing, and cost-efficient model routing.
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When public benchmarks are unavailable, the solution is to reverse the evaluation process: start with the known answer and generate the input. This shifts AI agent testing from slow expert review to automated, scalable code checks.
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Discover how to improve browser agents without changing model weights using the AutoBrowse loop. This system pairs a Doer agent with a Teacher agent to generate reusable 'skill' files, dramatically increasing reliability and reducing operational costs on complex websites.
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Managed Deep Agents simplifies deploying mission-critical AI agents. It bundles the Deep Agents harness with managed LangSmith infrastructure, allowing developers to define, test, and deploy agents using the mda CLI.
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Bigger context windows won't save your AI agents. Learn why models lose information in the middle and master the four strategies of Context Engineering: Externalize, Select, Compress, and Isolate.
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Learn how to build a self-improving company by turning agent failures into reusable skills. We discuss using MCP as the distribution layer and creating an organizational flywheel that captures hard-won knowledge.
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Entire is a new developer platform built to solve the 'missing middle' of the SDLC in the agent era. It captures context, decisions, and evaluations from intent to production, reimagining the PR into 'Trails' and integrating programmable 'Runners' and 'Gates.'
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The Gemini Interactions API and Managed Agents enable robust, stateful agentic workflows. Key features include persistent context via `interaction ID` and secure, remote sandboxes using `environment ID` for complex, multi-modal pipelines.
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Periodic Labs is applying AI to physical science, creating a closed-loop system that links computational prediction, robotic synthesis, and experimental verification to accelerate materials discovery.
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Learn how to manage the exploding costs of AI agents. Experts discuss cache-aware routing, open-weight models, and why only $18 of every $100 spent on AI actually ships to users.
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Jev by TypeSafe is a revolutionary tool for agentic engineering. It provides intelligent, JSON-programmable decision-making, acting as a cost-effective 'third primitive' to enhance agent safety, context management, and file analysis at scale.
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AI agents Codex and Claude Code raced in an Optimizer Speedrun, beating human records for training GPT-2. However, the key finding is that they combined existing ideas, not novel optimizers, highlighting the need for structured discovery loops.
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Learn how LinkedIn scaled AI agents to thousands of tools and playbooks using a Search/Get Schema/Execute meta-tool layer, enabling reliable, context-aware automation.
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TensorRT Model Connect simplifies the path from open-source models to deployable applications. It provides a consistent workflow to convert models into optimized bundles, supporting everything from LLMs to video generation on multi-device setups.
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Aspire AppHost simplifies polyglot development by providing a unified orchestration layer for mixed-stack projects. It automates service wiring, manages configuration via environment variables, and offers centralized observability across languages.
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The Agentic Operating Level is a framework that maps where engineers should focus attention, balancing leverage/speed (high) against control/understanding (low). Higher is not always better.
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