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All digests tagged Continuous Integration (CI)
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CoreWeave Agent Lens delivers intelligent agent observability at enterprise scale, providing a single view of every trace to turn production issues into proven fixes.
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Trusting AI agents requires more than just code. WorkOS demonstrates how to build robust verification loops using CLI health checks, browser snapshots, and LLM judges. Learn to make your agents self-correct and prove their work is done.
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Evals are running tests on traces. They are essential because agents are non-deterministic, meaning the same prompt can give different answers. This process allows teams to measure reliability and prove that changes actually improve the agent.
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AI agents are accelerating commit volume, creating bottlenecks in CI and review queues designed for human speed. Solutions require systemic changes: incremental builds, caching, and robust governance.
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Learn about AutoResearch: an agent-driven loop that automates code proposals and evaluations to optimize ML models. We demonstrate Mini Parameter Golf, a scaled-down challenge for inspecting complex optimization processes.
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Perception Agents enable AI to understand visual screens, moving beyond APIs. The workflow uses Visual Annotation (deterministic JSON output) and Visual Verification (Gherkin files) to close the design feedback loop, accelerating development.
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The talk details building self-learning loops for AI agents, moving observability from human dashboards to programmatic interfaces. Key tools include Arise Signal and Arise Skills, enabling agents to detect, investigate, and self-correct production failures.
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AI attackers are faster than traditional pen tests. To keep up with the volume of insecure code generated by LLMs, security must shift to continuous, multi-agent offensive security running on every PR.
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Discover how VS Code moved from monthly to weekly releases by rebuilding its entire system around AI agents. Learn about improving CI/CD with TypeScript Go, automating issue triage from 51 billion daily events, and implementing self-correcting feedback loops.
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The Defender's Window highlights the urgent need for a 'Defense Factory' to close the gap between advanced AI capabilities and cyber defenses. Learn how GPT-6 Astra and CodeX automate vulnerability remediation.
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AI code generation creates a bottleneck in traditional review processes. The talk explores how rapid agent output strains human maintainers, arguing that review is about aligning mental models, not just quality control.
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Building a safe AI coach for mental health requires more than just LLMs; it demands an eval-driven playbook. SonderMind details its modular, clinically grounded architecture and how they integrate human expert judgment directly into their CI pipeline.
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Evaluating AI video requires moving beyond single-frame metrics like CLIP score. The solution involves using VLMs trained with Bradley-Terry loss on paired footage, integrating the judge as a regression gate in CI.
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Jason Liu details how AI agents, using 'Appshots' and custom skills, automate complex knowledge work—from managing Git PRs to running continuous project monitoring.
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Learn Loop Engineering: The art of building reliable, self-improving AI agents. Discover how Core, Verification, Event-Driven, and Self-Improvement loops transform non-deterministic models into production-ready systems.
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Learn how to build self-improving AI loops that automate complex tasks. We cover the Loop Contract, advanced triggers (Combo/Workflow), and multi-agent verification systems for CI.
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