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All digests tagged DevOps
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AI agents are solving the coding problem, but verification is the new bottleneck. Learn how Anthropic scaled to 30,000 merged changes daily and why multi-layered, zero-trust verification is mandatory for modern software factories.
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Monzo built an in-house AI coding agent that authors 10% of PRs. Success came not from the LLM, but from building a robust, composable platform with an MCP gateway and sandboxed orchestration.
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Deploying AI in major banks requires overcoming compliance hurdles. The speaker details three pillars: securing the build pipeline, ensuring disaster recovery, and preventing PII leakage using advanced techniques like agent-based CVE testing.
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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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Flywire's Orbit platform integrates LLMs into deterministic workflows using YAML definitions, ensuring AI-driven automation maintains strict control and compliance within existing CI/CD infrastructure.
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AI agents pose a 'lethal trifecta' threat: private data access, untrusted content exposure, and external communication. Secure adoption requires three guardrails: CDEs, a Model Proxy, and an Agent Firewall.
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If you think the speed of writing code is your problem, you have bigger problems. The true bottleneck in software is rarely the keyboard; it's usually Discovery, Review, or Deployment.
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The future of building complex products lies in agent-driven systems. We explore multi-agent frameworks like Da Vinci for automated QA, self-hosting inference, and how agent players are revolutionizing MMORPGs.
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Codex Cloud allows agents to run tasks in managed environments, eliminating the need for local infrastructure upkeep. The system supports private resource access using Tailscale and OIDC, enabling secure, scalable development.
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The Software Factory proposes an autonomous system of agents that converts input signals (like bug reports) into production code. The focus shifts from coding to governing agents, emphasizing model-agnostic principles and measuring outcomes like signal-to-production time.
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Scaling AI agents requires more than just tools. Success demands redesigning the entire workflow, remodeling roles, and building trust incrementally through automated code review and merging.
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Adam Cogan explores the shift from Copilot hype to agentic AI workflows. Learn how agents are reshaping the enterprise stack, improving security, and changing the developer's role from coder to prompt engineer.
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Learn to operate a 'fleet of agents' instead of babysitting single sessions. We cover Goals, Loops, Hooks, and Git worktrees to build fully autonomous, verifiable development workflows.
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DevOps requires using both IDEs and CLIs. The real challenge is 'context overload'—managing the friction across coding, testing, builds, and deployment pipelines.
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AI coding agents are accelerating development but are causing massive codebase decay. Dan Adler argues that large-scale code visibility and infrastructure are the true bottlenecks, introducing Agentic Batch Changes for cross-repo updates.
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Coding agents accelerate development but create complex troubleshooting challenges. This talk outlines 'Self-Driving Production,' a closed-loop system using causal AI to solve root cause analysis, moving beyond simple observability.
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OpenAI's 'dots' AI agents automate complex tasks, from booking policy-compliant travel to synthesizing user feedback and closing the development loop by linking fixes to PRs.
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The ROI in an AI software factory comes from the system surrounding the agent—context, verification, and feedback loops—not the agent itself. Learn how to measure the full cost of a PR.
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Scaling development with AI requires more than just generating code. Learn how automated code review, Jev classifiers, and robust CI/CD guardrails are essential to manage the flood of AI-generated PRs and maintain system reliability.
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A quarter of PRs reviewed by Greptile are now AI-generated. The talk compares agent code (Codex, Claude, Devin) to human code on bug rates and review cycles, finding unique failure modes for each agent.
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