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All digests tagged Software Architecture
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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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Harness engineering is the infrastructure around LLMs, encompassing tools, context, guardrails, and telemetry. While LLMs introduce non-determinism, core software principles like CI/CD, security, and observability remain essential for building robust agents.
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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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Learn how to bring context to AI agents using the Model Context Protocol (MCP). This talk details how MCPs, Skills, and SubAgents create robust, secure, and highly productive AI workflows for enterprise development.
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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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AI agents have made writing code nearly free, shifting the bottleneck from coding to defining the problem. The future of engineering lies in the Product Engineer role: figuring out what 'good' means.
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AI is drastically changing software development. The focus is shifting from writing code to mastering system architecture, process governance, and the role of the forward-deployed engineer.
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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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Is the agent harness dead? Jediah Katz explains that while models are absorbing coding skills via the data flywheel, the harness remains critical for infrastructure, security, and unlocking new, non-coding frontiers.
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Loop engineering designs systems where AI agents iteratively prompt, observe, and adjust toward a measurable goal. This process requires managing context, tools (MCP/APIs), and persistent state (spine).
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AI agents are shifting from complex coding tasks to proactive 'assistance.' Engineers must focus on durable state management, structured output (Jev), and centralized connection gateways to build reliable, production-grade AI tools.
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AI Evals should not be a separate role; they must be an inner loop process. Domain expertise is key, and establishing a 'benevolent dictator' to define 'good' is crucial for efficient evaluation.
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AI development is defined by 'loop stacking.' The core skill is understanding your operational loop level—scaling up for productivity or scaling down for reliability.
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AI News & Strategy Daily | Nate B Jones
REA (Reverse Engineer Anything) is a tool that plugs into AI agents (Claude/Codex) to open up software on your computer and show you how it's actually built. It bridges the gap between business requirements and technical implementation.
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The consensus is that the web must change for agents, not just the agents themselves. WebMCP offers an elegant API to specify deterministic actions, enhancing security and allowing for chained actions across protocols.
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AI makes building software easier than ever, but this convergence creates a 'median' product that is free for everyone. To succeed, focus on novelty and distribution, not just the model.
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The future of AI engineering involves building robust, governed agent frameworks. Key architectural components include a centralized data vault, a five-layer agent lifecycle, and continuous evaluation loops to manage non-deterministic behavior.
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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 is changing software development from manual coding to architectural orchestration. Learn the five concepts—Spec-Driven Development, Context Engineering, and Loop Engineering—needed to master AI agents and build robust systems.
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The Data Context Layer provides a graph-based solution to give AI agents semantic understanding of data and code. By computing lineage, grain, and dependencies, it moves agents beyond ambiguous text search and unreliable SQL.
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