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All digests tagged Security Engineering
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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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Anthropic details its Managed Agents architecture, separating the agent's brain from its hands for improved reliability and security. They also discuss rewriting the Messages API from Python to Rust to achieve massive CPU efficiency gains.
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Anthropic introduces Flows in Claude Managed Agents, a multi-agent orchestration engine that allows a single goal to be executed by up to 1,000 collaborating agents. This solves the scaling problem of single agents and enables complex, verifiable AI workflows.
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Transitioning AI agents to production requires a governed framework. Learn how to implement centralized MCP Gateways, LangGraph workflows, and robust CI/CD pipelines for secure, scalable, and observable enterprise AI.
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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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The future of AI infrastructure demands a shift from separated cloud layers to a co-designed stack. Speakers detailed the move from pull-based web search to agent-driven, push-based data consumption, emphasizing the need for GPU-native indexing and robust, holistic security.
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Rowan Christmas demonstrates how coding agents can leak sensitive PII from host machines. He introduces Docker Sandboxes (sbx), a microVM solution that provides 'security by design' through kernel isolation and default-deny networking.
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Building agents is like shipping a 'malware starter pack.' Security requires deterministic layers, scanning the entire content supply chain, and ensuring the LLM acts only as an adviser, never an enforcer.
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Docker introduces sandboxing using MicroVMs to safely run autonomous AI agents locally. This hardware-level isolation prevents rogue agents from accessing sensitive host data, balancing speed and security.
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Platform engineering must evolve to manage Generative AI. The key is building a robust 'harness'—a system of guides and sensors—to ensure consistency, mitigate vulnerabilities, and guide agents toward high-quality outcomes.
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