Anthropic has published an extensive engineering blog showcasing the technical approaches behind its Claude AI assistant and agent development practices.

The collection includes 20+ detailed posts spanning agent architecture, evaluation methods, and safety measures. Topics range from "Building effective agents" to "Designing AI-resistant technical evaluations" and "Effective harnesses for long-running agents."

Recent posts focus heavily on agentic capabilities. The company detailed its "managed agents" approach in April 2026, which decouples AI reasoning from execution to improve scalability. Another post explores how Anthropic built a C compiler using parallel Claude instances working together.

Safety and evaluation frameworks

Several posts address AI safety concerns in production systems. The company published detailed postmortems of Claude Code quality issues, tracing problems to specific system changes and outlining remediation steps.

Anthropic also shared methods for creating "AI-resistant" technical evaluations that remain valid as models improve. The posts describe infrastructure for measuring agent performance across coding tasks, including the SWE-bench Verified benchmark where Claude 3.5 Sonnet achieved strong results.

The engineering team detailed their approach to sandboxing Claude Code, moving beyond simple permission prompts to more autonomous yet secure execution environments. They introduced "auto mode" that reduces friction while maintaining safety guardrails.

Developer tools and integration

The blog covers practical implementation details for developers building with Claude. Posts explain the Model Context Protocol (MCP) for connecting AI agents to external tools and data sources.

Anthropic described their "Desktop Extensions" system for one-click MCP server installation and shared best practices for agentic coding workflows. The company also introduced "Agent Skills" - pre-built capabilities that equip AI agents for real-world tasks.

The posts include technical deep-dives on context engineering, tool design, and multi-agent coordination. One post details how Anthropic uses agents to write better tools for other agents, creating a feedback loop for capability improvement.

The engineering blog positions Anthropic as focused on practical AI deployment challenges rather than just model development, emphasizing reliability and safety in production environments.