Continue from this implementation example into live AI market coverage.

Companies like Anthropic, OpenAI, LangChain, and Stripe have built production-grade agent harnesses—comprehensive software infrastructures that wrap large language models (LLMs) to enable reliable autonomous agent behavior. These harnesses manage orchestration loops, tool integrations, multi-timescale memory, context management, prompt construction, output parsing, state management, error handling, and safety guardrails. For example, Anthropic's Claude Code uses a three-tier memory hierarchy and git-based checkpoints, OpenAI's Agents SDK supports function and hosted tools with strict guardrails, and Stripe limits retry attempts to improve reliability. These harnesses address real-world challenges such as context window limitations, silent tool call failures, error compounding, and safety enforcement, enabling LLMs to perform complex multi-step tasks with improved success rates and robustness.