Live fixing an agent defect

This is the Claude Code SDK (also known as the Claude Agent SDK): the core agent harness now available as a library (TypeScript and Python) for production systems. It lets you integrate Claude's agentic capabilities—like file operations, code execution, and web search—directly into your developer tools and scripts. Leverage built-in features like automatic context management and the Model Context Protocol (MCP) for powerful, enterprise-ready integrations with external systems like Jira or Slack. Deploy sophisticated agents, such as SRE agents for diagnosing production issues or security review bots for auditing code, with fine-grained control over permissions and capabilities.

Claude is a next-generation AI assistant developed by Anthropic, a research firm prioritizing AI safety. The models (including Opus, Sonnet, and Haiku) leverage Constitutional AI to ensure helpful, honest, and harmless outputs, a key differentiator from competitors. Claude excels at complex enterprise tasks: processing massive context windows for in-depth data analysis, generating and reviewing code, and providing expert-level summarization for documents up to 200,000 tokens. It is deployed as a conversational chatbot and via API, offering scalable AI solutions for developers and businesses.

Haiku, originally OpenBeOS, is a free, open-source operating system that directly succeeds the BeOS architecture; development began in 2001. The system is built for responsiveness, featuring a fully threaded design for maximum efficiency on multi-core CPUs and a custom hybrid kernel derived from NewOS. It utilizes the Be File System (BFS), which supports indexed metadata, treating the file system like a database. The entire project (kernel, drivers, toolkit, and desktop applications) is written by a single team, ensuring a unique level of consistency and a cohesive object-oriented API for accelerated C++ development.

Tool calling architecture bridges the gap between static model reasoning and active system execution by turning LLMs into decision engines for external APIs. Instead of executing actions directly, the model evaluates user intent, selects the appropriate tool from a provided schema, and outputs structured JSON containing the required parameters. The hosting application executes the actual API or database call, returning the raw payload back to the model to synthesize a final response. This decoupling ensures secure, deterministic execution while enabling advanced agentic patterns like parallel function execution, programmatic loop orchestration, and dynamic tool search.

Think of the agentic loop as the engine that upgrades a standard AI chatbot into a fully autonomous worker. Instead of stopping after a single prompt, the system operates in a continuous, self-directed loop: it analyzes a task, selects the right tools (like GitHub or Greptile), executes an action, evaluates the output against a target metric, and refines its next step. This design shines in highly structured, binary environments (such as automated code reviews or SEO generation) where the agent can run independently until it hits a defined stopping condition (like a 5/5 quality score or a strict token budget limit).