AI agents get 14 filesystem tools optimized for coding workflows. Read massive files with offsets, batch-process multiple files, search codebases with streaming results, and make surgical edits with diff precision. When your LLM is writing code, refactoring projects, or analyzing repositories, these tools provide atomic operations, smart path validation, and concurrent traversal that keeps pace with agent thinking speed.

Spawn full VT100 pseudoterminal sessions, run builds, execute tests, and orchestrate complex command chains—all from agent context. Smart state detection knows when commands finish, real-time output streaming keeps LLMs informed, and security controls prevent dangerous operations. Perfect when AI agents need full system access: running npm install, compiling code, or managing deployment pipelines without human intervention required.

LLM coding agents can break down complex problems across multiple reasoning steps with stateful thinking sessions. Branch thought paths when exploring alternatives, revise previous reasoning when new insights emerge, and maintain unlimited context across extended problem-solving. Actor-model concurrency ensures lock-free performance. Ideal for planning architectures, debugging complex issues, or any multi-step analysis where agent thinking evolves dynamically.

... with sub-agents ... true N-depth agent delegation with full prompt control. AI agents can spawn specialized Claude sub-agents for deep research, complex code generation, or parallel analysis tasks. Delegate work to specialist agents with custom prompts, stream their output in real-time, and manage full conversation lifecycles. Built for hierarchical, coordinated agent pyramids.

LLM coding agents can track their own tool usage, analyze what's working, and optimize workflows with built-in introspection. See success rates, execution patterns, and detailed call history with full argument inspection. Essential for AI self-improvement: understand which tools you're using most, spot failure patterns, and debug your own behavior. Every invocation is tracked.

AI agents can create, edit, and manage reusable prompt templates with Jinja2 rendering and schema validation. Build prompt libraries, A/B test instruction variations, and standardize complex agent instructions—all programmatically. Maintain consistent instruction patterns across sessions with version control and dynamic rendering using variables, conditionals, and loops for sophisticated prompt engineering.