Unlike simple chatbots, AI agents tackle complex requests by first creating a detailed, transparent plan. The agent can even adapt this plan mid-process based on initial findings, demonstrating a more autonomous approach to problem-solving.

The creator of Claude Code's workflow is no longer about deep work on a single task. Instead, he kicks off multiple AI agents ("clods") in parallel and "tends" to them by reviewing plans and answering questions. This "multi-clotting" approach makes him more of a manager than a doer.

For stubborn bugs, use an advanced prompting technique: instruct the AI to 'spin up specialized sub-agents,' such as a QA tester and a senior engineer. This forces the model to analyze the problem from multiple perspectives, leading to a more comprehensive diagnosis and solution.

Knowledge workers are using AI agents like Claude Code to create multi-layered research. The AI first generates several deep-dive reports on individual topics, then creates a meta-analysis by synthesizing those initial AI-generated reports, enabling a powerful, iterative research cycle managed locally.

Before writing any code for a complex feature or bug fix, delegate the initial discovery phase to an AI. Task it with researching the current state of the codebase to understand existing logic and potential challenges. This front-loads research and leads to a more informed, efficient approach.

LLMs often get stuck or pursue incorrect paths on complex tasks. "Plan mode" forces Claude Code to present its step-by-step checklist for your approval before it starts editing files. This allows you to correct its logic and assumptions upfront, ensuring the final output aligns with your intent and saving time.

For large projects, use a high-level AI (like Claude's Mac app) as a strategic partner to break down the work and write prompts for a code-execution AI (like Conductor). This 'CTO' AI can then evaluate the generated code, creating a powerful, multi-layered workflow for complex development.

Use the Claude chat application for deep research on technical architecture and best practices *before* coding. It can research topics for over 10 minutes, providing a well-summarized plan that you can then feed into a dedicated coding tool like Cursor or Claude Code for implementation.

AI can get hyper-focused on a specific task and lose sight of the overall user flow. A dedicated "Spec Flow Analyzer" agent can simulate a user persona and review the entire plan, ensuring all necessary steps are connected and the feature is cohesive from a user's perspective.