How I Use AI Agents in My Dev Workflow

Modern software engineering is undergoing a paradigm shift. Rather than viewing AI strictly as an inline autocomplete tool, I integrate specialized **AI Coding Agents** directly into my daily execution pipeline.

1. Skill-Driven Architecture & MCP Integrations

By giving AI agents deterministic access to repository skills, semantic code search, and Model Context Protocol (MCP) toolchains, AI assistants operate with deep context about the system's underlying boundaries and state.

• **Context Precision**: Knowledge items and agent rules prevent standard AI "hallucinations" or anti-patterns.
• **Repeatable Toolchains**: MCP servers expose database queries, schema validation, and health checks directly to the LLM agent loop.

2. Planning Before Execution

For non-trivial tasks, I always require agents to run through a **Planning Mode**: 1. Inspect codebase architecture and dependencies. 2. Draft an implementation plan artifact with file-by-file changes. 3. Review and refine design decisions prior to code mutation.

3. Human in the Loop & Deterministic Safety

AI speeds up scaffolding and refactoring by 5x to 10x, but human oversight remains critical for business domain rules, security guarantees, and performance profiling. Combining continuous automated linting/building with agentic execution creates a robust shipping machine.