The biggest mistake I see with agentic AI? Not providing enough context.

Most ask: "How do I optimize this function?"

Better: "REST API in Node.js processes 10K req/min. Validation function has 200ms latency. Stack: Express + MongoDB. Optimize without breaking compatibility?"

The difference? Code that works vs. code you ship to production. 🚀

Why Context Powers Agentic Tools

Agentic AI tools (Cursor, Claude Code, GitHub Copilot) can autonomously navigate your codebase, read files, and understand architecture. They act as agents, not just autocomplete.

But they need you to define WHAT matters:

✅ Tech stack (specific versions) ✅ Constraints (performance targets, compatibility) ✅ Scale (100 vs. 1M users = different solutions) ✅ What you tried (prevents repeated suggestions) ✅ Environment (prod/dev, cloud provider, limits)

Real Examples

Debugging 🐛

❌ "Debug this code"

✅ "Production bug. React 18 + TypeScript. Hook in src/components/DataList.tsx causes infinite re-renders in lists >1000 items. React Profiler: 400ms per render. Maintain compatibility with DataListItem."

The agentic AI explores the file, related components, and suggests fixes based on your constraints.

Optimization ⚡

❌ "Make this faster"

✅ "src/api/validators/user.ts processes 10K req/min on AWS Lambda (Node 18, 512MB). Current: 200ms, target: <100ms. Can't change MongoDB schema (mobile app compat). Optimize?"

The agent analyzes the file, checks dependencies, and proposes optimizations within your constraints.

How Agentic AI Works

These tools can:

• ✅ Navigate entire codebases autonomously
• ✅ Read multiple files simultaneously
• ✅ Understand module relationships
• ✅ Run tests and validate changes

You guide them by:

• 🎯 Defining the problem precisely
• 🎯 Setting priorities (speed? maintainability?)
• 🎯 Marking constraints (compatibility, resources)
• 🎯 Pointing to relevant files/patterns

Think of it like this: The agentic AI is your coding partner. It can explore and act autonomously, but you set the mission parameters.

Context + constraints = production-ready solutions. 🚀