Master Cursor Agents: Plan, Build, Debug, Ship Code

Detailed Prompts and Context Discipline Drive Agent Output Quality

Coding agents excel when given precise intent rather than vague instructions. A simple prompt like "add a model page" leaves the agent guessing layouts, components, and styles, often yielding suboptimal code. Instead, reference existing codebase patterns, provide logs, screenshots, or specific requirements—e.g., "Use the dynamic route pattern from /models, match our design system's pills with icons, exclude hidden models." This specificity boosts output quality dramatically.

Context acts as the agent's working memory, accumulating messages, tool calls, and file reads. Overloaded context leads to errors, so start new conversations for fresh features or when the agent drifts. Tag exact files if known (@file.ts), but leverage the agent's tools for discovery. Latest models handle this well, pulling relevant context autonomously.

"The intent you provide the models through prompting really makes a difference in the quality that you get out."

Sub-agents prevent context bloat: spawn isolated explorers for searches, keeping the main thread lean. They return summaries only, ideal for large codebases.

Codebase Mastery via Semantic Search and Visualization

Agents replace manual regex hunts with natural language queries. Cursor equips them with instant grep (faster recursive ripgrep), semantic search (embeddings map code symbols to your query), and shell tools. Ask "Where do we handle authentication?"—it finds middleware semantically, even without literal matches.

Combine literal (grep) and semantic for best results, especially on massive repos. Auto-indexing happens in background; no setup needed. For architecture, request Mermaid diagrams: "Generate a Mermaid diagram of data flow in docs app." These visualize onboarding, flows, and dependencies.

Common pitfall: editing code without understanding it first. Agents take requests literally, inventing utils when patterns exist—creating tech debt. Always explore first: "Show existing model listing patterns," then build.

"A common mistake... is when developers ask the agents to change code, but they don't really understand exactly how that code works yet."

Plan Mode: Iterative Feature Development from Idea to Testable Build

Break features into verifiable steps: start in Cursor's plan mode (Shift+Tab). Vague input like "Add dedicated pages for each model in apps.docs" triggers sub-agents to explore structure, grep files, read configs, and propose:

1. Research codebase (reuse components? dynamic routes?).
2. Clarify requirements (e.g., "Which models? Non-hidden only.").
3. Generate editable Markdown plan with steps, files, Mermaid diagram.
4. Edit plan interactively.
5. Click "Build" for code diffs.

Post-build: Agent starts dev server ("npm run dev"), opens integrated browser. Test manually, feed errors back (copy-paste stack traces). Iterate with visuals: screenshot page, say "Make pills nicer with icons, match brand colors." Use voice input for speed.

Typed languages/linters shine here—errors auto-surface for quick fixes. This loop yields shippable features fast, following existing patterns.

Prerequisites: Familiarity with agents (tools, loops from prior course), your repo open in Cursor. Fits early dev cycle: ideate → plan → build → test.

Debugging Fundamentals Amplified by Agents

Follow six principles for any bug (human or agent):

1. Reproduce reliably.
2. Minimize repro (strip extras).
3. Isolate changes (one at a time).
4. Hypothesize root causes.
5. Instrument (logs/debugger).
6. Add tests post-fix.

Simple bugs: Paste stack trace—"Fix this error." Agent resolves instantly.

Complex: Use debug mode. Agent hypothesizes, adds targeted logs, prompts repro, analyzes runtime evidence, fixes surgically. Superior to manual slog.

Tips:

• Multi-model shots: Compare fixes from different LLMs.
• Evidence tools: "Analyze slow query" (explain analyze), external MCPs (Sentry for errors).
• Probe fixes: "Other edge cases? True root cause?" Avoid hallucinations; build conviction.

"If you don't understand the fix, it's going to be very hard for you to validate whether the code is actually correct."

Rigorous Review and Testing Prevent Regressions

Agent code demands human standards. Self-review first: "Find issues in changes"—spots i18n bugs like untranslated strings, auto-fixes.

Break big diffs: "Split into semantic commits, push PR." Easier for reviewers.

PR stage: Self-review + AI tools (BugBot comments logic bugs, dupes). Fix pre-human: clipped icons, duplicated functions.

Quality criteria: Compiles, passes tests/lints, follows patterns, no regressions, testable. Agents handle reviews/tests but verify manually.

Practice: On your repo, plan a small feature (e.g., new UI component), debug an injected bug, review the PR.

"Your standards for what gets merged should be the same whether it was written by an agent or written by hand."

Key Takeaways

• Start vague prompts in plan mode; iterate to precision with sub-agents and clarifications.
• Always explore codebase first (semantic/grep/Mermaid) before edits to honor patterns.
• Build-test-iterate loop: dev server + error feedback + screenshots/voice for rapid polish.
• Debug systematically: repro → minimize → hypothesize → instrument → test.
• Multi-pass review: self + agent issues + semantic commits + BugBot + human PR.
• Manage context ruthlessly: new chats, sub-agents, targeted @files.
• Question agent fixes deeply; conviction over blind trust.
• Ship faster: agents for 80% grunt, you for architecture/intent.