How to run agentic workflows with Cursor and Claude Code

2026-03-02 · Code Pipelines

Agentic isn't autocomplete.

It's multi-step tasks where the AI plans, edits, and runs - and most teams still run it wrong.

Here's how to set up and run agentic workflows with Cursor and Claude Code so you get the right result in fewer rounds.

What an agentic workflow actually is

An agentic workflow means the AI doesn't just suggest a single line or block - it takes a goal ("add a rate limiter to this API"), plans the steps needed, edits multiple files, runs terminal commands, and can call external tools. You stay in the loop by reviewing diffs and approving or editing changes before they apply. The model has agency while you keep control via review.

This is a meaningful shift from 2024's autocomplete-first AI coding. In 2026, the tools that matter handle multi-step tasks without you micromanaging every line. The key skills are: writing clear task specs, configuring context so the agent knows your codebase, and reviewing diffs effectively before accepting changes.

Define your workflow by outcome first: "I want the AI to implement feature X," "refactor module Y to use async/await," or "add tests for Z with 80% coverage." One clear outcome per agent run. For how different tools compare on agentic capabilities, see best agentic coding tools 2026.

Cursor Agent mode: setup and patterns

Cursor Agent mode is the primary tool for IDE-based agentic work. Here's how to set it up properly:

1. Configure .cursorrules - Create a .cursorrules file at your repo root. This tells the agent your stack, conventions, and constraints before it starts. Example:

• Language and framework (e.g. "TypeScript, Next.js 14, App Router")
• Code style (e.g. "use named exports, no default exports")
• Testing conventions (e.g. "use Vitest, co-locate test files")
• What to avoid (e.g. "do not install new dependencies without asking")

Good rules reduce clarification rounds and prevent the agent from making decisions you'd reverse. This directly lowers credit consumption - fewer rounds means fewer fast requests used.

2. Use @-mentions to scope context - In Agent mode, use @file or @folder to attach only what the agent needs. Including the whole repo when you only need two files wastes context tokens and credits. For a task touching the auth module, attach just @src/auth, not the entire codebase.

3. Write the task as a spec, not a question - Compare:

• Weak: "Can you improve the login function?"
• Strong: "Add rate limiting to the POST /auth/login handler. Limit to 5 attempts per IP per 15 minutes. Use Redis for the counter. Return 429 with a Retry-After header when exceeded. Add a test for the rate limit behavior."

The second prompt gets it right in 1–2 rounds. The first takes 5–10 turns of clarification. Tools like BrainGrid are built to help Cursor and Claude Code users write these specs efficiently - structured task descriptions that feed the agent exactly what it needs.

4. Review every diff before accepting - Cursor shows you all proposed changes before applying. Read them. The agent is right most of the time, but reviewing keeps you in control and helps you catch edge cases. Accept selectively: you can approve parts and reject others.

5. Have the agent run tests - End each agentic task by asking the agent to run your test suite: "Run npm test and fix any failures." Closing the loop in the same session means the task is verifiably done, not just "looks right."

Claude Code CLI: terminal-first agentic patterns

Claude Code CLI runs from your terminal - claude in your project directory. It reads your codebase, edits files, and runs commands without a GUI. It's the right tool when:

• You're already in the terminal and don't want to open an IDE
• The task is scripted or automated (CI pipelines, batch migrations)
• You want to chain tasks: "do X, then run tests, then do Y"
• You prefer terminal-native workflows over IDE workflows

Useful Claude Code CLI patterns:

• Batch migrations: "Update all API calls in src/api/ to use the new v2 endpoint format." Claude Code reads every file, makes consistent changes across the whole directory.
• Automated refactors: "Convert all class components in src/components/ to functional components with hooks." Runs through the whole folder systematically.
• One-off scripts: "Write a script that reads the CSV in data/users.csv and imports records into the database using our Prisma schema. Run it and show me the output."
• CI task automation: Use Claude Code in a GitHub Actions step to automate code review comments, generate changelogs, or apply consistent formatting fixes.

Claude Code respects a CLAUDE.md file at your repo root - similar to .cursorrules, it gives the agent project context and constraints. For a direct feature comparison with Cursor Agent, see Claude Code vs Cursor Agent.

Combining Cursor and Claude Code: the handoff pattern

Most advanced teams use both tools - each for what it's best at. Here's the practical split:

• Cursor Agent: interactive feature development, multi-file refactors where you want a visual diff, debugging with codebase context, any work where you're actively iterating
• Claude Code CLI: batch/scripted work, automation, CI tasks, quick one-off changes without opening the IDE, tasks you want to run headlessly

Example handoff flow for a new feature:

• Write the spec in BrainGrid (structured task description, acceptance criteria)
• Run Cursor Agent with that spec to implement the feature interactively - review diffs, approve changes
• Use Claude Code CLI to generate tests for the new feature: claude "Add tests for the feature I just implemented in src/features/rateLimit.ts"
• Claude Code runs the tests and fixes failures in one session
• Commit and push - CI runs clean

Same repo, same context files (.cursorrules / CLAUDE.md), complementary tools. The overlap is minimal once you internalize which tool fits which task.

MCP and context extensions

MCP (Model Context Protocol) lets Cursor and Claude Code connect to external data sources and tools - database schemas, internal APIs, ticketing systems, documentation. Add MCP servers so the agent can look up live data without you switching context. Examples: a database MCP server that lets the agent query your schema before writing migrations; a GitHub MCP server that lets it read issue descriptions; a docs MCP that gives it your API reference.

Configure MCP servers in Cursor under Settings → MCP, or in Claude Code's config. Start with one or two high-value sources (your DB schema and your API docs are usually highest value). For ideas on what to add, see best MCP servers for Cursor and Claude.

Other useful configuration: diff-based review is not optional - always review changes before applying, especially on large agentic tasks. For discipline around this, see diff-based workflows with AI.

Deploying what you build

Once your agentic workflow produces working code, shipping it is the next step. The fastest path from an AI-built app to production: push to GitHub, connect to a host, and deploy on merge.

Railway is the simplest managed option - connect your GitHub repo, add environment variables, and it deploys automatically on every push. Supports Node, Python, databases, and background workers. For VPS-level control at lower cost, Vultr gives you a full server from $2.50/mo with GPU options for AI inference. For the full deployment guide, see deploying AI apps to production.

Our take

For most teams in 2026, the sweet spot is Cursor for IDE-centric agentic work (features, refactors, debugging) and Claude Code for terminal-first and scripted tasks. Configure both with good context files (.cursorrules / CLAUDE.md), write specs before running agents, and review every diff. Add BrainGrid to structure your tasks before they hit the agent - cleaner input means better output in fewer rounds. If you're building a full DevEx stack, agentic workflows are the core - pair them with security in CI and good onboarding so the whole team can run them safely.

Related: Cursor vs Copilot 2026, Best AI coding assistant 2026, Best agentic coding tools 2026, Claude Code vs Cursor Agent, Deploy AI apps to production.

What you get when you add a spec layer:

• Structured task specs that feed both Cursor Agent and Claude Code
• 1–2 rounds instead of 5–10 to get the right result
• Fewer clarification rounds - so fewer credits burned
• Acceptance criteria the agent can verify before it stops