Cursor 201 Advanced Workflows

This session moves users from prompt tactics to repeatable systems for context engineering, validation, and multi-agent execution.

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Advanced workflow checklist

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• Context engineering for complex tasksBuild context windows intentionally and isolate task boundaries.
• Model strategy and cross-model validationChoose thinking depth and validate outcomes with second-pass review.
• Advanced orchestration and MCP workflowsUse multiple agents, tools, and integrations for delivery speed.

Module 1: Context engineering for complex codebases

Objectives

• Build precise context windows using scoped files, symbols, and docs.
• Separate planning and implementation conversations for long-running tasks.
• Use ask mode and exploration workflows to map unfamiliar systems faster.

Demo/Lab

1. Take a cross-file task and map the affected surface area before coding.
2. Build a plan chat, then launch a separate implementation chat.
3. Validate context quality by checking whether the assistant references the right files.

Knowledge check

Knowledge check: context engineering

What best reflects a context engineering mindset?

Use the same prompt template regardless of repository size.Curate task-specific context and split chats by objective.Rely on model memory instead of explicit references.

Apply at work

• Write a shared "context intake" checklist for your team.
• Pilot it on one multi-file refactor this sprint.

Module 2: Model selection and outcome validation

Objectives

• Select model depth based on task complexity and risk.
• Compare outputs across model families for high-stakes decisions.
• Use explicit quality gates before merge or release.

Demo/Lab

1. Run the same feature request with a fast model and a deep-thinking model.
2. Compare solution quality, edge-case handling, and test completeness.
3. Capture a model selection rubric your team can reuse.

Knowledge check

Knowledge check: model strategy

Select all practices that improve high-confidence outcomes.

Use model-agnostic workflows and validate critical work with a second pass.Skip validation if a model sounds confident.Match model depth to risk and complexity.

Apply at work

• Add a lightweight model selection note to your pull request template.
• Require second-pass validation on high-risk code paths.

Module 3: Multi-agent orchestration and MCP

Objectives

• Coordinate parallel agents safely across scoped tasks.
• Use MCP tools intentionally to connect external systems.
• Establish guardrails for async workflows and handoffs.

Demo/Lab

1. Split a feature into parallel workstreams (API, UI, docs) and compare throughput.
2. Route one workflow through an MCP integration relevant to your stack.
3. Document handoff criteria between background and foreground work.

Knowledge check

Knowledge check: orchestration and MCP

Which behavior is most important when scaling multi-agent workflows?

Run many agents without explicit ownership or boundaries.Define scoped responsibilities, checkpoints, and validation gates.Avoid external integrations to reduce complexity.

Apply at work

• Pilot one parallel-agent workflow on a medium complexity feature.
• Publish a short post-mortem on what improved speed or quality.

✦Challenge mission

Choose one production workflow and redesign it as a multi-agent pipeline with explicit handoffs, quality checks, and rollback safety.

Reinforcement after 201

Use bi-weekly office hours to review real implementation examples and nominate champions for advanced reverse demos.

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