AIdataNewsGlobal AIApplications Guide: Use Cursor's Agent SDK for Team Workflow Agents

For teams adopting AI applications inside existing software routines, the practical question is less about naming a model and more about placing reusable judgement where work already arrives: review queues, compliance checkpoints, and incident follow-up. Today, the Agent SDK is generally available as the framework used to build managed agents such as Bugbot, Security Reviewer, and Rollouts. Its application value is that an agent can be defined as code, inspected like policy, connected to repositories, and moved into use without rebuilding the whole development environment around a fixed product.

This framing matters because the most useful agent is the one that matches a particular team workflow. Previously, teams had to choose between adopting a tool shaped around another process or creating a custom stack from the beginning. The Agent SDK gives an in-between path: start from a template or documentation aimed at a coding agent, describe rules in files, test behavior locally, and deploy an agent to Cursor infrastructure with agent-sdk deploy in one command.

Where code-defined agents sit in real workflows

These agents are event-driven helpers. They attach to familiar systems, wait for a relevant trigger, then take an action that a team would otherwise repeat manually. The core scenarios are pull-request review, compliance workflows, and incident investigation. The value is not conversation; it is repeatable judgement attached to work already flowing through the organization.

Application area	Typical trigger	What the agent can do	How value is judged
Pull-request review	A change is submitted in GitHub, GitLab, or Bitbucket	Assess risk, route the change to the right team, approve work that meets policy	Whether it catches policy mismatches and reduces manual triage
Compliance workflow	Policy-sensitive work enters a review path	Apply recorded standards and let reviewers inspect the logic behind decisions	Whether policies can be changed and audited as code
Incident investigation	An alert or incident event occurs	Use connected tools and specialized agents to carry out follow-up work	Whether repeated investigative steps become faster and traceable

A practical application example: kb-reviewer

A Cursor team had assembled a fast-growing knowledge base of lessons learned and wanted every pull request checked against it, so past decisions would remain enforceable as the codebase changed. Using the Agent SDK, they created kb-reviewer in about a day. The agent now reviews every PR in the main codebase, marks changes that conflict with a stored lesson, points to the relevant note behind each finding, and encourages engineers to reconcile the discrepancies it reports.

Describe every agent component in project files

An Agent SDK agent is organized like a small software project. Instruction files are placed beside tool code, service connections, and the evals used to test it.

• Prompts are written in Markdown.
• Tools are defined as TypeScript functions.
• Policy and workflow logic can be inspected before it affects live work.
• Because the agent is expressed as code, changes can follow the same version control and review process as other software work.

Step-by-step: from local playground to deployed agent

A first project can be assembled in the local playground. After creating a directory and launching it, a starter agent may import homedir from node:os and join from node:path, then call defineAgent from @cursor/agent-sdk. The example can name it Robo, choose composer-2.5, set effort high and fast true, and point local cwd to the homedir-based path .cache/factory/robo.

1. Choose one bounded workflow where events are already clear.
2. Create the project directory and open the local playground.
3. Define the starter agent using defineAgent from @cursor/agent-sdk.
4. Connect it to GitHub, GitLab, or Bitbucket when repository events matter.
5. Encode the team standard: risk assessment, routing, and approval conditions.
6. Run evals and event replay locally before sending the agent into a shared environment.
7. Deploy with agent-sdk deploy; this sends the agent to Cursor infrastructure in one command.
8. Manage secrets and integrations from the dashboard while keeping local access to the same logs, evals, and playground.
9. If the team controls its own runtime, run the agent as a single Node process that can be packaged in a container.

Safe iteration, handoffs, and evaluation

The Agent SDK ships with evals and event replay, giving teams a way to iterate quickly while checking that critical workflows run as expected.

For more complicated work, an agent can hand a task to another specialized agent with its own tools and sessions. A job can also be divided among subagents working in parallel.

The PGA Tour uses the Agent SDK to power its software development factory, and evals keep that team moving quickly.

The Agent SDK has enabled PGA to build every component of our software factory on Cursor's infrastructure. It provides the flexibility we need to build agents that drive our development process. And built-in support for evals allows us to quickly iterate on new ideas and optimize existing capabilities reliably.

Michael Gyarmathy, Principal Engineer, PGA America

Common application mistakes to avoid

• Starting with a broad automation ambition instead of one event-driven workflow that has clear triggers and owners.
• Writing prompts as loose instructions rather than code-reviewable Markdown, tools as TypeScript, and tests as evals.
• Treating deployment as the end of governance, even though secrets and integrations are managed from the dashboard while logs and evals remain available locally.
• Relying on a generic workflow when the team standard is different; the release argues that the most useful agent matches how a particular team works.
• Ignoring the need to reconcile discrepancies when an agent flags a recorded lesson, as the kb-reviewer example is designed to do.

Action recommendations for teams piloting agents

1. Pick a narrow first use case: pull-request review, compliance workflow, or incident investigation.
2. Document the rule set as reviewable files, not only chat prompts.
3. Use the local playground to test the agent before deployment.
4. Run evals and event replay to confirm critical behavior.
5. Deploy only after approval routing and policy checks are clear.
6. Choose Cursor infrastructure for managed operation or self-managed infrastructure for a single Node process.

For teams whose AI application goal is to make recurring engineering judgement repeatable rather than simply add a conversational assistant, the next step is practical: review the documentation, launch the local playground, and validate one workflow before expanding the roadmap.