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Automated Software Engineering

Build agents and workflows for software engineering

Create agents in DataPrompt with the models, tools, and context they need. Combine them into workflows that plan, implement, test, and review software, for your own projects or across a team.

Get started Explore the workflow
process / feature-delivery RUNNING
PROCESS Take a scoped change from plan to reviewed pull request.
  1. 01
    Plan and scopeadvanced model · plan required before step 02
    done
  2. 02
    Implementefficient model · repository workspace
    done
  3. 03
    Run your checksyour suite, your commands · halts on failure
    42s
  4. 04
    Reviewadvanced model · diff, risks and cost attached
    next
Process budget used34%
4 stages · 2 models · 61% cachedApproval required before handoff

How it works

Create your agents and put them to work together

Build and configure agents directly in DataPrompt. Give each one a role, instructions, a model, and access to the tools and project knowledge it needs. Test and refine its behavior as you develop it.

Use agents individually or combine them with Python steps in a reusable workflow. Connect your repository, define the handoffs, and add checks and approvals where needed. Review the results and costs to improve the next run.

Workflow Steps Steps run sequentially; next links are set by order.
No steps defined yet.
  1. 1
    Plan and scope Agent step
    Agent
  2. 2
    Implement Agent step
    Agent
  3. 3
    Run checks Agent step
    Agent
  4. 4
    Review Agent step
    Agent

Try the workflow editor: add, reorder, or remove a step.

  1. 01
    Build your agents

    Create agents for planning, coding, testing, or review. Configure their instructions, models, tools, and context, then test each agent on a task.

  2. 02
    Compose a workflow

    Arrange agents and code steps into a repeatable process. Define what each stage receives, what it produces, and which checks must pass before work moves on.

  3. 03
    Run and refine

    Start from chat, a webhook, or a schedule. Review each stage and adjust the workflow as you learn from its results.

Workflow structure

A consistent process, from planning to review

Define the stages of your workflow once, with checks and approval points where you need them. Reuse that structure across tasks and adapt it as your working practices evolve.

  1. 01 Plan plan the change first gate
  2. 02 Implement write the code gate
  3. 03 Test run the tests gate
  4. 04 Review review before merge gate

Workflow optimization

Optimize agent behavior and control costs

Use run history to identify repeated tool calls and unnecessary spending, then refine the models, tools, and context each stage uses. Compare runs to assess the effect of your changes.

In this example, repeated searches increased the implementation cost. Explore the finding below and review a suggested change to the tools available at that stage.

1 finding in Run 15
Run 14two days ago
  1. 01
    Plan and scope advanced model
    $0.022
  2. 02
    Implement efficient model
    $0.068
  3. 03
    Run checks command step
    $0.019
  4. 04
    Review advanced model
    $0.027
Whole run$0.135
Run 15just now
  1. 01
    Plan and scope advanced model
    $0.022 —
  2. 02
    Implement efficient model
    $0.149 2.2×

    Recommended change

    Implement called repo_search 14 times in this run and twice in the last one, fetching files it had already been given.

    • Run 14Run 15tokens
    • read_file668.4k
    • apply_patch223.2k
    • repo_search21437.8k

    Disable repo_search for this stage so the agent stops reaching for it.

  3. 03
    Run checks command step
    $0.019 —
  4. 04
    Review advanced model
    $0.027 —
Whole run$0.216
01
Validation steps

Run checks before each handoff and review failures in the context of the stage that produced them.

02
Cost visibility

Review model usage and spending by stage to make informed choices about where to use each model.

03
Targeted retries

Review a failed stage, make an adjustment, and resume from that point in the workflow.

04
Run history

Review the models, actions, checks, and approvals associated with each run.

05
Isolated execution

Agents work in a sandbox, with repository access handled by the platform and credentials kept outside the agent environment.

Secure agent execution

Put agents to work without exposing repository credentials

Set the scope of repository work before a run begins. Agents work in an isolated workspace while DataPrompt manages credentials and access to remote actions.

The platform checks each requested remote action against the permissions assigned to the run and records it for review. This keeps access decisions separate from the agent's work.

Managed by DataPrompt

  • GitHub credential
  • Capability grants
  • Run scope and budget

Credentials and access grants remain with the platform. The agent requests repository actions through the tools assigned to its run.

agent sandbox

Tools available in this example

  • github_issue_get
  • github_issue_comments
  • source_control_push
  • source_control_create_change_request
  • prepare_change
  • publish_change

no credentialno networkno other tool

What it can do

  • Read and edit files in its own workspace
  • Run the commands and tests you allowed
  • Ask for one of the six tools it was given
  • Open a pull request for you to review

What it cannot do

  • Hold a credential or reach the network
  • Reach for a tool that is not on that list
  • Merge, approve, deploy or release anything
  • Act outside the scope the run started with

Pricing

For individual projects and engineering teams

Each plan includes validation, scoped repository access, and run history. Choose the capacity and support that fit your work, from independent development to an organization-wide rollout.

Professional

For independent developers using coding agents in their daily work.

$150 / month
  • Repeatable engineering processes
  • Validation, repair paths, and approvals
  • Complete run history and evidence
  • Controlled repository workspaces
  • Email support
Get started
For growing use
Business

For teams running shared workflows across more projects.

$350 / month
  • Everything in Professional
  • Higher request and parallel-run limits
  • More workflow and delegated-agent capacity
  • Priority email support
Choose Business
Enterprise

For organizations with specific capacity, onboarding, and procurement requirements.

Custom
  • Tailored workflow and usage capacity
  • Security and architecture review
  • Commercial terms and invoicing
  • Rollout and onboarding support
  • Priority support path
Contact us

Professional includes 10M efficient-model and 1M advanced-model tokens each month; Business includes 30M and 5M. Allowances count combined input and output tokens and reset monthly. Model availability may change with provider availability and pricing. Taxes may apply.

Getting started

Start with an agent for a task you know

Build and test your first agent, then bring in additional agents and workflow steps as the task grows.

Get started