Give AI a task.
Keep control of the work.
WhoseDev Agent helps you move from an idea to implementation by turning high-level development requests into structured work across your codebase.
Ask. Plan. Build. The agent works within the models, tools, permissions, workspace context, and capabilities configured in your WhoseDev environment.
More than autocomplete.
More than a chatbot.
An AI agent is a system designed to work toward a goal rather than simply generate a response to a single prompt. In a development environment, that means the system can take a broader request, reason about the work involved, organize actions, and use available capabilities to help move the task forward.
That difference matters when software work becomes larger than one line of code. A real engineering task might involve understanding an existing implementation, finding related files, deciding what should change, creating new code, updating supporting code, and checking the resulting work.
WhoseDev Agent is built around that task-level workflow. You communicate the objective. The agent helps structure the work. Your development environment provides the context and capabilities. You remain the person making the final engineering decisions.
Ask. Plan. Build.
Three simple stages turn a high-level development request into structured implementation while keeping the developer in the loop.
Ask
Tell the agent what you want to accomplish in natural language. You can describe an outcome, a bug, a feature, a refactor, or a broader engineering objective without manually turning everything into individual prompts.
Plan
The agent reasons about the request and turns it into a structured sequence of work. It can consider relevant workspace context, files, dependencies, available tools, and the capabilities configured in your environment.
Build
The agent carries out the planned work by creating or modifying code through the capabilities available to it. You remain in control of the resulting changes and can review them before accepting them.
Software development is rarely a one-prompt problem.
Traditional autocomplete is excellent when you already know what you want to type. Chat is useful when you want an explanation or a generated answer. An agent is useful when the goal itself is larger than the answer.
“What code comes next?”
Helps you write code based on what is already around the cursor.
“How should I solve this?”
Helps answer questions, explain concepts, and generate individual pieces of work.
“Get this done.”
Helps turn a broader development objective into a sequence of reasoning and implementation steps.
From request to implementation.
The agent does not have to treat every request as a single generation. A broader development objective can be approached as a sequence of steps, with context and tools participating throughout the process.
Ask
Describe the outcome you want.
Plan
Work out the files, changes, and steps needed.
Build
Create or modify code using available tools.
Review
Inspect the work and decide what should be accepted.
$ agent task
Fix the authentication flow
✓ request received
✓ workspace context loaded
→ plan generated
✓ implementation steps prepared
→ building...
An agent that lives closer to your development environment.
One of the important ideas behind WhoseDev is bringing agentic capabilities closer to the developer's actual workspace. A local AI agent can be part of the development environment rather than feeling like a completely separate application where you constantly move context back and forth.
In practice, “local” can describe where parts of the agent workflow, context handling, tooling, or models operate. The exact behavior depends on your WhoseDev configuration and the models and services you choose to use.
That distinction matters because an AI agent is not necessarily the same thing as an AI model. The agent is the system coordinating the task. Models provide reasoning or generation capabilities. Tools provide ways to interact with the development environment. Your workspace provides project-specific context.
Your workspace stays central
A local AI agent can operate around the development environment you are already using, making the workspace itself an important part of the agent's context.
Context can be controlled
Instead of assuming that an AI system should have unrestricted access to everything, WhoseDev is designed around controlling what project information becomes available to AI features.
Models can be separated from workflow
The agent experience is not the same thing as a single AI model. The agent can provide the workflow while the configured models and providers supply the intelligence used for individual tasks.
Developer control remains important
Local or agentic does not mean automatic. Developers still decide what the system can access, what changes are acceptable, and what ultimately becomes part of the codebase.
An agent is only useful when it has the right context.
A development agent needs more than a prompt. It may need to understand relevant project files, existing patterns, APIs, dependencies, configuration, and the boundaries of what it is allowed to do. WhoseDev is designed around making those capabilities part of a controlled development workflow.
Codebase awareness
An agent can reason about a development request in the context of the workspace information made available to it instead of treating every prompt as an isolated piece of text.
Multi-file work
Larger engineering tasks often involve several files. Agent workflows are designed to help coordinate those changes instead of forcing you to manually prompt for every individual edit.
Structured execution
Rather than jumping immediately from prompt to output, the agent can approach a task as a sequence of actions that can be understood and reviewed.
Tool-aware workflows
Depending on your configuration, the agent can work with the tools and capabilities exposed to it by the WhoseDev environment.
Less prompt-by-prompt work.
More focus on the outcome.
Developers should be able to communicate what they want to achieve without manually translating every engineering task into dozens of tiny instructions. Agentic workflows make it possible to work at a higher level while still keeping implementation visible and reviewable.
Work at the task level
Describe an objective instead of manually converting every step into separate prompts.
Keep the developer involved
Agentic workflows do not remove the need for engineering judgment. You remain responsible for reviewing and accepting changes.
Use your existing workflow
The agent is intended to complement your editor, codebase, models, and development process rather than replace them.
Make complex work easier to reason about
Breaking a larger task into visible stages makes it easier to understand what the system is attempting to accomplish.
Start with what you actually want done.
You do not have to speak to an agent like a programming language. Explain the objective in the way you would explain the task to another developer, then let the agent help turn that objective into implementation.
Fix a bug
"Fix the authentication flow"Give the agent the outcome you want. It can help reason about the relevant implementation, plan the required changes, and work through the code involved.
Build a feature
"Add a team invitation system"Start with the feature objective instead of manually specifying every file. The agent can help translate the requirement into an implementation plan.
Refactor code
"Refactor this service layer"Give the agent a broader engineering objective and let it help identify the work required across the relevant implementation.
Understand a codebase
"Explain how authentication works"Agents can be useful for exploration as well as modification, helping developers reason about how different parts of a project fit together.
Add an API
"Add an endpoint for project members"Describe the expected behavior and let the agent help connect the implementation across the relevant API, models, validation, and supporting code.
Improve existing code
"Make this component easier to maintain"Instead of prescribing every edit, communicate the engineering outcome and use the agent to help work toward it.
AI should work with your development environment, not dictate it.
Agentic development introduces more capability, which makes control even more important. WhoseDev is built around giving developers visibility over how AI participates in their workflow.
Depending on your configuration, that can include choosing models and providers, controlling what context is available, defining what tools can be used, and deciding how agentic actions fit into your development process.
Model choice
Use the models and providers supported by your WhoseDev setup.
Context control
Control the project information made available to AI features.
Developer review
Review generated work and remain responsible for changes made to your codebase.
An agent is powerful.
It is not infallible.
Agentic systems can make development workflows more efficient, but they do not replace engineering judgment. Generated code can be incomplete, incorrect, or based on assumptions that do not match your project.
What the agent can help with
- • Understanding development objectives
- • Structuring larger coding tasks
- • Working across relevant implementation areas
- • Generating and modifying code
- • Exploring unfamiliar parts of a codebase
- • Repeating structured engineering workflows
What still belongs to the developer
- • Defining the desired outcome
- • Deciding what access and context are appropriate
- • Reviewing generated changes
- • Testing important behavior
- • Making final engineering decisions
- • Accepting or rejecting changes
A note on agent capabilities
Agent behavior depends on the version of WhoseDev, the configured model, available tools, workspace permissions, network configuration, and other settings. AI-generated changes can be incorrect, so developers should review generated code and validate important changes before relying on them.
Give your development work
a better starting point.
Explore WhoseDev and see how an agent can help you move from high-level intent to structured implementation while keeping you in the driver's seat.