Key takeaways
- Agent-based software development platforms range from coding workspaces to systems that coordinate agents across engineering workflows.
- Teams should assess how agents access code, tools, project context, and approval steps before assigning them larger tasks.
- Human review, testing, and permissions remain important even when agents can work independently.
- Platforms differ in whether they focus on individual developers, shared team environments, or organization-wide operations.
- Port leads this list for teams that want to connect agent activity to engineering context, workflows, and governance in one platform.
AI agents are moving from code suggestions to longer development tasks: exploring repositories, changing files, running tests, and preparing work for review. The platforms in this list take different approaches to that shift. Some provide a shared environment for coding agents; others focus on autonomous execution, an integrated development workspace, or organization-wide context and controls.
The right choice depends on where your team needs agents to work and how much oversight those agents need. This list considers coding workflows, integration with existing tools, support for team use, and the ability to review or govern agent activity.
Top platforms for agent-based software development
1. Port
Port takes an organization-level approach to agent-based software development. Rather than centering its product on a single coding agent or editor, it provides a platform for connecting agents to engineering context, workflows, and governance. Its Context Lake brings data from engineering tools into a queryable layer, helping developers and agents work with information such as service ownership, dependencies, and standards.
Teams can use Port’s MCP server to let agents query that context and invoke workflows. Port also supports custom agents, agent registries, orchestration, and governance controls. This makes the platform relevant when agents need to do more than edit code: they might need to request an environment, respond to an incident, or follow a defined remediation workflow.
Port ranks first because its scope addresses a team-level challenge in agent-based development: making agents useful across the software lifecycle while keeping actions visible and bounded. It can sit alongside the coding tools a team already uses, giving platform teams a place to define how agents access organizational context and carry out approved actions.
Relevant capabilities include:
- A context layer for engineering data
- Agent management and workflow orchestration
- MCP connections for agent access to context and actions
- Governance and human approval options
2. JetBrains Air
JetBrains Air is designed to bring different coding agents into a shared environment. Developers can use supported agents in JetBrains IDEs or through Air Gateway, while Air Teams provides a shared agentic environment and Air Governance gives managers policy controls. Air Context is intended to help agents work with codebase information.
That combination makes Air a workspace and management layer, rather than a single model or agent. Teams can use certain third-party agents alongside JetBrains tools, depending on configuration and provider requirements. The handoff between local work and remote sessions can be useful when a developer starts a task in an IDE and wants to continue it in a team environment.
Relevant capabilities include:
- Agent sessions in IDEs and a remote team environment
- Organization and team level policies
- Support for multiple agent providers
- Shared context and credit management
3. OpenAI Codex
OpenAI Codex supports software development tasks that can run in cloud environments, letting developers delegate work and review progress while other tasks continue. Teams can use it for implementation, testing, code review, and related work, with reusable environments tied to repositories. Codex also supports multi-agent workflows and can connect to tools through plugins.
The main distinction is its task-oriented approach: developers can assign work, monitor the agent, then inspect the changes and results. That model can help when a task is clear enough to delegate but still needs human review before it is merged. Codex can also be used across ChatGPT, an editor, and the terminal, depending on the available setup.
Relevant capabilities include:
- Cloud-based coding tasks
- Reusable repository environments
- Parallel agent workflows
- Code review support and tool integrations
4. Cursor Cloud Agents
Cursor Cloud Agents run development tasks in dedicated virtual machines with a repository and configured development environment. An agent can plan a task, edit code, run commands, and test changes, then report back. Because work runs remotely, developers can delegate longer tasks and return to the result later.
Cursor also provides artifacts such as logs, screenshots, or videos to help people inspect what an agent did. Teams can start agents from Cursor or supported web, mobile, and collaboration surfaces. Controls for repositories, secrets, and network access help administrators shape the environment available to each run.
Relevant capabilities include:
- Isolated virtual machines for agent runs
- Repository and environment configuration
- Testing and command execution
- Artifacts that support review
5. Kiro
Kiro combines an IDE for active development with web-based autonomous sessions. Developers can use structured specifications to shape work, give agents repository guidance, and use hooks or persistent knowledge to support recurring tasks. Kiro Web can run an agent in a cloud sandbox, where it can plan work, make changes, and open a pull request.
This approach emphasizes clearer requirements and project conventions. For teams that want agents to take on multi-step tasks, specs can help turn a broad request into a defined set of expectations. Autonomous mode allows an agent to carry out work with less step-by-step input, while the team can still inspect the resulting changes.
Relevant capabilities include:
- IDE and cloud-based agent sessions
- Specification-driven task planning
- Repository guidance and persistent knowledge
- Pull request creation from autonomous sessions
6. OpenHands
OpenHands is an open-source platform for building and running software development agents. It includes tools for interactive agent work as well as automation and controls intended to support use across an organization. Teams can use it to explore agent workflows while retaining more flexibility over how the system is deployed and configured.
The platform is relevant to teams that want to adapt an agent environment to their own engineering processes. Depending on the product setup, organizations can connect agents to codebases and task workflows, and build on the platform to support internal tools. That flexibility can also mean more responsibility for deployment, maintenance, security configuration, and operations than a fully managed development product.
Relevant capabilities include:
- Open-source agent platform
- Interfaces for interactive development tasks
- Automation and organization-level controls
- Potential for custom deployment and extension
7. Claude Code
Claude Code is a coding agent that works through a developer’s terminal and can also be used through supported desktop and team workflows. It can inspect a codebase, make changes across files, run commands, and handle multi-step programming tasks. The Claude Agent SDK gives developers a way to build custom agent experiences using related tools and permission frameworks.
This gives teams two paths: use Claude Code directly for engineering work, or build an application around agent capabilities. The command-line workflow can fit teams that already rely on terminals, scripts, and repository-level instructions. Enterprise controls and integrations may be available through the organization’s chosen Claude setup.
Relevant capabilities include:
- Terminal-based coding sessions
- Multi-file edits and command execution
- Custom agent development through the SDK
- Configurable permissions and team controls
8. Replit Agent
Replit Agent combines AI-assisted development with an environment for building, running, and deploying applications. Users can describe a software change or application, then work with the agent as it modifies a development copy and checks the result. Replit for Business also supports working with connected tools and existing codebases.
The integrated environment can reduce setup work for projects where development, execution, and deployment are closely linked. It can also make it easier for people outside traditional engineering roles to participate in building software. Teams should still evaluate how well the workflow fits their production systems, repository practices, and deployment controls, particularly when adapting the product to established codebases.
Relevant capabilities include:
- Agent-assisted application development
- Integrated development and deployment environment
- Support for existing codebases in business workflows
- A shared workspace for building and checking changes
9. Amp
Amp is a coding agent and development environment that supports both interactive work and longer-running tasks. Developers can use it through the web or local tools, and its agent can use subagents for research or parallel work. Amp’s “orbs” provide a remote environment where agents can set up, run, and snapshot projects, with results available for review.
That structure focuses on letting agents operate in a project environment rather than only returning code suggestions. Developers can inspect files and changes, run the application, and review work from different devices. Teams considering Amp should assess how its remote environment fits their repository setup and what permissions agents need to build and test software.
Relevant capabilities include:
- Web and local agent workflows
- Remote project environments called orbs
- Subagents for research and parallel tasks
- Review access to files and running applications
Where agents fit in the development workflow
Agent-based software development can support a range of tasks, but the amount of autonomy should match the consequences of an error. A useful starting point is to choose work that is repeatable and easy to verify.
- Low-risk, well-scoped tasks: test generation, documentation updates, and routine code changes.
- Cross-system tasks: environment provisioning, dependency updates, or incident follow-up, with clear permissions and logs.
- Higher-impact changes: production configuration, security-sensitive code, and release decisions, with explicit human review.
Teams can expand an agent’s responsibilities as they learn how reliably it follows project conventions and produces verifiable results.
Questions to ask before adopting an agent platform
Use these questions in a pilot with real repositories and representative tasks:
- What can an agent read or change? Check repository access, secrets, network access, and permissions for connected tools.
- How will reviewers verify its work? Look for diffs, test output, logs, and a clear record of actions.
- Can the platform use our engineering context? Check whether agents can access ownership, dependencies, standards, and workflow information.
- Which tasks need approval? Decide where an agent can proceed independently and where a person must approve an action.
- Who will own the setup? Identify the people responsible for integrations, policies, environment configuration, and ongoing review.
Frequently asked questions
What is agent-based software development?
Agent-based software development uses AI agents to carry out programming tasks through a sequence of actions. Depending on the platform, an agent may inspect a codebase, edit multiple files, run commands, execute tests, or prepare a pull request. Developers typically set the task and review the result, with the platform defining which tools and permissions the agent can use.
How is a coding agent different from a code assistant?
A code assistant commonly responds to a prompt with suggestions, explanations, or generated snippets. A coding agent can take a task through multiple steps, such as exploring files, editing code, and running tests. The boundary varies by product, and many platforms offer both interactive assistance and agent workflows. Teams should compare how much work happens automatically and how results are reviewed.
Do agent-based development platforms replace software developers?
These platforms support developers by handling parts of a workflow, but they do not remove the need for engineering judgment. People still define requirements, make architectural decisions, assess security and reliability, and approve changes where appropriate. Agents can make mistakes or misunderstand project context, so teams need tests, code review, permissions, and clear ownership for the systems they use.
How should a team evaluate an agent platform?
Start with a pilot using representative tasks in a noncritical repository. Compare how well each platform follows project conventions, accesses the right context, runs tests, and presents changes for review. Also examine repository permissions, data handling, integrations, deployment options, and administrative controls. Track both time saved and the effort required to correct or supervise agent output.
Can agent platforms work with existing developer tools?
Many platforms connect to existing code repositories, IDEs, terminals, or collaboration tools, but the integrations differ. Some run inside an editor; others create remote work environments or expose agent capabilities through APIs and MCP. Before adopting one, verify support for your actual repository host, CI system, identity controls, and review process, then test the complete workflow with your team.
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