Most teams are already using AI to autocomplete code. Very few are using agentic AI that can actually ship features. Agentic AI does more than answer prompts. It reads tickets, creates a plan, edits your repo, runs tests, opens pull requests, and sometimes even wires itself into your CI and deployment pipeline. When you pair that with a sane review process and guardrails, you can move from “AI as a suggester” to “AI as a junior engineer that never sleeps.” In this article, we’ll explore the top seven agentic AI tools that write, test, and deploy your full-stack features.
In this guide you will see seven practical agentic AI tools that can help you write, test, and deploy full stack features with less grind and more control.
Quick Comparison Of 7 Agentic Ai Dev Tools
Use this table as a fast map before you dive into the details.
| Tool | Core Superpower | Where It Works Best | Typical Workflow Impact |
|---|---|---|---|
| Devin | End to end AI software engineer for real world tickets | Teams with active backlogs in Git repos | Takes tickets, plans work, edits code, runs tests, opens PRs |
| OpenHands | Open source dev agent you can self host | Engineering teams that want full control and on prem | Reads and edits repos, runs commands, automates multi step tasks |
| GitHub Copilot Agent Mode And Agent HQ | Agentic layer inside GitHub and IDE | Teams already living in GitHub | Plans changes, edits code, interacts with MCP tools, helps with review |
| OpenAI Codex With AgentKit | Cloud coding agent plus workflow builder | Teams building custom dev agents and pipelines | Runs as CLI or IDE helper, connects to visual workflows, plugs into CI |
| Leap | AI dev agent that builds and deploys apps to your cloud | Startups and teams that want “idea to hosted app” fast | Scaffolds apps, integrates services, deploys to AWS or GCP |
| Firebase Studio With Gemini | Web workspace for AI assisted full stack apps | Product teams building web and mobile on Firebase | Designs APIs, backends, and frontends with AI help in one place |
| AWS Frontier Agents And Kiro | Enterprise scale coding and DevOps agents | Large orgs already running on AWS | Triages bugs, improves coverage, automates incidents and optimizations |
Now let us look at what “agentic” really means in practice and how each tool fits into a full stack workflow.
What Agentic Ai Actually Means For Full Stack Features
The key difference between a standard AI assistant and an agentic AI is initiative.
A regular coding assistant waits for prompts. You ask for a function, it writes a function.
An agentic AI can:
- Read a ticket or issue and break it into tasks
- Explore your repo to understand current architecture
- Decide which files to edit and in what order
- Run tests and linters, inspect failures, and retry
- Interact with external tools, for example CI or deployment scripts
You still keep humans in the loop for review and approvals, but the agent handles large parts of the boring glue work between “spec” and “merged into main.”
When you pick the right tools and plug them into your stack, you get:
- Faster movement from idea to prototype
- Better test coverage on routine work
- Less context switching for senior engineers
- A clearer path to fully automated pipelines for known patterns
The tools below all aim at that workflow, each with a slightly different angle.
Devin: Ai Software Engineer For Real World Tickets
Devin presents itself directly as an AI software engineer. It connects to your issue tracker and code hosting, then treats tickets as work units it can complete end to end.
Typical Devin flow looks like this in practice. Agentic AI Tools That Write, Test, and Deploy Your Full-Stack Features
- You assign a ticket to Devin from tools such as Jira, Linear, or Slack.
- Devin proposes a plan that lists files to inspect, steps to take, and tests to run.
- Once you approve, Devin edits the codebase in a cloud environment, running the app or tests as needed.
- It opens a pull request with a summary of changes and test results.
For full stack work, Devin can jump between backend, frontend, and infrastructure files as long as those live in reachable repos. That makes it useful for tasks such as:
- Implementing new API endpoints plus the corresponding UI components
- Refactoring shared models across client and server
- Fixing bugs that require understanding how data flows through the whole system
It works best when you feed it well written tickets and maintain a stable test suite. Treat it like a powerful junior engineer that always follows process but still needs code review from humans.
Openhands: Open Source Dev Agent You Can Self Host
OpenHands, formerly known as OpenDevin, is an open source platform for autonomous developer agents. Instead of living behind a closed SaaS interface, it gives you a controllable agent that you can inspect, extend, and run inside your own environment.
At a high level, OpenHands can:
- Read and navigate large codebases
- Edit files across repositories
- Execute shell commands and scripts
- Run tests and report failures
- Use tools such as browsers and documentation loaders
For full stack teams this is appealing when:
- You have strict data residency or compliance requirements
- You want to integrate agents deeply into custom systems
- You prefer to tune prompts and tools rather than rely on a fixed product
OpenHands is great for automating recurring chores such as dependency upgrades, codebase wide refactors, or repetitive feature scaffolding. Since you host it yourself, you can also sandbox it tightly, restrict what it can access, and monitor every action.
Github Copilot Agent Mode And Agent Hq: Agentic AI Tools That Write, Test & Deploy Full-Stack Features
GitHub Copilot started as inline code completion. Agent mode and Agent HQ move it into agentic territory.
Inside the editor, agent mode can:
- Read your project context and open files
- Plan multi step changes rather than single edits
- Call tools via the MCP ecosystem for tasks such as running tests or schema migrations
- Help you review code, not only write it
Agent HQ on GitHub side gives you a central place to work with multiple coding agents. It lets you:
- Assign issues directly to agents
- Track progress on automated tasks
- Compare different agents on the same problem if you want to evaluate them
For full stack features, Copilot fits naturally in teams that already live in GitHub pull requests and issues. You can imagine flows such as:
- Use Copilot chat to draft a design
- Ask agent mode to implement the change across backend and frontend
- Trigger tests through tools it can call
- Use Copilot again to summarize the pull request and surface risky spots
Because everything happens in familiar tools, adoption friction is low. The main work is designing good policies about when agents may push changes and how review works.
Openai Codex And Agentkit Workflows
OpenAI’s Codex is a coding focused agent that runs across CLI, IDE, and cloud. AgentKit is a platform to build, deploy, and monitor agent workflows. Together they give you a flexible way to turn your full stack development process into repeatable flows.
Think of one layer as the “hands” that read and write code, and another as the “conductor” that decides which steps to run in which order.
Examples of full stack flows you can build.
- When a new feature branch is created, spin up a Codex sandbox, run code quality checks, and propose test additions.
- For a bug report, have an agent reproduce the issue, bisect relevant commits, and suggest a patch.
- For known patterns, such as adding a CRUD endpoint, let Codex scaffold handlers, tests, and front end forms, then have AgentKit call CI and report status.
Because these tools are model agnostic and workflow oriented, they work well for teams who want to design custom pipelines rather than adopt a single opinionated app.
Leap: Idea To Deployed App In Your Own Cloud: Agentic AI Tools That Write, Test & Deploy Full-Stack Features
Leap focuses directly on building and deploying applications, not only editing existing repos. Its promise is simple. Describe what you want, let the AI generate the app, then deploy to your own AWS or GCP account.
This makes Leap interesting for:
- Spinning up internal tools that do not justify weeks of human time
- Quickly testing new product ideas with real, hosted prototypes
- Creating reference implementations for patterns you use often
In a typical flow, you would:
- Provide a high level spec for the app, including data model and key screens.
- Let Leap generate a full stack codebase that follows those requirements.
- Review the code, adjust structure where needed, and regenerate modules if required.
- Use the built in deployment hooks to push the app to your cloud.
Because you own both the code and the infrastructure, you keep control over security and long term maintenance, while still letting the agent handle most of the grunt work.
Firebase Studio With Gemini For Web And Mobile Apps
Firebase Studio combines Firebase’s backend platform with AI support to help you design, build, and iterate on full stack applications in the browser. It uses Gemini models under the hood to assist with:
- Defining data models and security rules
- Building APIs and serverless functions
- Scaffolding web and mobile frontends that talk to those backends
If your stack already leans on Firebase for authentication, database, and hosting, Studio can feel like a natural extension where you:
- Sketch out new features in plain language
- Let the AI propose data structures and endpoints
- Generate starter UI components that interact with your data
- Deploy to Firebase hosting without leaving the workspace
For small teams and startups, this reduces the friction between “we should test this feature” and “here is a working build in production.” It is especially helpful when not everyone on the team is a senior backend engineer.
Aws Frontier Agents And Kiro For Enterprise Pipelines
AWS’s frontier agents are positioned as autonomous systems that can run for hours or days with minimal supervision. For development and DevOps, Kiro and related agents focus on:
- Automating coding tasks and improving test coverage
- Reviewing designs and pull requests with a security lens
- Mapping infrastructure and resolving incidents in live environments
If your full stack apps already live on AWS, these agents can:
- Watch logs and metrics to spot anomalies early
- Suggest infrastructure changes based on real usage
- Execute predefined runbooks during incidents
- Work alongside humans during complex debugging sessions
You can pair them with more coding focused agents so that one layer proposes and implements app changes while another layer ensures reliability and security stay under control. This ecosystem suits organizations that already have strict governance and want AI to fit inside those walls.
How To Choose The Right Agentic AI Tool For Your Team: Tools That Write, Test & Deploy Full-Stack Features
You do not need all seven tools at once. Start with the problems that hurt the most.
Ask yourself a few simple questions. Agentic AI Tools That Write, Test & Deploy Full-Stack Features
- Where do features get stuck today, design, coding, testing, or deployment
- Which parts of your stack change most often, backend, frontend, infra, or all of the above
- How sensitive is your code and data, do you need on prem or strict region controls
- How comfortable is your team with new interfaces, CLI, IDE, or web platforms
Then match categories.
- If you want an “AI teammate” for existing repos, start with Devin, OpenHands, or Copilot agent mode.
- If you want to design reusable workflows that plug into many tools, look at Codex plus AgentKit.
- If you want full apps from spec to deployment in your own cloud, explore Leap or Firebase Studio.
- If you run heavy workloads on AWS and care about resilience, experiment with frontier agents and Kiro for DevOps and security work.
Begin with a pilot on a narrow type of task, for example bug fixes in one service or small internal tools. Measure real outcomes such as time saved, test coverage, and cycle time, not only how impressive the demos look.
Guardrails So Your Agent Does Not Ship Chaos
Agentic AI can move fast, which is great until it points that speed at the wrong folder or environment. Put a few non negotiable rules in place.
- Never give agents direct write access to production without some human approval step.
- Use separate sandboxes or branches where agents can experiment safely.
- Limit which commands agents may run on your systems and log everything.
- Keep your tests reliable and fast, since agents depend heavily on them for feedback.
- Treat pull requests from agents like PRs from junior developers, review them with care.
Handled this way, agents become force multipliers instead of new sources of risk.
Bringing Agentic Ai Into Your Full Stack Workflow
The shift from autocomplete to agents is less about replacing developers and more about changing which work humans do.
Let the Agentic AI Tools That Write, Test & Deploy Full-Stack Features:
- Digest long tickets and set up scaffolding
- Wire together boilerplate across layers
- Run repetitive tests and linting on every change
Let humans:
- Define requirements and boundaries
- Design architecture and data flows
- Review changes, name trade offs, and mentor the system
Pick one agentic tool, one project, and one metric. Run a real experiment instead of only reading case studies. You may find that the combination of human judgment and patient AI automation is exactly what your team needed to finally ship full stack features as fast as stakeholders imagine them.
