From Jira to PR. Claude-Powered AI Agents that Code, Test, and Review for You

TL;DR on Claude-Powered Dev Agents: What You Need to Know

• AI Agents Evolve Beyond Copilot: Coding AI has progressed from simple autocompletion (e.g., Copilot) to autonomous agents like AI Teammate that code, test, review, and operate tools independently – acting as true junior developers.
• Claude’s Strength: Anthropic’s Claude models (4.0, 3.7 Sonnet) excel due to advanced reasoning, minimal hallucination, and large context handling, outperforming alternatives like OpenAI and Gemini in these production tasks.
• End-to-End Development Automation:
  • Reads Jira tickets and Git issues, understands tasks, and plans changes.
  • Clones repos, maps codebases, implements incremental changes with aider.
  • Runs linters and tests, self-corrects until everything passes.
  • Pushes code, opens PRs, and maintains context without human intervention.
• Beyond Code Writing: AI Teammate autonomously responds to PR feedback comments, updates documentation, and generates tests. Failed tests or reviewer suggestions trigger seamless iterations, with updates pushed in clean, traceable branches.
• Productivity Impact: Freed from repetitive coding and testing tasks, developers focus on high-impact features and architecture. Non-technical team members can trigger code changes by creating tickets, closing the gap between planning and implementation.
• Deployment & Architecture: FastAPI server on Google Cloud Run, integrated via webhooks with Jira and Git. Uses aider for semantic repo mapping and PyGitHub/python-gitlab SDKs for full repo operations.
• Key Takeaway: Claude-powered agents are not gimmicks; they deliver production-grade development workflows autonomously. When embedded into robust orchestration systems, they scale teams efficiently, acting like reliable junior developers ready to boost delivery speed and ROI.
• Bottom Line: AI coding agents like AI Teammate unlock real productivity gains, enabling companies to build and ship faster without expanding headcount proportionally.

The Rise of Real AI Dev Agents

In just a few years, the role of AI in software development has transformed significantly. What started as simple code autocompletion tools, like GitHub Copilot, has evolved into a new generation of autonomous coding agents capable of handling development tasks end-to-end . These agents do not just assist developers — they reason, make decisions, and operate independently across the tools used by teams — like Jira or GitHub.

Among the large language models behind this shift, Anthropic’s Claude stands out. Known for its advanced reasoning capabilities, strong safety alignment, and ability to handle complex development contexts, Claude has become the leading choice for high-stakes business use cases.

At deepsense, we have placed Claude at the core of AI Teammate — our AI coding agent built to work with the project board and codebase. Acting as a true member of the engineering team, AI Teammate can interact with Jira tickets, write code, address pull request comments, and generate tests and documentation. Claude 4.0 and Claude 3.7 Sonnet have proven to be our most reliable partners in practical development scenarios.

From Ticket to Pull Request – No IDE Required

One of the most powerful aspects of using Claude in our solution is how seamlessly it manages the entire development workflow, from a Jira ticket or Git issue to a ready-to-review pull request, all without requiring human-in-the-loop coding.

Here is how Claude-powered AI-Teammate works in practice:

1. Understand the task
   Claude-powered agent begins by reading and interpreting the description of the ticket assigned to him in the Jira or Git board.
2. Inspect the codebase
   It autonomously clones the corresponding repository and inspects the codebase to understand the project structure and dependencies.
3. Prepare the implementation plan
   Before starting the implementation, it prepares a plan to identify which files need to be created or modified, and outlines the proposed changes for each of them.
4. Implement the changes
   Using the aider framework, Claude implements the changes by adding or modifying code in the chosen files. It works incrementally, with structured reasoning at each step.
5. Verify changes with retries if needed
   It runs linters and tests to validate the changes. If anything fails, Claude does not stop — it adjusts the implementation and reruns checks until everything passes.
6. Push changes and open a PR
   Once the work is complete, the agent pushes the updated branch and opens a pull request. The PR title and description are based on the details fetched from the original ticket to maintain context and traceability.

The entire pipeline is triggered automatically whenever a new ticket is created and assigned to the AI Teammate. This is enabled by automation rules and webhooks configured directly within the Jira or Git project. All of this happens without opening an IDE or writing a single line of code manually. Developers can stay focused on their work while Claude handles its tasks independently, end-to-end.

Beyond Code: PR Feedback, Tests, Docs — Handled

Claude goes far beyond code generation. Once AI Teammate is assigned to a pull request, it continuously monitors for reviewer comments and responds autonomously. Whether it is a general suggestion or an individual inline comment, the agent processes it, applies changes, and updates the PR — automating the feedback loop for small, iterative fixes.

Claude also handles test generation and documentation updates, ensuring that each change is fully validated and properly explained. If generated tests fail, it iterates — fixing code or test logic and retrying until everything passes. To keep workflows clean and traceable, it submits the final suggestions in a separate branch. The original PR is mentioned in the description, and the reviewer of the original PR is assigned as the reviewer of the new one.

Productivity & ROI: What Changes

Integrating our AI Teammate, powered by Anthropic’s Claude, into our development workflow has delivered real benefits in our productivity and efficiency. By automating routine coding tasks — such as implementing repetitive code and generating tests — our developers can now dedicate more time to high-impact features, architectural work, and innovation. 

And yes, we used AI Teammate to build AI Teammate. Our typical workflow involved using the agent to draft initial versions of the changes, which were then reviewed, refined, and improved by developers. This collaborative approach enabled us to prepare a ready-to-use solution in just two weeks.

Project managers and other non-technical people can now initiate code-level changes simply by creating or commenting on tickets. They do not need to write any code or explain technical details to developers. This speeds up collaboration and closes the gap between planning and implementation.

How It Works – Under the Hood

AI Teammate is a FastAPI-based server application, containerized with Docker and deployed on Google Cloud Run for scalable, public access. The server exposes a dedicated endpoint for handling new Jira or Git tickets. 

Triggering the Flow: From Ticket to Task

Each time a ticket is assigned to AI Teammate — either in Jira or Git board — automation rules fire a webhook. The webhook sends structured payloads to the dedicated API endpoint, including custom headers to identify the Git provider (e.g., GitHub or GitLab), the repository path, and optional parameters. The backend handles these asynchronously, allowing the Claude-powered implementation flow to begin in the background, without blocking user interaction.

The automation rule is configured to wait for a successful response from the webhook. If 201 Created status is returned, the ticket is automatically moved to the In Progress column. Otherwise, the rule execution is stopped.

Repo Mapping & Planning

The first step in the implementation pipeline involves identifying the correct Git repository and cloning the latest version of the main branch into a temporary working directory. From here, the aider framework is used to generate a semantic map of the codebase, cataloging key files, class definitions, function signatures, and other symbols. This map serves as a guide for Claude — giving it a high-level architectural overview.

The agent then prepares the implementation plan, combining details from the ticket and the repository map. It outlines which files need to be modified or created, and provides reasoning for each change. The plan becomes a part of the system prompt injected into Claude, along with strict coding instructions covering clean code principles, performance considerations, and avoiding shortcuts like TODOs or commented-out sections.

Powered by Claude – Intelligent, Iterative Code Generation

When it is time to write or edit code, the LLM takes over. While several models were evaluated, including OpenAI’s and Gemini, the best results came from Anthropic’s Claude 4.0 and Claude 3.7 Sonnet, especially in handling large context windows and iterative reasoning tasks. These models consistently delivered strong performance on complex development tasks with minimal hallucination, making them ideal for this kind of automation.

To ensure production-ready, high-quality code, all implemented changes are validated through a retry loop. Linters and tests defined in the repo are executed after each iteration. If errors are detected, Claude receives structured feedback and tries to resolve the issues automatically.

Delivering the Code

Once the implementation is finished, the Claude-powered agent creates a new Git branch based on the main one, commits the changes, and pushes them to the remote repo. A pull request is then opened automatically, with Claude assigned as the PR author and the original ticket reporter set as the reviewer. The PR title and description are obtained directly from the original ticket to maintain context and traceability. Additionally, a label is added to indicate that the changes were generated by Claude.

All interactions with Git, including creating branches or pull requests, committing, and pushing changes, are handled through specific SDKs: PyGitHub for GitHub and python-gitlab for GitLab.

Key Takeaways – Agents as Scalable Dev Resources

Claude goes far beyond simple chatbots – it acts like a junior developer seamlessly integrated into your software development toolchain. For tech companies across Europe, the US, and APAC, when combined with a robust orchestration system like AI Teammate, Claude can handle production workloads autonomously and reliably. 

Our Claude-powered agent demonstrates a proven, scalable approach that engineering and DevOps teams can adopt to accelerate delivery, reduce operational costs, and efficiently grow their development capacity without increasing headcount. Companies using Claude for AI coding automation are gaining a competitive edge by shipping high-quality code faster while keeping their teams focused on innovation.