Thomas Dohmke has spent much of his career close to the people who build software. Before he became widely known as the former CEO of GitHub, he was already working in the world of developer tools, mobile apps, and engineering productivity. That background matters because his newest company, Entire, is not a random jump into artificial intelligence. It feels more like the next step in a career built around one question: how can software teams build better, faster, and with less friction?
After leading GitHub through one of the most important shifts in modern software development, Dohmke has returned to startup life. His new company, Entire, is focused on a fresh problem that is becoming harder to ignore. AI agents can now write, edit, test, and explain code at a speed that would have sounded unrealistic only a few years ago. But the tools around software development were mostly built for human developers working through issues, branches, commits, pull requests, and code reviews.
That gap is where Entire is trying to build something useful. The company wants to create a developer platform for the AI agent era, where human engineers and AI coding agents can work together without losing context, intent, or accountability along the way.
Who is Thomas Dohmke
Thomas Dohmke is best known for his time as CEO of GitHub, but his story started long before that role. He grew up in Germany and developed an early interest in software, computers, and building tools. Over time, that interest turned into a career focused on helping developers do their best work.
Before taking the top role at GitHub, Dohmke co-founded HockeyApp, a mobile developer platform that helped teams test and distribute apps. Microsoft acquired HockeyApp in 2014, and Dohmke later became part of Microsoft’s broader developer tools world. That path eventually led him to GitHub, the platform used by millions of developers, startups, enterprises, and open-source communities around the world.
His career has been shaped by a clear pattern. He does not just build software products for end users. He builds tools for the people who build software. That is an important distinction. Developer tools need trust, speed, reliability, and a deep understanding of how engineers actually work. Dohmke’s experience at HockeyApp, Microsoft, and GitHub gave him a front-row seat to how software teams change when better tools enter the workflow.
Thomas Dohmke’s success at GitHub
When Thomas Dohmke became CEO of GitHub, the company was already one of the most important platforms in software development. But his leadership years came at a turning point. Coding was starting to change from a mostly manual craft into something deeply connected with AI assistance.
The biggest symbol of that shift was GitHub Copilot. What began as an AI coding assistant grew into one of the most talked-about products in the developer tools market. Copilot helped bring AI-assisted coding into the mainstream. It showed that developers could use AI not only for small autocomplete suggestions, but also for larger coding tasks, explanations, tests, and workflow support.
Under Dohmke’s leadership, GitHub became closely tied to the rise of AI-powered software development. The platform continued serving open-source communities, enterprise teams, students, and individual developers, while also becoming a central part of Microsoft’s AI developer strategy. That experience gave Dohmke a rare view of how developers actually respond to AI tools at scale.
It also exposed a deeper issue. AI can help write code, but writing code is only one part of software development. Teams also need to understand why code changed, who approved it, what assumptions were made, what constraints were followed, and whether the result can be trusted. Those questions become much more complicated when AI agents start producing large amounts of code.
Why Thomas Dohmke returned to startup life
Dohmke’s move away from GitHub was not framed as a retreat from developer tools. It looked more like a return to the kind of founder work that shaped the earlier part of his career. After years inside one of the world’s most influential software platforms, he stepped back into the startup world at a moment when the developer ecosystem was changing again.
That timing is important. The rise of AI coding agents has created a new kind of software workflow. Developers are no longer only asking AI for suggestions. In many cases, they are giving agents goals, asking them to inspect codebases, create changes, write tests, and complete multi-step engineering tasks.
This changes the job of the developer. Engineers still need judgment, architecture skills, security awareness, and product understanding. But they may spend more time reviewing, guiding, correcting, and coordinating AI-generated work. In that world, the old development workflow can start to feel stretched.
Entire appears to be Dohmke’s answer to that shift. Instead of building yet another code editor, AI model, or coding assistant, the company is trying to build the connective tissue around agent-driven software development.
What is Entire
Entire is a developer tools startup founded by Thomas Dohmke for the AI agent era. Its goal is to help software teams work with AI coding agents in a more structured, traceable, and collaborative way.
The company is not trying to replace every tool developers already use. It is also not trying to compete directly with AI coding agents by building another model. Instead, Entire is focused on the layer around the work. That means capturing code changes, prompts, reasoning, constraints, files touched, agent activity, and development context so teams can understand how a change came together.
That may sound technical, but the problem is simple. When a human developer makes a change, teammates can usually ask questions, read the pull request, inspect the commit history, and understand the thinking behind the work. When an AI agent creates a change, a lot of that thinking can disappear inside a temporary session. The code may remain, but the path that produced it can be lost.
Entire wants to make that path visible.
The problem Entire wants to solve
AI coding agents are powerful, but they create a new kind of mess. A developer can ask an agent to fix a bug, refactor a feature, add tests, or update documentation. The agent may touch several files, run commands, call tools, and make decisions based on the prompt and the context it sees.
At the end, the team may get a commit or a pull request. But the commit alone does not always explain enough. It may show what changed, but not why the agent made certain choices. It may not reveal what instructions were followed, which files were considered, what trade-offs were made, or where the agent may have guessed.
That is a real issue for engineering teams. Code review is already a bottleneck in many companies. If AI agents increase the volume of code, review can become even harder. Developers may face more pull requests, bigger diffs, and less confidence in the reasoning behind changes.
This is where Entire is positioning itself. The company wants to help teams keep the full story behind agent-generated code. Not just the final result, but the context around the result.
How Entire is building for human and AI collaboration
The central idea behind Entire is that software development is becoming a partnership between humans and AI agents. But partnerships only work when there is shared context.
Human developers need to know what an agent did. They need to know what the agent was asked to do, what files it touched, which tools it used, and how it moved from instruction to output. Without that context, developers are forced to review AI-generated work almost from scratch.
Entire is trying to make agent work easier to inspect. Its platform is designed to connect the output of coding agents with the development workflow teams already understand. That includes Git, commits, code history, and review processes.
This approach is practical because developers are unlikely to abandon their entire workflow overnight. Engineering teams already rely on Git-based systems, pull requests, CI pipelines, code owners, and release processes. A useful AI-agent platform needs to fit into that world while adding the missing layer of agent memory and reasoning.
Dohmke’s vision seems to be built around that balance. Entire is not saying humans no longer matter. It is saying the tools around humans need to change because AI agents are becoming part of the team.
Entire Checkpoints and why they matter
The first major product from Entire is Checkpoints, an open-source tool built to capture the context behind AI-agent work. It is designed to preserve information that would normally disappear after an agent session ends.
A checkpoint can include details such as prompts, transcripts, files changed, commands used, tool calls, token usage, and other traces of the work. By tying this context to Git commits, Checkpoints helps developers see more than a plain code diff.
That matters because software teams need trust. If an AI agent makes a change in a codebase, a reviewer should be able to understand the job it was given and how it completed that job. This is especially important for larger teams, regulated industries, enterprise software, and any product where reliability and security matter.
Think of it this way. Git tells a team what changed. Entire Checkpoints aims to help explain how and why the change happened. That extra layer could become valuable as more teams bring AI coding agents into daily development.
Why Git alone is not enough for the AI coding era
Git is one of the most important tools in software history. It is fast, reliable, distributed, and deeply trusted by developers. It does an excellent job of tracking file changes over time. But Git was not built to store the full reasoning process behind AI-generated code.
A traditional commit can show that a function changed, a test was added, or a dependency was updated. What it usually cannot show is the full AI session behind that work. It does not naturally preserve the prompt, the agent’s intermediate steps, the commands it ran, or the assumptions it made while completing the task.
That missing context becomes more important as agents do more work. If one developer writes ten lines of code, a teammate can review them quickly. If an AI agent generates hundreds of lines across several files, the reviewer needs more help. They need a trail that makes the work easier to trust, audit, and improve.
This is why Entire is focused on agent context. It is not trying to make Git irrelevant. It is trying to extend the development workflow around Git so it can handle a new kind of contributor.
Thomas Dohmke’s bigger vision for software development
The story of Thomas Dohmke and Entire is bigger than one startup launch. It reflects a wider shift in the software industry.
For years, developer tools were built around helping humans write code faster. Better editors, better package managers, better hosting platforms, better CI systems, and better collaboration tools all supported human developers. Then GitHub Copilot helped prove that AI could sit beside developers and assist them while they worked.
Now the industry is moving into a different phase. AI agents are not just suggesting code. They are taking on tasks, moving through codebases, and producing work that needs to be reviewed and merged. That creates a new need for coordination.
Dohmke seems to be betting that the next great developer platform will not only help people write code. It will help humans and AI agents work together safely, clearly, and at scale.
That is a meaningful bet because software development is not only about speed. A company can generate code faster and still create more confusion if the code is hard to review or trust. The real opportunity is not just more output. It is better coordination around that output.
Why Entire could matter for developers and engineering teams
For individual developers, Entire could make AI coding work feel less temporary. Instead of losing the history of a coding session, they could keep a structured record of what happened. That makes it easier to revisit a change, explain it to a teammate, or understand why an agent made a certain decision.
For engineering managers, the value could be even broader. Teams need visibility into how AI tools are being used. They need to know whether agents are improving productivity, creating review pressure, introducing risk, or repeating the same mistakes. A tool that captures agent context could help teams manage those questions with more confidence.
For enterprise teams, the need is even sharper. Large companies care about auditability, compliance, security, and repeatable processes. If AI agents become regular contributors to software projects, enterprises will need ways to track their work. They will not want important engineering decisions locked inside a chat window or scattered across temporary agent sessions.
That is why Entire is entering an important space. The future of coding may depend not only on how good AI agents become, but also on how well teams can manage them.
The challenge ahead for Thomas Dohmke and Entire
The opportunity is large, but the road will not be easy. The AI coding market is crowded and moving quickly. OpenAI, Anthropic, Google, Microsoft, Cursor, and other companies are all shaping how developers use AI in their daily workflow.
That means Entire has to prove it is not just another layer of tooling. Developers are careful about adding new tools to their stack. If a product creates friction, it will be ignored. If it fits naturally into the workflow and solves a real pain, it can become important very quickly.
The challenge for Entire is to make agent context feel essential. Developers need to feel that saving prompts, transcripts, reasoning, and tool activity is not busywork. It has to make code review better, debugging easier, teamwork smoother, and AI-generated code safer to trust.
Dohmke’s background gives the company credibility. He has led one of the most influential developer platforms in the world. He has seen how engineers adopt new tools and how developer habits change over time. But even with that experience, Entire will need to earn trust one team at a time.
What Thomas Dohmke’s journey says about the future of developer tools
Thomas Dohmke’s move from GitHub to Entire says a lot about where developer tools may be heading. The last major shift was about AI helping developers write code. The next shift may be about helping teams manage software work produced by both humans and AI agents.
That future will need more than faster code generation. It will need better memory, clearer workflows, stronger review processes, and tools that make agent-generated work easier to understand. It will need platforms that treat AI agents as active participants in software development, while still keeping humans in control of quality, direction, and judgment.
This is the space Entire is trying to define. By focusing on context, traceability, and agent-human collaboration, Dohmke is building for a world where software teams may produce more code than ever before, but still need the same thing they have always needed: clarity.
The success of Entire will depend on whether developers see this problem as urgent. But the timing makes sense. AI agents are moving into real engineering workflows, and the tools around them are still catching up. If Entire can become the place where code, context, prompts, reasoning, and review come together, it could play an important role in the next chapter of software development.