Coding Agents 101: The Art of Actually Getting Things Done The year is 2025. Coding agents aren't magic, but they're about the closest thing we have. We've noticed some engineers, in particular at the senior-to-staff level, finding success faster than others. Here we share some top lessons sourced from the experience of our customers and ourselves. About this guide: Product-agnostic We discuss tips that will help you be successful with any coding agent. Tactical We offer our favorite bits of actionable advice. Technical While coding agents can be valuable to many, this guide is written with engineers in mind.
Developer tooling has been rapidly evolving. Ten years ago, it was autocomplete and intellisense, capable of suggesting method names and carrying out programmatic refactors. Four years ago, it was copilots and tab complete, capable of writing the next couple lines of code for you. Two years ago, it was generative chatbots, capable of assisting your development and generating entire files for you. Today, it is autonomous agents, capable of taking initial descriptions to final pull requests with little human intervention. We've focused on realizing this vision over the past two years by building Devin. Now, interest in autonomous agents is reaching new heights, especially with recent releases of similar products . These agents can appear in many forms, including web apps, mobile apps, and integrations within popular tools like Slack, GitHub, Linear, and Jira.
While a human paired with an AI assistant can achieve more than any AI alone, an autonomous agent's ability to handle tasks end to end allows for a new level of multi-tasking, turning every engineer into an engineering manager.
Adapting to working effectively alongside these new AI colleagues can take some time. Interestingly, we've observed that senior-to-staff level engineers tend to adopt and become proficient with these tools the fastest. Ultimately, these tools will become commonplace across all levels of engineering. Based on our experience and customer feedback, we want to share key insights and lessons learned to help everyone successfully integrate these tools into their workflows.
Prompting Basics These fundamental guidelines will help you effectively interact with coding agents in 2025. If you take nothing else away, you should at least remember these.
Say how you want things done, not just what Think of the agent as a junior coding partner whose decision-making can be unreliable. Simple tasks can be described directly, but for more complex tasks, clearly outline your preferred approach from the outset. Providing the agent with the overall architecture and logic upfront not only boosts its chances of success but also reduces your time reviewing code, as you will already be familiar with the intended method. Example: Instead of "add unit tests," specify the functionality to test, identify important edge cases, and clarify what needs mocking, if anything.
Tell the agent where to start Think about where you'd start if you were handling the task yourself. Even if you don't know specific file or function names, mention the repository, relevant documentation, and key components involved. Clearly indicating these elements minimizes wasted effort and confusion. Example: "Please add support for Google models to our code. You should look at the latest docs [here](link) and create a new implementation file in the model groups directory"
Practice defensive prompting Imagine giving the same prompt to a new intern. Where would confusion or errors likely arise? Anticipate these points and proactively clarify your instructions to avoid ambiguity. Example: "Please fix the C++ bindings for our search module to pass the new unit tests. Be careful, you will probably need to recompile the bindings each time you change the code before you test."
Give access to CI, tests, types, and linters Much of the magic of agents comes from their ability to fix their own mistakes and iterate against error messages. Providing strong feedback loops through tools like type checkers, linters, and unit tests greatly enhances their performance. Consider typed Python over plain Python, or TypeScript over JavaScript. Teach your agent how to run common checks and tests, ensuring it has all necessary packages and access rights. If the agent can interact with a browser, provide clear instructions on running your front-end development environment. Example: Our team transitioned from mostly untyped Python SDKs to exclusively typed SDKs (this is also a good task ideally for coding agents).
Leverage your expertise Everything above becomes easier when you're familiar with your codebase. Even simple tasks benefit from your ability to verify logic and results. Human oversight remains essential—ultimately, you hold responsibility for the final correctness of the code. Ownership and verification will continue to be critical responsibilities for human engineers, even as these tools become increasingly sophisticated.
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