An AI agent that writes code autonomously — reading requirements, generating implementations, running tests, and iterating on failures — is one of the most practically valuable agent applications for software teams. Unlike a simple code generation prompt, a coding agent closes the feedback loop: it sees whether the code it wrote actually works, reads the error output when it does not, and revises until the tests pass or the task is complete. This guide covers how to build a coding agent, the tools it needs, how to handle the write-run-debug loop, and the practical constraints that determine where it works well.
What a Coding Agent Does Differently
The difference between asking an LLM to write code and running a coding agent is the feedback loop. A standard code generation prompt produces code that may or may not work — you find out by running it yourself. A coding agent runs the code, reads the output, and decides what to do next based on whether it worked. This loop continues until the code passes, the agent gives up, or a maximum step count is reached. The agent handles the mechanical work of the write-run-debug cycle; you handle the requirements and the final review.
This feedback loop matters most for non-trivial code where getting it right the first time is unlikely. For a simple utility function, code generation without a loop is fine. For a feature that touches multiple files, requires specific library behaviour, or has edge cases that only appear at runtime, the agent’s ability to observe real execution output and revise accordingly produces significantly better results than a single-shot generation.
The Tools a Coding Agent Needs
A minimal coding agent needs four tools to operate effectively:
- Write file — creates or overwrites a file with specified content. The agent uses this to write initial implementations and revisions.
- Read file — reads the current content of a file. Used to read existing code before modifying it, and to verify that a write succeeded.
- Run command — executes a shell command and returns stdout and stderr. Used to run tests, execute the code, check syntax, or run linters.
- List directory — shows the files in a directory. Used to understand the project structure before making changes.
Optional but valuable additions: a search tool that finds files matching a pattern or containing a string (for understanding where to make changes in a large codebase), and a git tool that shows recent changes or creates commits after successful implementations.
Figure 1 — Coding agent: the write-run-debug loop
The System Prompt for a Coding Agent
The system prompt shapes the agent’s behaviour across the entire coding session. Key elements for a coding agent system prompt:
- Instruct the agent to read relevant existing files before writing any new code — this prevents the agent from generating code that conflicts with existing implementations
- Specify the language, framework, and coding conventions to follow
- Instruct the agent to run tests after every implementation and to fix failures before declaring the task complete
- Tell the agent to make minimal changes — modifying only the files necessary for the task, not refactoring unrelated code
- Specify how to handle uncertainty: ask a clarifying question rather than guess when requirements are ambiguous
The “read before writing” instruction deserves particular emphasis. Coding agents that dive straight into writing without reading the existing codebase frequently introduce inconsistencies — using different variable naming conventions, reimplementing functions that already exist, or ignoring established patterns. A small upfront investment in reading the relevant files produces substantially more coherent code.
Sandboxing the Run Command Tool
The run command tool is the most powerful and most dangerous tool in a coding agent’s arsenal. Without constraints, it can execute arbitrary shell commands — including ones that delete files, make network requests, or modify system state in ways that are hard to reverse. For production coding agents, the run command tool should be sandboxed:
- Run in a Docker container or VM that can be reset to a clean state if something goes wrong
- Restrict the network access of the execution environment — code that calls external APIs during development can be surprising
- Set resource limits — memory, CPU time, and disk write limits — to prevent runaway processes
- Log every command executed so you can audit what the agent did after the session
For local development use where you trust the agent’s task and the project it is working in, these constraints can be relaxed. But any coding agent running on production code or in a CI environment should operate in a sandboxed execution environment as a matter of policy.
Handling Test Failures Effectively
How the agent handles test failures determines much of its effectiveness on non-trivial tasks. A naive coding agent retries the same approach repeatedly when tests fail; a good one reads the error output carefully and changes strategy. The system prompt should instruct the agent to include the full error message and stack trace in its reasoning before revising, and to explain what it believes went wrong before making a fix. This explicit error analysis, visible in the thought trace, both improves the quality of fixes and makes debugging easier when the agent is stuck.
Set a maximum revision count — three to five revisions is usually the right range. If the agent has not produced passing tests after five attempts, it is usually stuck in a pattern that requires different information or a different approach rather than more iterations. At the revision limit, have the agent report what it tried and why it believes it is stuck, rather than silently stopping or looping further.
Task Scoping for Reliable Results
Coding agents produce their best results on well-scoped tasks with clear success criteria. “Add input validation to the user registration endpoint — email must be valid format, username must be 3–20 alphanumeric characters, and both fields must be present — and write tests that verify each validation rule” is a well-scoped task. “Improve the codebase” is not. The success criterion — tests passing — must be unambiguous and machine-verifiable. Tasks where success requires subjective judgment (is this code clean enough? does this UI look good?) are poorly suited to autonomous coding agents; tasks where success is determined by passing a test suite are the natural fit.
Reviewing Agent Output
An AI coding agent that produces passing tests has not necessarily produced correct code — it has produced code that passes the tests you specified. Tests that do not cover important cases, tests that the agent wrote itself (and may have written to be easy to pass rather than meaningful), and tests that pass but miss the intended behaviour are all real failure modes. Review agent-generated code with the same care you would apply to a junior developer’s pull request — check that the implementation is correct, not just that the tests pass, and verify that the tests actually test what they claim to test. The agent handles the mechanical write-run-debug cycle; human judgment remains essential for verifying that what passed the tests is actually what you wanted.
An AI coding agent is most valuable as a tireless first-pass implementer for well-defined tasks — it handles the mechanical work of getting code to pass tests so you can focus your attention on whether the tests themselves are right and whether the implementation fits the broader system. Used this way, it accelerates development on the class of tasks that benefit most from iteration: anything where getting it right requires multiple rounds of running and fixing, which in practice is most non-trivial software development.
Coding Agents for Data Analysis Tasks
A particularly effective application of coding agents is data analysis — tasks where the goal is to produce insights from data rather than deliver production code. The write-run-debug loop maps naturally onto exploratory data analysis: the agent writes a script to load and examine the data, runs it, reads the output, forms a hypothesis about what to explore next, writes another script, and iterates. This kind of exploratory iteration is time-consuming to do manually and well-suited to agent automation. The success criterion for data analysis tasks is less mechanical than for software tasks — “does this analysis answer the question?” requires human judgment — so the agent’s role is to produce the analysis code and results, with the human deciding whether the findings are meaningful and complete. For teams that regularly do ad hoc data analysis, a coding agent connected to a read-only database and a Python execution environment covers the majority of exploratory queries without requiring manual script writing for each one.
Combining Coding Agents with Code Review
The most effective workflow for coding agents pairs autonomous implementation with deliberate human review rather than treating agent output as ready to merge. A practical pattern: the agent implements and passes tests, opens a draft pull request, and the developer reviews the diff with the same scrutiny they would apply to a junior developer’s work. The review focuses on what the agent cannot verify — whether the implementation fits the broader architecture, whether the tests actually cover the important cases, whether the approach is maintainable, and whether any security-sensitive patterns were handled correctly. This combination preserves the agent’s speed advantage on implementation while keeping human judgment where it matters most. Teams that adopt this pattern typically see faster cycle times on implementation tasks without a reduction in code quality, because the human review time is spent on substance rather than mechanics.