Coding is one of the domains where AI agents genuinely shine — not because writing code is easy for an LLM, but because code has something rare among agent tasks: a built-in, objective way to check if the work actually succeeded. Tests pass or they don't; code runs or it throws an error. That verifiability is what makes coding agents both practical to build and unusually reliable compared to more open-ended agentic tasks. Here's what actually goes into one.
Why Coding Is a Natural Fit for Agents
As covered in the autonomous agents post, agents work best in domains with verifiable outcomes and reasonable reversibility. Code checks both boxes: running a test suite gives immediate, objective feedback, and a bad code change is usually easy to revert. This lets a coding agent operate in a genuine feedback loop — write code, run it, see what breaks, fix it — without needing a human to manually evaluate every intermediate step.
The Core Tools a Coding Agent Needs
File read/write access. The ability to view existing code and write new or modified files — the most basic requirement for any coding task.
Code execution. Running scripts, tests, or the application itself to observe actual behavior, rather than the agent's LLM merely guessing whether code would work.
A test runner. Executing an existing test suite (or writing new tests) to verify that changes actually achieve the intended behavior without breaking anything else — this is the verification signal that makes the whole feedback loop work.
Search across the codebase. Finding relevant files, function definitions, or usages — since most real coding tasks require understanding existing code, not just writing new code in isolation.
Version control operations. Creating commits, branches, or diffs, letting changes be reviewed, tracked, and reverted — connecting to the reversibility point that makes coding agents relatively safe to operate with meaningful autonomy.
The Basic Agent Loop for Coding
- Understand the task. Parse the user's request — fix a bug, add a feature, refactor a function — and gather relevant context: which files are involved, what the existing code looks like, what tests already exist.
- Plan an approach. Especially for non-trivial tasks, break the work into a sequence of smaller steps rather than attempting everything in one large, unverified change — connecting directly to the chain-of-thought principles covered earlier in this series.
- Make a change. Write or modify code based on the current step of the plan.
- Run tests or execute the code. Check whether the change actually works as intended.
- Evaluate the result. If tests pass, move to the next step or conclude the task. If something fails, use the error output as new information and revise the approach.
- Repeat until the task is complete — or until a reasonable stopping condition is reached, like a maximum number of attempts, to avoid the agent looping indefinitely on an unsolvable failure.
Why Test-Driven Feedback Matters So Much Here
This loop only works well because of the verification step. Without a way to check whether code actually works, a coding agent is just generating plausible-looking code with no signal for whether it's correct — reintroducing exactly the hallucination risk covered earlier in this series, just applied to code instead of prose. A coding agent with access to a real test runner can catch its own mistakes and iterate toward a genuinely working solution, rather than confidently presenting broken code as finished.
Context Management Is a Real Challenge
As covered in the context engineering post, a coding agent working within a large codebase can't reasonably fit the entire project into its context window. Effective coding agents need deliberate strategies for this:
- Targeted file retrieval — pulling in only the files actually relevant to the current task, rather than the whole repository
- Code search tools — letting the agent find relevant definitions or usages on demand, rather than needing everything pre-loaded
- Summarization of prior steps — condensing earlier parts of a long task so the agent retains the important decisions made so far without the context window filling up with every intermediate detail
Safety Considerations Specific to Coding Agents
Sandboxed execution. Running agent-generated code in an isolated environment, separate from production systems, limits the damage a flawed or malicious code change could cause — directly echoing the privilege limitation principle from the prompt injection prevention post.
Human review for consequential changes. Even a highly capable coding agent benefits from human review before changes are merged into a shared codebase or deployed to production — an application of the human-confirmation principle covered in the same post.
Careful handling of external, untrusted input. A coding agent that reads external content — a bug report, a linked issue, a file from an untrusted source — is exposed to the same indirect prompt injection risks covered earlier in this series, and needs the same structural safeguards: clear separation of trusted instructions from untrusted content it's merely processing.
The Bottom Line
Coding agents are one of the more mature, practical applications of agentic AI specifically because code offers a rare, genuine feedback signal: tests either pass or they don't. A well-built coding agent combines file access, code execution, test running, and codebase search into a loop of writing, verifying, and revising — with sandboxing, human review for consequential changes, and careful handling of untrusted input keeping that autonomy appropriately bounded, even as the agent handles an increasing share of the actual implementation work.