AI Coding Agents in Practice: From Project Understanding to Autonomous Development

1. From Code Completion to Development Agents

Software development assistance tools have gone through roughly three stages. The first is inline completion, where the tool predicts the next piece of code based on nearby content. This is useful for reducing repetitive typing. The second is the conversational assistant, where developers give a question or code snippet to a model and receive an explanation or rewrite suggestion. The change still has to be pasted into the project manually. The third is the agent form, where the model not only generates content but can also read project files, execute commands, observe runtime results, and continue correcting its work based on feedback.

An agent typically involves three core parts:

• Perception: reading project structure, source code, configuration files, and runtime logs;
• Planning: breaking a natural-language goal into executable steps;
• Execution and feedback: calling editing, terminal command, and testing tools, then adjusting the next action based on output.

Understanding these three parts helps determine which tasks a tool can handle reliably. If a task only requires generating a single function, completion or chat is enough. If a task involves cross-module changes, repeated trial and error, and environment validation, the value of the agent form becomes apparent.

2. Runtime Forms and Permission Control

The terminal is a common runtime environment for these tools. A terminal can access the file system, version control, build scripts, and package managers, allowing an agent to complete the loop from modification to validation.

Common runtime forms include:

1. Command-line interface: started at the repository root, handling cross-file tasks and fitting into scripts and continuous integration workflows;
2. Editor plugin: showing code diffs inside the IDE so developers can review each change;
3. Desktop or web interface: suitable for discussing approaches, reading long documents, or assisting analysis when no local environment is available.

Regardless of form, permission control should come before feature use. The following principles are recommended:

• Grant read and write access only to the current project directory, avoiding access to the user’s home directory or system configuration;
• Keep human confirmation for operations such as deleting files, modifying dependencies, and making network requests;
• Work on a separate branch so every change can be rolled back through version control.

3. Long Context: The Basis for Project Understanding

The amount of text a model can process at once is called the context window, measured in tokens. A larger window lets the model see more files at the same time, but window size does not equal understanding quality. Information in the middle of long text is often harder to use accurately than information at the beginning and end. Practical agents therefore usually need additional mechanisms:

• Code indexing: building a symbol table that records where functions, classes, and interfaces are defined and how they are referenced;
• Dependency graph: parsing module import relationships to locate the impact scope of a change;
• Chunk retrieval: placing only the fragments relevant to the current task into context;
• Summary cache: generating brief descriptions for modules that have already been read, reducing repeated reading.

The following example uses the standard library to count module import relationships in a Python project. This is the first step in building a dependency graph:

import ast
from collections import defaultdict
from pathlib import Path
def build_import_graph(root: str) -> dict[str, set[str]]:
    graph = defaultdict(set)
    for path in Path(root).rglob("*.py"):
        module = ".".join(path.relative_to(root).with_suffix("").parts)
        tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path))
        for node in ast.walk(tree):
            if isinstance(node, ast.Import):
                graph[module].update(alias.name for alias in node.names)
            elif isinstance(node, ast.ImportFrom) and node.module:
                graph[module].add(node.module)
    return graph
if __name__ == "__main__":
    deps = build_import_graph("src")
    for mod, targets in sorted(deps.items()):
        print(mod, "->", ", ".join(sorted(targets)))

With a dependency graph, an agent can first find all modules that directly or indirectly depend on a module before modifying it, then include those files in context. This reduces the chance of missing related changes.

4. Typical Development Scenarios and Practices

1. Taking Over an Undocumented Legacy Project

Recommended steps: confirm the build method and runtime entry point, map module relationships, identify the core data flow, generate an overview document, and finally have an engineer verify the key conclusions one by one. Model-generated summaries may miss implicit conventions such as environment variables, scheduled tasks, or historical compatibility logic. The verification step cannot be skipped.

2. Incremental Refactoring

Refactoring should use small-grained changes, such as extracting functions, unifying interfaces, or replacing obsolete calls, and tests should run after each step. The more specific the task description, the easier the result is to verify. For example:

Change functions in the order module that access the database directly to call through a repository interface, keep public function signatures unchanged, and run all tests under tests/order when done.

This description states the scope, constraints, and acceptance criteria. Saying only “refactor the order module” makes it difficult for an agent to judge what counts as complete.

3. Debugging and Fault Localization

Provide error logs, reproduction steps, and relevant code, and ask the agent to first list possible causes and then design a verification method for each one. This approach helps avoid changes that look reasonable but have not been tested.

4. Test Completion

Generate test cases for functions with insufficient coverage, and require an explanation of which branch each case covers. Generated tests also need review to prevent assertions from being written incorrectly alongside a faulty implementation, creating the illusion that “the test passes but the feature is wrong.”

5. Operations Fixes

When configuration files or deployment scripts fail, an agent can compare historical versions, parse error messages, and suggest fixes. For operations involving production environments, it should output a plan only, to be executed by an engineer with the appropriate permissions.

5. Capability Boundaries of Three Tool Categories

Current assistance tools can be divided into three categories by work granularity:

• Inline completion tools: low response latency, suitable for speeding up single-point input, with little awareness of the overall project;
• IDE-native enhancements: deeply integrated with the editor, suitable for interactive coding and local rewrites; cross-project tasks need to be split by the developer;
• Terminal agents: task-oriented, able to modify across files, run commands, and iterate based on results, but with higher review cost per step and a need for clear acceptance criteria.

Tool selection should be based on task shape. In daily coding, they can be mixed: completion handles local speed, agents handle cross-module tasks, and chat handles approach discussions.

6. Adoption Risks and Controls

• Code quality: generated code may look correct but fail to follow team conventions. Code formatting checks, static type checks, and automated tests should be hard gates.
• Data security: do not place keys, credentials, or user data within the agent’s readable scope. Sensitive configuration should be injected through environment variables or a secret management service.
• Dependencies and licenses: third-party libraries or code snippets introduced by an agent require security scanning and license review.
• Over-reliance: require developers to explain every merged change. Code that is not sufficiently understood should not enter the main branch directly.
• Traceability: keep task descriptions and change records for later tracing and retrospective review.

7. Implementation Steps for Team Adoption

1. Select two or three non-core modules as a pilot;
2. Define evaluation metrics such as task completion time, defect rate, and review rework count, and record baseline data before adoption;
3. Write a common task description template for the team and add coding conventions to the project documentation;
4. Hold a weekly review and adjust permission scope and review processes based on results.

Conclusion

The value of agent-style tools lies in turning development tasks into executable, verifiable loops. Understanding context mechanisms, designing clear task boundaries, and relying on version control and automated tests as safeguards are essential for bringing these tools into a reliable engineering process.